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15 Data Science Projects to get you Started

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When it comes to getting a job in data science, data scientists need to think like Creatives. Yes, that¡¯s correct. Those looking to enter this field need to have a data science portfolio of previously completed data science projects, similar to those in Creative professions. What better way to prove to your future data science team that you¡¯re capable of being a data scientist than proving you can do the work?

A common problem for data science entrants is that employers want candidates with experience, but how do you get experience without having access to experience? Suppose you¡¯re looking to get that first foot in the door. It will behoove you to undertake a couple of data science projects to show future employers you¡¯ve got what it takes to use big data to identify opportunities and succeed in the field.

The good news is that we live in a time of open and abundant data. Websites like Kaggle offer a treasure trove of free data for deep learning on everything from crime statistics to Pokemon to Bitcoin and more. However, the wealth of easily accessible data can be overwhelming, which is why we¡¯ve taken it upon ourselves to present 15 data science projects you can execute in Python to showcase and improve your skills in data analytics. Our data science project ideas cover various topics, from Spotify songs to fake news to fraud detection and techniques such as clustering, regression, and natural language processing.

Before you dive in, be sure to adhere to these four guidelines no matter which data science project idea you choose:

1. Articulate the Problem and/or Scenario

It¡¯s not enough to do a project where you use ¡°X¡± to predict ¡°Y¡±; you need to add some context to your work because data science does not occur in a vacuum. Tell us what you¡¯re trying to solve and how data science can address that. Employers want to know if you can turn a problem into a question and a question into a solution. A good place to start is to depict a real-world scenario in which your data project would be useful.

2. Publish & Explain Your Work

Create a GitHub repository where you can upload your Jupyter Notebooks and data. Write a blog post in which you narrate your project from start to finish. Talk about the problem or question at the heart of the project, and explain your decision to clean the data in a certain way or why you decided to use a certain algorithm. Why all this? Potential employers need to understand your methodology.

3. Use Domain Expertise

If you¡¯re trying to break into a specific field such as finance, health, or sports, use your knowledge of this area to enhance your project. This could mean deriving a useful question to a pressing problem or articulating a well-thought-out interpretation of your project¡¯s results. For example, if you¡¯re looking to become a data scientist in the finance sector, it would be worthwhile to show how your methods can generate a return on investment.

4. Be Creative & Different

Anyone can copy and paste code that trains a machine learning algorithm. If you want to stand out, review existing data science projects that use the same data and fill in the gaps left by them. If you¡¯re working on a prediction project, try coming up with an unexpected variable that you think would be beneficial.

Data Science Projects

1. Titanic Data

Working on the Titanic dataset is a rite of passage in data science. It’s a useful dataset that beginners can work with to improve their feature engineering and classification skills. Try using a decision tree to visualize the relationships between the features and the probability of surviving the Titanic.

2. Spotify Data

Spotify has an amazing API that provides access to rich data on their entire catalog of songs. You can grab cool attributes such as a song¡¯s acoustics, danceability, and energy. The great thing about this data source is that the project possibilities are almost endless. You can use these features to try to predict genre or popularity. One fun idea would be to better understand your music by training a machine learning classifier on two sets of songs; songs you like and songs you do not.

3. Personality Data Clustering

You¡¯ve probably heard the phrase, ¡°There are X types of people.¡± Well, now you can actually find out how many types of people there really are. Using this dataset of almost 20k responses to the Big Five Personality Test, you can actually answer this question. Throw this data into a clustering algorithm such as KMeans and sort this into K number of groups. Once you decide on the optimal number of clusters, it¡¯s incumbent on you to define each cluster. Come up with labels that add meaning to each group, and don¡¯t be afraid to use plenty of charts and graphs to support your interpretation.

4. Fake News

If you are interested in natural language processing, building a classifier to differentiate between fake and real news is a great way to demonstrate that. Fake news is a problem that social media platforms have been struggling with for the past several years and a project that tackles this problem is a great way to show you care about solving real-world problems. Use your classifier to identify interesting insights about the patterns in fake versus real news; for example, tell us which words or phrases are most associated with fake news articles.

5. COVID-19 Dataset

There probably isn¡¯t a more relevant use of data science than a project analyzing COVID-19. This dataset provides a wealth of information related to the pandemic. It provides a great opportunity to show off your exploratory data analysis chops. Take a deep dive into this data, and through data visualization unearth patterns about the rate of COVID infection by county, state, and country.

6. Telco Customer Churn

If you¡¯re looking for a straightforward project that is extremely applicable to the business world, then this one’s for you. Use this dataset to train a classifier that predicts customer churn. If you can show employers you know how to prevent customers from leaving their business, you¡¯ll most definitely grab their attention. Pro tip: this is a great projection to show your understanding of classification metrics besides accuracies, such as precision and recall.

7. Lending Club Loans

Like the Telco project, the Lending Club loan dataset is extremely relevant to the business world. Here you can train a classifier that predicts whether or not a Lending Club loanee will pay back a loan using a wealth of information such as credit score, loan amount, and loan purpose. There are a lot of variables at your disposal, so I¡¯d recommend starting with a handful of features and working your way up from there. See how far you can get with just the basics.

Also, this is a fairly untidy dataset that will require extensive cleaning and feature engineering, which is a good thing because that is often the case with real-world data. Be sure to explain your methodology behind preparing your dataset for the machine learning algorithm ¡ª this informs the audience of your domain expertise.

8. Breast Cancer Detection

This dataset provides a simpler classification scenario in which you can use health-related variables to predict instances of breast cancer. If you¡¯re looking to apply your data science skills to the medical field, this is certainly worth a shot.

9. Housing Regression

If classification isn¡¯t your thing, then might I recommend this ready-made regression project in which you can predict home prices using variables like square footage, number of bedrooms, and year built. A project such as this can help you understand the factors driving home sales and let you get creative in your feature engineering. Try to involve outside data that can serve as proxies for quality of life, education, and other things that might influence home prices. And if you want to show off your scraping skills, you can always create your dataset by scraping Zillow.

10. Seeds Clustering

The seeds dataset from UCI provides a simple opportunity to use clustering. Use the seven attributes to sort the 210 seeds into K number of groups. If you¡¯re looking to go beyond KMeans, try using hierarchical clustering, which can be useful for this dataset because the low number of samples can be easily visualized with a dendrogram.

11. Credit Card Fraud Detection

Another project idea for those of you intent on using business world data is to train a classifier to predict instances of credit card fraud. The value of this project to you comes from the fact that it’s an imbalanced dataset, meaning that one class vastly outweighs the other (in this case, non-fraudulent transactions versus fraudulent). Training a model that is 99% accurate is essentially useless, so it’s up to you to use non-accuracy metrics to demonstrate the success of your model.

12. AutoMPG

This is a great beginner regression project in which you can use car features to predict their fuel efficiency. Given that this data is from the past, an interesting idea you can use is to see how well this model does on data from recent cars to show how car fuel efficiency has evolved over the years.

13. World Happiness

Using data science to unlock what¡¯s behind happiness? Maybe you can with this dataset on world happiness rankings. You can go a number of ways with this project; you can use regression to predict happiness scores, cluster countries based on socio-economic characteristics, or visualize the change in happiness throughout the world from 2015 to 2019.

14. Political Identity

The Nationscape Data Set is an absolute goldmine of data on the demographics and political identities of Americans. If you¡¯re a politics junkie, it¡¯ll be sure to satisfy your fix. Their most recent round of data features over 300,000 instances of data collected from extensive surveys of Americans. If you¡¯re interested in using demographic information for political ideology or party identification this is the dataset for you. This is an especially great project to flex your domain expertise in study design, research, and conclusion. Political analysis is replete with shoddy interpretations that lack empirical data analysis, and you could use this dataset to either confirm or dispel them. But be warned that this data will require plenty of cleaning, which you¡¯ll need to get used to, given that’s the majority of the job.

15. Box Office Prediction

If you¡¯re a movie buff, then we¡¯ve got you covered with the TMDB dataset. See if you can build a workable box office revenue prediction model trained on 5000 movies worth of data. Does genre actually correlate with box office success? Can we use runtime and language to help explain the variation in the revenue? Find out the answers to those questions and more with this project.

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How to Get a Job in Data Science Fast

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You want to get a data science job fast. Obviously, no one wants one to get a job slowly. But the time it takes to find a job is relative to you and your situation. When I was seeking my first data science job, I had normal just Kevin bills and things to budget for, plus a growing family who was hoping I’d get a job fast. This was different from some of my classmates, while others had their own versions of why they needed a job fast, too. I believe that when writing a how-to guide on getting a data science job quickly, we should really acknowledge that we¡¯re talking about getting you, the reader, a job faster. Throughout this article, we’ll discuss how to get a job as a data scientist faster than you might otherwise, all things considered.

Getting a job faster is not an easy task in any industry, and getting a job faster as a data scientist has additional encumbrances. Some jobs, extremely well-paying jobs, require a nebulous skill set that most adults could acquire after several years in the professional working world. Data science is not one of those jobs. For all the talk about what a data scientist actually does, there’s a definite understanding that the set of skills necessary to successfully execute any version of the job are markedly technical, a bit esoteric, and specialized. This has pros and cons, which we¡¯ll discuss. The community of people who aspire to join this field, as well as people already in the field, is fairly narrow which also has pros and cons.

Throughout this article, we’ll cover two main ways to speed up the time it takes to get a data science job: becoming aware of the wealth of opportunities, and increasing the likelihood that you could be considered employable.

Becoming Aware of the Wealth of Opportunities

Data science is a growing, in-demand field. See for yourself in Camm, Bowers, and Davenport’s article, “The Recession’s Impact on Analytics and Data Science” and “Why data scientist is the most promising job of 2019” by Alison DeNisco Rayome. It’s no secret however that these reports often only consider formal data science job board posts. You may have heard or already know that there exists a hidden job market. It stands to reason that if this hidden job market exists, there may also be a number of companies who have not identified their need for a data scientist yet, but likely need some portion of data science work. Here¡¯s your action plan, assuming you already have the requisite skills to be a data scientist:

1. Find a company local to your region. This is easier if you know someone at that company, but if you don¡¯t know anyone, just think through the industries that you¡¯d like to build a career in. Search for several companies in those fields and consider a list of problems that might be faced by that organization, or even those industries at large.

2. Do some data work. Try to keep the scope of the project limited to something you could accomplish in one to two weekends. The idea here is not to create a thesis on some topic, but rather to add to your list of projects you can comfortably talk about in a future interview. This also does not have to be groundbreaking, bleeding edge work. Planning, setting up, and executing a hypothesis test for a company who is considering two discount rates for an upcoming sale will give you a ton more fodder for interviews over a half-baked computer vision model with no clear deliverable or impact on a business.

3. You have now done data science work. If you didn¡¯t charge money for your services on the first run, shame on you. Charge more next time.

4. Repeat this process. The nice thing about these mini projects is that you can queue up your next potential projects while you execute the work for your current project at the same time.

Alternatively, you could consider jobs that are what I call the ¡°yeah but there’s this thing¡­¡± type jobs. For example, let’s say you¡¯re setting up a database for a non-profit and really that¡¯s all they need. The thing is… it¡¯s really your friend¡¯s non-profit, all they need is their website to log some info into a database, and they can¡¯t pay you. Of course you should not do things that compromise your morals or leave you feeling as though you’ve lowered your self worth in any way. Of course you¡¯d help out your friend. Of course you would love some experience setting up a database, even if you don¡¯t get to play with big data. Does that mean that you need to explain all of those in your next job interview? Of course not! Take the job and continue to interview for others. Do work as a data engineer. Almost everyone¡¯s jobs have a ¡°yeah but” element to them; it¡¯s about whether the role will help increase your likelihood of being considered employable in the future.

Increasing the Likelihood That You Could Be Considered Employable

Thought experiment: a CTO comes to you with a vague list of Python libraries, deep learning frameworks, and several models which seem relevant to some problems your company is facing and tasks you with finding someone who can help solve those issues. Who would you turn to if you had to pick a partner in this scenario? I¡¯ll give you a hint ¡ª you picked the person who satisfied three, maybe four criteria on what you and that team are capable of.

Recruiting in the real world is no different. Recruiters are mitigating their risk of hiring someone that won¡¯t be able to perform the duties of the position. The way they execute is by figuring out the skills (usually indicated by demonstrated use of a particular library) necessary for the position, then finding the person who seems like they can execute on the highest number of the listed skills. In other words, a recruiter is looking to check a lot of boxes that limit the risk of you as a candidate. As a candidate, the mindset shift you need to come to terms with is that they want and need to hire someone. The recruiter is trying to find the lowest risk person, because the CTO likely has some sort of bearing on that recruiter¡¯s position. You need to basically become the least risky hire, which makes you the best hire, amongst a pool of candidates.

There are several ways to check these boxes if you¡¯re the recruiter. The first is obvious: find out where a group of people who successfully complete the functions of the job were trained, and then hire them. In data science, we see many candidates with training from a bootcamp, a master’s program, or PhDs. Does that mean that you need these degrees to successfully perform the function of the job? I¡¯d argue no ¡ª it just means that people who are capable of attaining those relevant degrees are less risky to hire. Attending 足球竞彩网 Assembly is a fantastic way to show that you have acquired the relevant skills for the job.

Instead of having your resume alone speak to your skill, you can have someone in your network speak to your skills. Building a community of people who recognize your value in the field is incredibly powerful. While joining other pre-built networks is great, and opens doors to new opportunities, I¡¯ve personally found that the communities I co-created are the strongest for me when it comes to finding a job as a data scientist. These have taken two forms: natural communities (making friends), and curated communities. Natural communities are your coworkers, friends, and fellow classmates. They become your community who can eventually speak up and advocate for you when you¡¯re checking off those boxes. Curated communities might be a Meetup group that gathers once a month to talk about machine learning, or an email newsletter of interesting papers on Arxiv, or a Slack group you start with former classmates and data scientists you meet in the industry. In my opinion, the channel matters less, as long as your community is in a similar space as you.

Once you have the community, you can rely on them to pass things your way and you can do the same. Another benefit of 足球竞彩网 Assembly is its focus on turning thinkers into a community of creators. It¡¯s almost guaranteed that someone in your cohort, or at a workshop or event has a similar interest as you. I¡¯ve made contacts that passed alongside gig opportunities, and I¡¯ve met my cofounder inside the walls of 足球竞彩网 Assembly! It¡¯s all there, just waiting for you to act.

Regardless of what your job hunt looks like, it¡¯s important to remember that it’s your job hunt. You might be looking for a side gig to last while you live nomadically, a job that¡¯s a stepping stone, or a new career as a data scientist. You might approach the job hunt with a six-pack of post-graduate degrees; you might be switching from a dead end role or industry, or you might be trying out a machine learning bootcamp after finishing your PhD. Regardless of your unique situation, you¡¯ll get a job in data science fast as long as you acknowledge where you¡¯re currently at, and work ridiculously hard to move forward.

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What is Data Science?

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It¡¯s been anointed ¡°the sexiest job of the 21st century¡±, companies are rushing to invest billions of dollars into it, and it¡¯s going to change the world ¡ª but what do people mean when they mention ¡°data science¡±? There¡¯s been a lot of hype about data science and deservedly so, but the excitement has helped obfuscate the fundamental identity of the field. Anyone looking to involve themselves in data science needs to understand what it actually is and is not.

In this article, we’ll lay out a deep definition of the field, complete descriptions of the data science workflow, and data science tasks used in the real world. We hope that any would-be entrants into this line of work will come away reading this article with a nuanced understanding of data science that can help them decide to enter and navigate this exciting line of work.

So What Actually is Data Science?

A quick definition of data science might be articulated as an interdisciplinary field that primarily uses statistics and computer programming to derive insights from and base decisions from a collection of information represented as numerical figures. The ¡°science¡± part in data science is quite apt because data science very much follows a scientific process that involves formulating a hypothesis and using a specific toolset to confirm or dispel that hypothesis. At the end of the day, data science is about turning a problem into a question and a question into an answer and/or solution.

Tackling the meaning of data science also means interrogating the meaning of data. Data can be easily described as ¡°information encoded as numbers¡± but that doesn¡¯t tell us why it¡¯s important. The value of data stems from the notion that data is a tangible manifestation of the intangible. Data provides solid support to aid our interpretations of the world. For example, a weather app can tell you it¡¯s cold outside but telling you that the temperature is 38 degrees fahrenheit provides you with a stronger and specific understanding of the weather.

Data comes in two forms: qualitative and quantitative.

Qualitative data is categorical data that does not naturally come in the form of numbers, such as demographic labels that you can select on a census form to indicate gender, state, and ethnicity.

Quantitative data is numerical data that can be processed through mathematical functions; for example stock prices, sports stats, and biometric information.

Quantitative can be subdivided into smaller categories such as ordinal, discrete, and continuous.

Ordinal: A sort of qualitative and quantitative hybrid variable in which the values have a hierarchical ranking. Any sort of star rating system of reviews is a perfect example of this; we know that a four-star review is greater than a three-star review, but can¡¯t say for sure that a four- star review is twice as good as a two-star review.

Discrete: These are countable and finite values that often appear in the form of integers. Examples include number of franchises owned by a company and number of votes cast in an election. It¡¯s important to remember discrete variables have a finite range of numbers and can never be negative.

Continuous: Unlike discrete variables, continuous can appear in decimal form and have an infinite range of possibilities. Things like company profit, temperature, and weight can all be described as continuous. 

What Does Data Science Look Like?

Now that we¡¯ve established a base understanding of data science, it¡¯s time to delve into what data science actually looks like. To answer this question, we need to go over the data science workflow, which encapsulates what a data science project looks like from start to finish. We¡¯ll touch on typical questions at the heart of data science projects and then examine an example data science workflow to see how data science was used to achieve success.

The Data Science Checklist

A good data science project is one that satisfies the following criteria:

Specificity: Derive a hypothesis and/or question that’s specific and to the point. Having a vague approach can often lead to a waste of time with no end product.

Attainability: Can your questions be answered? Do you have access to the required data? It¡¯s easy to come up with an interesting question but if it can¡¯t be answered then it has no value. The same goes for data, which is only useful if you can get your hands on it.

Measurability: Can what you’re applying data science to be quantified? Can the problem you¡¯re addressing be represented in numerical form? Are there quantifiable benchmarks for success? 

As previously mentioned, a core aspect of data science is the process of deriving a question, especially one that is specific and achievable. Typical data science questions ask things like, does X predict Y and what are the distinct groups in our data? To get a sense of data science questions, let¡¯s take a look at some business-world-appropriate ones:

  • What is the likelihood that a customer will buy this product?
  • Did we observe an increase in sales after implementing a new policy?
  • Is this a good or bad review?
  • How much demand will there be for my service tomorrow?
  • Is this the cheapest way to deliver our goods?
  • Is there a better way to segment our marketing strategies?
  • What groups of products are customers purchasing together?
  • Can we automate this simple yes/no decision?

All eight of these questions are excellent examples of how businesses use data science to advance themselves. Each question addresses a problem or issue in a way that can be answered using data science.

The Data Science Workflow

Once we¡¯ve established our hypothesis and questions, we can now move onto what I like to call the data science workflow, a step-by-step description of a typical data science project process.

After asking a question, the next steps are:

  1. Get and Understand the Data. We obviously need to acquire data for our project, but sometimes that can be more difficult than expected if you need to scrape for it or if privacy issues are involved. Make sure you understand how the data was sampled and the population it represents. This will be crucial in the interpretation of your results.
  1. Data Cleaning and Exploration. The dirty secret of data science is that data is often quite dirty so you can expect to do significant cleaning which often involves constructing your variables in a way that makes your project doable. Get to know your data through exploratory data analysis. Establish a base understanding of the patterns in your dataset through charts and graphs.
  1. Modeling. This represents the main course of the data science process; it¡¯s where you get to use the fancy powerful tools. In this part, you build a model that can help you answer a question such as can we predict future sales of a product from your dataset.
  1. Presentation. Now it¡¯s time to present the results of your findings. Did you confirm or dispel your hypothesis? What are the answers to the questions you started off with? How do your results advance our understanding of the issue at hand? Articulate your project in a clear and concise manner that makes it digestible for your audience, which could be another team in your company or your company¡¯s executives.

Data Science Workflow Example: Predicting Neonatal Infection

Now let¡¯s parse out an example of how data science can affect meaningful real-world impact, taken from the book Big Data: A Revolution That Will 足球竞彩网 How We Live, Work, and Think.

We start with a problem: Children born prematurely are at high risk of developing infections, many of which are not detected until after a child is sick.

Then we turn that problem into a question: Can we detect patterns in the data that accurately predict infection before it occurs?

Next, we gather relevant data: variables such as heart rate, respiration rate, blood pressure, and more.

Then we decide on the appropriate tool: a machine learning model that uses past data to predict future outcomes.

Finally, what impact do our methods have? The model is able to predict the onset of infection before symptoms appear, thus allowing doctors to administer treatment earlier in the infection process and increasing the chances of survival for patients.

This is a fantastic example of data science in action because every step in the process has a clear and easily understandable function towards a beneficial outcome.

Data Science Tasks

Data scientists are basically Swiss Army knives, in that they possess a wide range of abilities ¡ª it¡¯s why they’re so valuable. Let’s go over the specific tasks that data scientists typically perform on the job.

Data acquisition: For data scientists, this usually involves querying databases set up by their companies to provide easy access to reams of data. Data scientists frequently write SQL queries to retrieve data. Outside of querying databases, data scientists can use APIs or web scraping to acquire data.

Data cleaning: We touched on this before, but it can’t be emphasized enough that data cleaning will take up the vast majority of your time. Cleaning oftens means dealing with null values, dropping irrelevant variables, and feature engineering which means transforming data in a way so that it can be processed by a model.

Data visualization: Crafting and presenting visually appealing and understandable charts is a hugely valuable skill. Visualization has an uncanny ability to communicate important bits of information from a mass of data. Good data scientists will use data visualization to help themselves and their audiences better understand what¡¯s going on.

Statistical analysis: Statistical tests are used to confirm and/or dispel a data scientist¡¯s hypothesis. A t-test or chi-square are used to evaluate the existence of certain relationships. A/B testing is a popular use case of statistical analysis; if a team wants to know which of two website designs leads to more clicks, then an A/B test is the right solution.

Machine learning: This is where data scientists use models that make predictions based on past observations. If a bank wants to know which customers are likely to pay back loans, then they can use a machine learning model trained on past loans to answer that question.

Computer science: Data scientists need adequate computer programming skills because many of the tasks they undertake involve writing code. In addition, some data science roles require data scientists to function as software engineers because data scientists have to implement their methodologies into their company¡¯s backend servers.

Communication: You can be a math and computer whiz, but if you can¡¯t explain your work to a novice audience, your talents might as well be useless. A great data scientist can distill digestible insights from complex analyses for a non-technical audience, translating how a p-value or correlation score is relevant to a part of the company¡¯s business. If your company is going to make a potentially costly or lucrative decision based on your data science work, then it’s incumbent on you to make sure they understand your process and results as much as possible.

Conclusion

We hope this article helped to demystify this exciting and increasingly important line of work. It¡¯s pertinent to anyone who’s curious about data science ¡ª whether it’s a college student or an executive thinking about hiring a data science team ¡ª that they understand what this field is about and what it can and cannot do.

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Designing a Dashboard in Tableau for Business Intelligence

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Tableau is a data visualization platform that focuses on business intelligence. It has become very popular in recent years because of its flexibility and beauty. Clients love the way Tableau presents data and how easy it makes performing analyses. It is one of my favorite analytical tools to work with.

A simple way to define a Tableau dashboard is as a glance view of a company’s key performance indicators, or KPIs. There are different kinds of dashboards available ¡ª it all depends on the business questions being asked and the end-user. Is this for an operational team (like one at a distribution center) that needs to see the number of orders by hour and if sales goals are achieved? Or, is this for a CEO who would like to measure the productivity of different departments and products against forecast? The first case will require the data to be updated every 10 minutes, almost in real-time. The second doesn’t require the same cadence, and once a day will be enough to track the company performance.

Over the past few years, I¡¯ve built many dashboards for different types of users, including department heads, business analysts, and directors, and helped many mid-level managers with data analysis. If you are looking for Tableau dashboard examples, you have come to the right place. Here are some best practices for creating Tableau dashboards I¡¯ve learned throughout my career.

Why Use a Data Visualization?

A data visualizations tool is one of the most effective ways to analyze data from any business process (sales, returns, purchase orders, warehouse operation, customer shopping behavior, etc.).

Below we have a grid report and bar chart that contain the same data source information. Which is easier to interpret?

Grid report

Bar Chart
Grid report vs. bar chart.

That¡¯s right ¡ª it¡¯s quicker to identify the category with the lowest sales, Tops, using the chart.

Many companies previously used grid reports to operate and make decisions, and many departments still do today, especially in retail. I once went to a trading meeting on a Monday morning where team members printed pages of Excel reports with rows and rows of sales and stock data by product and took them to a meeting room with a ruler and a highlighter to analyze sales trends. Some of these reports took at least two hours to prepare and required combining data from different data sources with VLOOKUPs ¡ª a function that allows users to search through columns in Excel. After the meeting, they threw the papers away (a waste of paper and ink), and then the following Monday it all started again.

Wouldn’t it be better to have an effective dashboard and reporting tool in which the company’s KPIs were updated daily and presented in an interactive dashboard that could be viewed on tablets/laptops and digitally sliced and diced? That’s where tools like Tableau server dashboards come in. You can drill down into details and answer questions raised in the meeting in real-time when creating a Tableau project – something you couldn’t do with paper copies.

How to Design a Dashboard in Tableau SERVER

Step 1: Identify who will use the dashboard and with what frequency.

Tableau dashboards can be used for many different purposes, such as measuring different KPIs, and therefore will be designed differently for each circumstance. This means that, before you can begin designing a new dashboard, you need to know who is going to use it and how often.

Step 2: Define your topic.

The stakeholder (i.e., director, sales manager, CEO, business analyst, buyer) should be able to tell you what kind of business questions need to be answered and the decisions that will be made based on the dashboard.

Here, I am going to use the dataset for my Tableau dashboard example from a fictional retail company to report on monthly sales.

The commercial director would like to know 1) the countries to which the company¡¯s products have been shipped, 2) which categories are performing well, and 3) sales by product. The option of browsing products is a plus, so the tableau dashboard should include as much detail as possible.

Step 3: Initially, make sure you have all of the necessary data available to answer the questions specified in your new dashboard.

Clarify how often you will get the data, the format in which you will receive the data (inside a database or in loose files), the cleanliness of the data, and if there are any data quality issues. You need to evaluate all of this before you promise a delivery date.

Step 4: Create your dashboard.

When it comes to dashboard design, it¡¯s best-practice to present data from top to bottom when in presentation mode. The story should go from left to right, like a comic book, where you start at the top left and finish at the bottom right.

Let¡¯s start by adding the data set to Tableau. For this demo, the data is contained in an Excel file generated by software I developed myself. It¡¯s all dummy data.

To connect to an Excel file from Tableau, select ¡°Excel¡± from the Connect menu. The tables are on separate Excel sheets, so we¡¯re going to use Tableau to join them, as shown in the image below. Once the tables are joined, go to the bottom and select Sheet 1 to create your first visualization.

Excel Sheet in Tableau
Joining Excel sheet in Tableau.

We have two columns in the Order Details table: Quantity and Unit Price. The sales amount is Quantity x Unit Price, so we¡¯re going to create the new metric, ¡°Sales Amount.¡± Right-click on the measures and select Create > Calculated Field.

Creating a Map in Tableau

We can use maps to visualize data with a geographical component and compare values across geographical regions. To answer our first question ¡ª ¡°Which countries the company¡¯s products have been shipped to?¡± ¡ª we¡¯ll create a map view of sales by country.

1. Add Ship Country to the rows and Sales Amount to the columns.

2. Change the view to a map.

Map
Visualizing data across geographical regions.

3. Add Sales Amount to the color pane. Darker colors mean higher sales amounts aggregated by country.

4. You can choose to make the size of the bubbles proportional to the Sales Amount. To do this, drag the Sales Amount measure to the Size area.

5. Finally, rename the sheet ¡°Sales by Country.¡±

Creating a Bar Chart in Tableau

Now, let¡¯s visualize the second request, ¡°Which categories are performing well?¡± We¡¯ll need to create a second sheet. The best way to analyze this data is with bar charts, as they are to compare data across categories. Pie charts work in a similar way, but in this case we have too many categories (more than four) so they wouldn¡¯t be effective.

1. To create a bar chart, add Category Name to the rows and Sales Amount to the columns.

2. Change the visualization to a bar chart.

3. Switch columns and rows, sort it by descending order, and show the values so users can see the exact value that the size of the rectangle represents.

4. Drag the category name to ¡°Color.¡±

5. Now, rename the sheet to ¡°Sales by Category.¡±

Sales category bar chart
Our Sales by Category breakdown.

Assembling a Dashboard in Tableau

Finally, the commercial director would like to see the details of the products sold by each category.

Our last page will be the product detail page. Add Product Name and Image to the rows and Sales Amount to the columns. Rename the sheet as ¡°Products.¡±

We are now ready to create our first dashboard! Rearrange the chart on the dashboard so that it appears similar to the example below. To display the images, drag the Web Page object next to the Products grid.

Dashboard Assembly
Assembling our dashboard.

Additional Actions in Tableau

Now, we¡¯re going to add some actions on the dashboard such that when we click on a country, we¡¯ll see both the categories of products and a list of individual products sold.

1. Go to Dashboard > Actions.

2. Add Action > Filter.

3. Our ¡°Sales by Country¡± chart is going to filter Sales by Category and Products.

4. Add a second action: Sales by Category will filter Products.

5. Add a third action, this time selecting URL.

6. Select Products, <Image> on URL, and click on the Test Link to test the image¡¯s URL.

What we have now is an interactive dashboard with a worldwide sales view. To analyze a specific country, we click on the corresponding bubble on the map and Sales by Category will be filtered to what was sold in that country.

When we select a category, we can see the list of products sold for that category. And, when we hover on a product, we can see an image of it.

In just a few steps, we have created a simple dashboard from which any department head would benefit.

Dashboard
The final product.

Dashboards in Tableau at 足球竞彩网 Assembly

In GA¡¯s Data Analytics course, students get hands-on training with the versatile Tableau platform. Students will learn the ins and outs of the data visualization tool and create dashboards to solve real-world problems in 1-week, accelerated or 10-week, part-time course formats ¡ª on campus and online. You can also get a taste in our interactive tableau training with these classes and workshops.

Ask a Question About Our Data Programs

Meet Our Expert

Samanta Dal Pont is a business intelligence and data analytics expert in retail, eCommerce, and online media. With an educational background in software engineer and statistics, her great passion is transforming businesses to make the most of their data. Responsible for the analytics, reporting, and visualization in a global organization, Samanta has been an instructor for Data Analytics courses and SQL bootcamps at 足球竞彩网 Assembly London since 2016.

Samanta Dal Pont, Data Analytics Instructor, 足球竞彩网 Assembly London

How is Python Used in Data Science?

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Python is a popular programming language used by both developers and data scientists. But what makes it so popular and why are so many data scientists choosing Python over other programming languages? In this article, we’ll explore the advantages of Python programming and why it’s useful for data science.

What is Python?

No, we’re not talking about the giant, tropical snake. Python is a general-purpose, high-level programming language. It supports object oriented, structured, and functional programming paradigms.

Python was created in the late 1980s by the Dutch programmer Guido van Rossum who wanted a project to fill his time over the holiday break. His goal was to create a programming language that was a descendant of the ABC programming language but would appeal to Unix/C hackers. Van Rossum writes that he chose the name Python for this language, “being in a slightly irreverent mood (and a big fan of Monty Python’s Flying Circus).”

Python went through many updates and iterations and by the year 2008, Python 3.0 was released. This was designed to fix many of the design flaws in the language, with an emphasis on removing redundant features. While this update had some growing pains as it was not backwards compatible, the new updates made way for Python as we know it today. It continues to be well-maintained and supported as a popular, open source programming language.

In ¡°The Zen of Python,¡± developer Tim Peters summarizes van Rossum¡¯s guiding principles for writing code in Python:

Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren’t special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one– and preferably only one –obvious way to do it.
Although that way may not be obvious at first unless you’re Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it’s a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea — let’s do more of those!

These principles touch on some of the advantages of Python in data science. Python is designed to be readable, simple, explicit, and explainable. Even the first principle states that Python code should be beautiful. In general, Python is a great programming language for many tasks and is becoming increasingly popular for developers. But now you may be wondering, why learn Python for data science?

Why Python for Data Science?

The first of many benefits of Python in data science is its simplicity. While some data scientists come from a computer science background or know other programming languages, many come from backgrounds in statistics, mathematics, or other technical fields and may not have as much coding experience when they enter the field of data science. Python syntax is easy to follow and write, which makes it a simple programming language to get started with and learn quickly. 

In addition, there are plenty of free resources available online to learn Python and get help if you get stuck. Python is an open source language, meaning the language is open to the public and freely available. This is beneficial for data scientists looking to learn a new language because there is no up-front cost to start learning Python. This also means that there are a lot of data scientists already using Python, so there is a strong community of both developers and data scientists who use and love Python.

The Python community is large, thriving, and welcoming. Python is the fourth most popular language among all developers based on a 2020 Stack Overflow survey of nearly 65,000 developers. Python is especially popular among data scientists. According to SlashData, there are 8.2 million active Python users with ¡°a whopping 69% of machine learning developers and data scientists now us[ing] Python (compared to 24% of them using R).¡±4 A large community brings a wealth of available resources to Python users. Not only are there numerous books and tutorials available, there are also conferences such as PyCon where Python users across the world can come together to share knowledge and connect. Python has created a supportive and welcoming community of data scientists willing to share new ideas and help one another. 

If the sheer number of people using Python doesn¡¯t convince you of the importance of Python for data science, maybe the libraries available to make your data science coding easier will. A library in Python is a collection of modules with pre-built code to help with common tasks. They essentially allow us to benefit from and build on top of the work of others. In other languages, some data science tasks would be cumbersome and time consuming to code from scratch. There are countless libraries like NumPy, Pandas, and Matplotlib available in Python to make data cleaning, data analysis, data visualization, and machine learning tasks easier. Some of the most popular libraries include:

  • NumPy: NumPy is a Python library that provides support for many mathematical tasks on large, multidimensional arrays and matrices.
  • Pandas: The Pandas library is one of the most popular and easy-to-use libraries available. It allows for easy manipulation of tabular data for data cleaning and data analysis.
  • Matplotlib: This library provides simple ways to create static or interactive boxplots, scatterplots, line graphs, and bar charts. It’s useful for simplifying your data visualization tasks.
  • Seaborn: Seaborn is another data visualization library built on top of Matplotlib that allows for visually appealing statistical graphs. It allows you to easily visualize beautiful confidence intervals, distributions, and other graphs.
  • Statsmodels: This statistical modeling library builds all of your statistical models and statistical tests including linear regression, generalized linear models, and time series analysis models.
  • Scipy: Scipy is a library used for scientific computing that helps with linear algebra, optimization, and statistical tasks.
  • Requests: This is a useful library for scraping data from websites. It provides a user-friendly and responsive way to configure HTTP requests.

In addition to all of the general data manipulation libraries available in Python, a major advantage of Python in data science is the availability of powerful machine learning libraries. These machine learning libraries make data scientists¡¯ lives easier by providing robust, open source libraries for any machine learning algorithm desired. These libraries offer simplicity without sacrificing performance. You can easily build a powerful and accurate neural network using these frameworks. Some of the most popular machine learning and deep learning libraries in Python include:

  • Scikit-learn: This popular machine learning library is a one-stop-shop for all of your machine learning needs with support for both supervised and unsupervised tasks. Some of the machine learning algorithms available are logistic regression, k-nearest neighbors, support vector machine, random forest, gradient boosting, k-means, DBSCAN, and principal component analysis.
  • Tensorflow: Tensorflow is a high-level library for building neural networks. Since it was mostly written in C++, this library provides us with the simplicity of Python without sacrificing power and performance. However, working with raw Tensorflow is not suited for beginners.
  • Keras: Keras is a popular high-level API that acts as an interface for the Tensorflow library. It’s a tool for building neural networks using a Tensorflow backend that’s extremely user friendly and easy to get started with.
  • Pytorch: Pytorch is another framework for deep learning created by Facebook¡¯s AI research group. It provides more flexibility and speed than Keras, but since it has a low-level API, it is more complex and may be a little bit less beginner friendly than Keras. 

What Other Programming Languages are Used for Data Science?

Python is the most popular programming language for data science. If you’re looking for a new job as a data scientist, you’ll find that Python is also required in most job postings for data science roles. Jeff Hale, a 足球竞彩网 Assembly data science instructor, scraped job postings from popular job posting sites to see what was required for jobs with the title of ¡°Data Scientist.¡± Hale found that Python appears in nearly 75% of all job postings. Python libraries including Tensorflow, Scikit-learn, Pandas, Keras, Pytorch, and Numpy also appear in many data science job postings.

Image source: The Most In-Demand Tech Skills for Data Scientists by Jeff Hale

R, another popular programming language for data science, appeared in roughly 55% of the job postings. While R is a useful tool for data science and has many benefits including data cleaning, data visualization, and statistical analysis, Python continues to become more popular and preferred among data scientists for a majority of tasks. In fact, the average percentage of job postings requiring R dropped by about 7% between 2018 and 2019, while Python increased in the percentage of job postings requiring the language. This isn’t to say that learning R is a waste of time; data scientists that know both of these languages can benefit from the strengths of both languages for different purposes. However, since Python is becoming increasingly popular, there’s a high chance that your team uses Python, and it’s important to use the language that your team is comfortable with and prefers.

What is the Future of Python for Data Science?

As Python continues to grow in popularity and as the number of data scientists continues to increase, the use of Python for data science will inevitably continue to grow. As we advance machine learning, deep learning, and other data science tasks, we’ll likely see these advancements available for our use as libraries in Python. Python has been well-maintained and continuously growing in popularity for years, and many of the top companies use Python today. With its continued popularity and growing support, Python will be used in the industry for years to come.

Whether you’ve been a data scientist for years or you are just beginning your data science journey, you can benefit from learning Python for data science. The simplicity, readability, support, community, and popularity of the language ¡ª as well as the libraries available for data cleaning, visualization, and machine learning ¡ª all set Python apart from other programming languages. If you aren¡¯t already using Python for your work, give it a try and see how it can simplify your data science workflow.

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Data at Work: 3 Real-World Problems Solved by Data Science

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At first glance, data science seems to be just another business buzzword ¡ª something abstract and ill-defined. While data can, in fact, be both of these things, it¡¯s anything but a buzzword. Data science and its applications have been steadily changing the way we do business and live our day-to-day lives ¡ª and considering that 90% of all of the world¡¯s data has been created in the past few years, there¡¯s a lot of growth ahead of this exciting field.

While traditional statistics and data analysis have always focused on using data to explain and predict, data science takes this further. It uses data to learn ¡ª constructing algorithms and programs that collect from various sources and apply hybrids of mathematical and computer science methods to derive deeper actionable insights. Whereas traditional analysis uses structured data sets, data science dares to ask further questions, looking at unstructured ¡°big data¡± derived from millions of sources and nontraditional mediums such as text, video, and images. This allows companies to make better decisions based on its customer data.

So how is this all manifesting in the market? Here, we look at three real-world examples of how data science drives business innovation across various industries and solves complex problems.

Continue reading →

How to Quickly get an Internship in Data Science

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After studying statistics, probability, programming, algorithms, and data structures for long hours, putting all the knowledge in action is essential. An internship at a great company is a great way to practice your skills, but at the same time is one of the most difficult jobs. Especially with such vast competition.  

Nowadays, many other opportunities are branded as ¡°internship experiences¡± but they’re not actually internships. A key distinction is as follows: if you’re asked to pay for an internship, then it’s not an internship. An internship is a free opportunity to work in a specific industry for a short period of time, usually shadowing an existing employee or team.

This article will provide you with five tips to help you secure your first data science internship. However, first we’ll discuss what exactly data science is and what the job entails.

What is data science?

Data science focuses on obtaining actionable insights from data, both raw and unstructured, often in large quantities. This big data is analyzed by data analysts as it¡¯s so complex it cannot be understood by existing software or machines.

Ultimately, data science is concerned with providing solutions to problems we don¡¯t yet know are problems or concerns. It¡¯s essentially about looking into the future and finding fixes for things that may happen or might be implemented. On the other hand, a data analyst¡¯s role is to investigate current data and how this impacts the now.

What is the role of a data scientist?

As a data science intern, you will be responsible for collecting, cleaning, and analyzing various datasets to gather valuable insights. Later, with the help of other data scientists, these insights will be shared with the company in an effort to contribute to business strategies or product development. Within the role of a data scientist, you will be expected to be independent in your work collecting and cleaning data, finding patterns, building algorithms, and even conducting your own experiments and sharing these with your team.

5 Tips to Finding Your First Data Science Internship

Now that you know what data science is and what a data science analyst does, you may be wondering how to get a data science internship. Here are five actionable tips to land your first data science internship, beginning with a more obvious one: acquiring the right skills.

1.   Acquire the right skills

As a data scientist, you’re expected to possess a variety of complex skills. Therefore, you should begin learning these now to set yourself aside from your competition and increase the likelihood of landing a data science internship.

In fact, regardless of your internship role, you should be actively learning new skills all of the time, preferably skills that are related to your industry (e.g. data science). There’s no set formula to acquire skills; there are numerous ways to get started, such as online data science courses (some of which are free), additional University modules, or conducting some data science work yourself, perhaps in your free time.

The more relevant data science skills you have, the more appealing you’ll be to employees looking for a data science intern. So, start learning now and distinguish yourself from your competition; you won¡¯t regret it.

2.   Customize each data science application

A common problem many graduate students make when applying for internships online is bulk-applying and using the same CV and cover letter for each application. This is a lengthy and tedious process, and rarely pays dividends.

Instead, students should customize each data science application to each company or organization that they¡¯re applying for. Not all data science jobs are the same ¡ª their requirements are somewhat different, both in the industry and the company’s goals and beliefs. To increase your likelihood of landing a data science internship, you need to be genuinely interested in the company you are applying for, and show this in your application. Be sure to read through their website, look at their previous work, initiatives, goals, and beliefs. And finally, make sure that the companies you are applying for are places you actually want to work at, or else the sincerity of your application may be cast in a negative light, even if you don¡¯t realize this.

3.   Create a portfolio

To stand out in such a saturated market, it¡¯s essential to create your very own portfolio. Ideally, your portfolio should consist of one or several of your own projects where you collect your own data. It¡¯s good to indicate you have the experience on paper, but showing this to potential employers first-hand shows that you¡¯re willing to go above and beyond, and that you truly do understand datasets and other data scientist tasks.

Your portfolio project(s) should be demonstrable, covering all typical steps of machine learning and general data science tasks such as collecting and cleaning data, looking for outliers, building models, evaluating models, and drawing conclusions based on your data and findings.  Furthermore, go ahead and create a short brief to explain your project(s), to include as a preface to your portfolio.

4.   Practicing for interviews is crucial

While your application may land you an interview, your interview is the penultimate deciding factor as to whether or not you get the data science internship. Therefore, it¡¯s essential to prepare the best you can. 

There are several things you can do to prepare:

¡ñ  Research what to expect in the interview.

¡ñ  Know your project and portfolio like the back of your hand.

¡ñ  Research common interview questions and company information.

¡ñ  Practice interview questions and scenarios with a friend or family member. 

Let’s break down each of these points further.

Research what to expect in the interview.

Every interview is different, but you can research roughly what to expect. For example, you could educate yourself on the company’s latest policies and events, ongoing initiatives, or their plans for the coming months. Taking the time to research the company will come through in your interview and show the interviewer that you’re dedicated and willing to do the work.

Know your project and portfolio like the back of your hand.

To show your competence and expertise, it¡¯s essential to have a deep and thorough understanding of your project and portfolio. You’ll need to be able to answer any questions your interviewer asks, and provide detailed and knowledgeable answers.

Prior to the interview, familiarize yourself with your project, revisiting past data, experiments, and conclusions. The more you know, the better equipped you’ll be.

Research common interview questions and company information.

Most data science internship interviews follow a similar series of questions. Before your interview, research these, create a list of the most popular and difficult questions, and prepare your answers for each question. Even if these exact questions may not come up, similar ones are likely to. Preparing thoughtful answers in advance provides you with the best opportunity to express professional and knowledgeable answers that are sure to impress your interviewers.

This leads us to our next point: practicing these questions.

Practice interview questions and scenarios with a friend or family member.

Once you¡¯ve researched a variety of different questions, try answering these with a friend or family member, ideally in a similar environment as the interview. Practicing your answers to these questions will help you be more confident and less nervous. 

Be sure to go over the more difficult questions, just in case they come up in your actual data science internship interview.

Ask whomever is interviewing you (the friend or family member, for example) to ask some of their own questions, too, catching you off guard and forcing you to think on your feet. This too helps you get ready for the interview, since this is likely to happen regardless of how well you prepare.

5.   Don¡¯t be afraid to ask for feedback

You¡¯re not going to get every data science internship you apply for. Even if you did, you wouldn¡¯t be able to take them all. Therefore, we recommend asking for feedback on your interview and application in general.

If you didn¡¯t land the internship the first time, you can use this feedback and perhaps re-apply at a future date. Most organizations and companies will be happy to offer feedback unless they have policies in place preventing them. With clear feedback, you’ll be able to work on potential weaknesses in your application and interview and identify areas of improvement for next time.

Over time, after embracing and implementing this feedback, you¡¯ll become more confident and better suited to the interview environment ¡ª a skill that will undoubtedly help you out later in life.

Frequently Asked Questions

What do data analyst interns do?

Data analyst interns are responsible for collecting and analyzing data and creating visualizations of this data, such as written reports, graphs, and presentations.

How do I get a data science job with no experience?

Getting a data science job with no experience will be very difficult. Therefore, we recommend obtaining a degree in a relevant subject (e.g. computer science) if possible and creating your own portfolio to showcase your expertise to potential employers.

What does a data science intern do?

Data science interns perform very similar roles and tasks to full-time data scientists. However, the main difference here is that interns often shadow or work with another data scientist, not alone. As an intern, you can expect to collect and clean data, create experiments, find patterns in data, build algorithms, and more.

To Conclude

Data science internships are few and far between, and landing one can be difficult. But it¡¯s not impossible and the demand for these roles is slowly increasing as the field becomes more popular.

The role of a data scientist intern includes analyzing data, creating experiments, building algorithms, and utilizing machine learning, amongst a variety of other tasks. To successfully get a data science internship, you should begin acquiring the right skills now, customize each application, create your very own portfolio and project, practice for interviews, and don¡¯t be afraid to ask for feedback on unsuccessful applications.

Best of luck to all those applying, and remember: preparation is key.

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3 Tips for Preparing for a Data Science Interview

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Hello intrepid data scientist! First off, I’d like to congratulate you; you’re likely reading this post because you’re preparing to interview for a data science job. This means I’ll assume that: (a) you’re the type of person that researches ways to improve and level up in your career, and (b) you’re reached the interview stage ¡ª congrats!

As a data science instructor, I’m often asked for advice on how to prepare for a data science interview. In response, I usually bring up three major themes. You need to:

1. Have a background that includes sufficient knowledge of the field of data science to fulfill the job¡¯s tasks.

2. Have implemented that knowledge in some way that the community recognizes.

3. Be able to convince your interviewer of your knowledge and abilities.

1. Knowledge of Data Science

I’ve taken part in interviewing many data scientists and have also been interviewed. Through being on both sides of the table, I’ve seen that there are usually three-ish areas of knowledge that an interviewer is looking for: prerequisite knowledge of data science at large, which includes: mathematics[1], coding[2], databases[3], and the ability to communicate findings and insights[4]; knowledge of the company and its vertical; and knowledge of the tech stack of that company.

If you’re reading this article with a fairly long time horizon and not trying to cram, then you can prepare ahead of time with the knowledge of data science at large by taking a look at this blog post which has a long list of curated resources. If you are reading this and trying to prepare for a data science interview on a short time horizon, this article and this article have a list of questions with answers to get you in the zone.

Knowledge of the company is going to come from research of that company. Read up on the company and if you have time, find second and third degree connections through LinkedIn or people you know and reach out. As a 足球竞彩网 Assembly alum, I¡¯ve found it incredibly helpful to go to a company¡¯s LinkedIn page, check out who the fellow alumni are, and connect through a LinkedIn message or offering to buy them coffee. Reading up on the company usually takes the form of doing research about the company itself (founding principles, place in the market, investment stage, etc.), but it also takes the form of looking up who you¡¯d be working alongside if you started working there. What does the data team look like? Are there data engineers or other data scientists?[5]

During a data science interview, your background will likely speak to your knowledge of the vertical you¡¯re applying to. In the absence of that, some portfolio projects are a great second option to show your domain expertise.

Thomas Hughes, Manager of Data Science and Machine Learning at Etsy, shared this bit of advice on striking a balance between generalized skills, specific skills, and knowledge in a vertical:

¡°Companies who do not have much experience in data work generally look for candidates who specialize in their industry vertical. Since they don’t know what they’re looking for, they often will say, ¡®I’m looking for someone who has solved problems similar to my problems, which I’m assuming means they have to be coming from my industry.¡¯

More mature companies, with experience in the data space, recognize that many of the techniques are applicable across industries and don’t require industry specific knowledge, and furthermore, someone who’s deeply trained in a specific technique often adds more value than someone who’s just familiar with an industry vertical.¡±

Theodore Villacorta, Executive Director of Analytics at Warner Brothers, shared with me that, ¡°regarding vertical, your background matters less; it¡¯s more about skills to get data from a database and how you can perform with it.¡±

Lastly, you need to be fairly well versed in the tech stack that the company primarily uses. Villacorta offers: ¡°Since knowledge of one of the two main open source languages is a strong requisite, along with the ability to use the corresponding SQL packages for those languages, it might be a great idea to showcase those in a portfolio piece. Most organizations have some form of SQL database.¡± At minimum, be prepared to answer questions about any tech stack that the company uses within the realm of data science and especially be prepared to answer questions about any tech that your resume lists. I usually like to do two things in preparation, to get an idea of what¡¯s being used: first, I¡¯ll head to stackshare.io and see if the company is listed. Second, I¡¯ll look at the skills that current employees list on LinkedIn.

2. 足球竞彩网 Recognition

The second piece is the community piece, especially if you have plenty of time before the data science interview. 足球竞彩网 is purposefully a fairly amorphous term here. You can attend in-person events like meetups or conferences, or you can also have a community of coworkers, or a community of social media followers. I suggest laying the groundwork naturally. Networking can feel uncomfortable, but finding people you genuinely like being around in this field is usually pretty easy (didn¡¯t anyone tell you that data scientists are the coolest people in any room?). If you don¡¯t find a community that you¡¯re into, try building one: set up a talk featuring other data scientists. Think like a starfish here, not a spider. You¡¯re trying to create interactions and connections that continue to build new interactions in your absence; not interactions and connections that fall into a void once you¡¯re no longer making them happen.

3. Convince Your Interviewer

In your data science interview, you need to convince the interviewer of your capabilities of both areas above. Interviewers are looking to make sure that you¡¯re someone that generally fits into the puzzle board of other employees that make up the company culture. Show them that you¡¯re great at the community thing through past coworkers or your involvement in open source projects online, engagements with people on Twitter, your writing style on blog posts, and the like. As Villacorta mentions, ¡°For everyone, regardless of how cross functional of a role, I think it¡¯s important to find someone who has an ability to collaborate, share resources¡­I¡¯ll usually ask behavioral questions like ¡®tell me a time when¡­¡¯ in order to get a sense of a candidate¡¯s abilities in this area.¡±

Hughes explains, ¡°Senior level positions generally need to be providing leadership and influence over non-technical stakeholders. So they need experience explaining how the work they and their team is doing is valuable in non-technical ways.¡± Demonstrating your knowledge in an interview comes down to staying open. You¡¯ve done the studying, now just get out of your own way.

I like employing the beginner¡¯s mind here. Take every question in as though you¡¯re uncovering the answer alongside the interviewer. In other words, think of it kind of like an archeological dig, rather than a tennis match. When you get an interview question like, ¡°what¡¯s a P value?¡± you can respond with, ¡°are you curious about calculating and interpreting P values in the context of hypothesis testing in a project? Because I had a great project I worked on [insert teaser to a project here]¡­ or are you looking for a definition?¡± This gives your interviewer a ton more fodder to work with and opens you up to answer questions in the Situation, Task, Action, Results (STAR) format, especially as it relates to former projects and jobs.

Regardless of where you are in the interviewing process, know that there is a position and great fit for a company for you somewhere. I think it’s helpful to consider the process of interviewing through the lens of a company ¡ª they’ve been looking for you! Don¡¯t let your own ego get in the way of letting a genuine interaction take place during the data science interview. Interviews aren¡¯t something you¡¯re ¡°stuck with¡± having to put up with on your march towards another job. In fact, they can be incredibly rewarding moments to find new areas to learn about in this fascinating field we¡¯re in. Good luck, and let me know how it went!

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[1] Stats questions are incredibly popular fodder for data science interviews. Linear Algebra is less often questioned in interviews, but more helpful on the job.

[2] You should be fluent in at least one of the two major open source languages: Python or R.

[3] Data lives in databases, unless it lives in dozens of Excel files on a Shared Drive. You don¡¯t want to work at places without a database though.

[4] This is actually really difficult to gauge in an interview because everyone gives candidates leeway for being nervous. Often you can pass this test by being affable and confident in your answer.  

[5] Note that if the answer to either of these questions is ¡°no¡±, then you¡¯re going to be playing both roles.

How to Run a Python Script

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As a blooming Python developer who has just written some Python code, you’re immediately faced with the important question, ¡°how do I run it?¡± Before answering that question, let’s back up a little to cover one of the fundamental elements of Python.

An Interpreted Language

Python is an interpreted programming language, meaning Python code must be run using the Python interpreter.

Traditional programming languages like C/C++ are compiled, meaning that before it can be run, the human-readable code is passed into a compiler (special program) to generate machine code šC a series of bytes providing specific instructions to specific types of processors. However, Python is different. Since it¡¯s an interpreted programming language, each line of human-readable code is passed to an interpreter that converts it to machine code at run time.

So to run Python code, all you have to do is point the interpreter at your code.

Different Versions of the Python Interpreter

It¡¯s critical to point out that there are different versions of the Python interpreter. The major Python version you¡¯ll likely see is Python 2 or Python 3, but there are sub-versions (i.e. Python 2.7, Python 3.5, Python 3.7, etc.). Sometimes these differences are subtle. Sometimes they¡¯re dramatically different. It¡¯s important to always know which Python version is compatible with your Python code.

Run a script using the Python interpreter

To run a script, we have to point the Python interpreter at our Python code…but how do we do that? There are a few different ways, and there are some differences between how Windows and Linux/Mac operating systems do things. For these examples, we¡¯re assuming that both Python 2.7 and Python 3.5 are installed.

Our Test Script

For our examples, we¡¯re going to start by using this simple script called test.py.

test.py
print(¡°Aw yeah!¡±)'

How to Run a Python Script on Windows

The py Command

The default Python interpreter is referenced on Windows using the command py. Using the Command Prompt, you can use the -V option to print out the version.

Command Prompt
> py -V
Python 3.5

You can also specify the version of Python you’d like to run. For Windows, you can just provide an option like -2.7 to run version 2.7.

Command Prompt
> py -2.7 -V
Python 2.7

On Windows, the .py extension is registered to run a script file with that extension using the Python interpreter. However, the version of the default Python interpreter isn¡¯t always consistent, so it¡¯s best to always run your scripts as explicitly as possible.

To run a script, use the py command to specify the Python interpreter followed by the name of the script you want to run with the interpreter. To avoid using the full file path to your script (i.e. X:\足球竞彩网 Assembly\test.py), make sure your Command Prompt is in the same directory as your Python script file. For example, to run our script test.py, run the following command:

Command Prompt
> py -3.5 test.py
Aw yeah!

Using a Batch File

If you don¡¯t want to have to remember which version to use every time you run your Python program, you can also create a batch file to specify the command. For instance, create a batch file called test.bat with the contents:

test.bat
@echo off
py -3.5 test.py

This file simply runs your py command with the desired options. It includes an optional line “@echo off” that prevents the py command from being echoed to the screen when it¡¯s run. If you find the echo helpful, just remove that line.

Now, if you want to run your Python program test.py, all you have to do is run this batch file.

Command Prompt
> test.bat
Aw yeah!

How to Run a Python Script on Linux/Mac

The py Command

Linux/Mac references the Python interpreter using the command python. Similar to the Windows py command, you can print out the version using the -V option.

Terminal
$ python -V
Python 2.7

For Linux/Mac, specifying the version of Python is a bit more complicated than Windows because the python commands are typically a bunch of symbolic links (symlinks) or shortcuts to other commands. Typically, python is a symlink to the command python2, python2 is a symlink to a command like python2.7, and python3 is a symlink to a command like python3.5. One way to view the different python commands available to you is using the following command:

Terminal
$ ls -1 $(which python)* | egrep ¡®python($|[0-9])¡¯ | egrep -v config
/usr/bin/python
/usr/bin/python2
/usr/bin/python2.7
/usr/bin/python3
/usr/bin/python3.5

To run our script, you can use the Python interpreter command and point it to the script.

Terminal
$ python3.5 test.py
Aw yeah!

However, there¡¯s a better way of doing this.

Using a shebang

First, we¡¯re going to modify the script so it has an additional line at the top starting with ¡®#!¡¯ and known as a shebang (shebangs, shebangs¡­).

test.py
#!/usr/bin/env python3.5
print(¡°Aw yeah!¡±)

This special shebang line tells the computer how to interpret the contents of the file. If you executed the file test.py without that line, it would look for special instruction bytes and be confused when all it finds is a text file. With that line, the computer knows that it should run the contents of the file as Python code using the Python interpreter.

You could also replace that line with the full file path to the interpreter:

#!/usr/bin/python3.5

However, different versions of Linux might install the Python interpreter in different locations, so this method can cause problems. For maximum portability, I always use the line with /usr/bin/env that looks for the python3.5 command by searching the PATH environment variable, but the choice is up to you.

Next, we¡¯re going to set the permissions of this file to be Python executable with this command:

Terminal
$ chmod +x test.py

Now we can run the program using the command ./test.py!

Terminal
$ ./test.py
Aw yeah!

Pretty sweet, eh?

Run the Python Interpreter Interactively

One of the awesome things about Python is that you can run the interpreter in an interactive mode. Instead of using your py or python command pointing to a file, run it by itself, and you¡¯ll get something that looks like this:

Command Prompt
> py
Python 3.7.3 (v3.7.3:ef4ec6ed12, Mar 25 2019, 21:26:53) [MSC v.1916 32 bit (Intel)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>>

Now you get an interactive command prompt where you can type in individual lines of Python!

Command Prompt (Python Interpreter)
>>> print(¡°Aw yeah!¡±)
Aw yeah!

What¡¯s great about using the interpreter in interactive mode is that you can test out individual lines of Python code without writing an entire program. It also remembers what you¡¯ve done, just like in a script, so things like functions and variables work the exact same way.

Command Prompt (Python Interpreter)
>>> x = "Still got it."
>>> print(x)
Still got it.

How to Run a Python Script from a Text Editor

Depending on your workflow, you may prefer to run your Python program or Python script file directly from your text editor. Different text editors provide fancy ways of doing the same thing we¡¯ve already done ¡ª pointing the Python interpreter at your Python code. To help you along, I¡¯ve provided instructions on how to do this in four popular text editors.

  1. Notepad++
  2. VSCode
  3. Sublime Text
  4. Vim

1. Notepad++

Notepad++ is my favorite general purpose text editor to use on Windows. It¡¯s also super easy to run a Python program from it.

Step 1: Press F5 to open up the Run¡­ dialogue

Step 2: Enter the py command like you would on the command line, but instead of entering the name of your script, use the variable FULL_CURRENT_PATH like so:

py -3.5 -i "$(FULL_CURRENT_PATH)"

You¡¯ll notice that I¡¯ve also included a -i option to our py command to ¡°inspect interactively after running the script¡±. All that means is it leaves the command prompt open after it¡¯s finished, so instead of printing ¡°Aw yeah!¡± and then immediately quitting, you get to see the Python program¡¯s output.

Step 3: Click Run

2. VSCode

VSCode is a Windows text editor designed specifically to work with code, and I¡¯ve recently become a big fan of it. Running a Python program from VSCode is a bit complicated to set it up, but once you¡¯ve done that, it works quite nicely.

Step 1: Go to the Extensions section by clicking this symbol or pressing CTRL+SHIFT+X.

Step 2: Search and install the extensions named Python and Code Runner, then restart VSCode.

Step 3: Right click in the text area and click the Run Code option or press CTRL+ALT+N to run the code.

Note: Depending on how you installed Python, you might run into an error here that says ¡®python¡¯ is not recognized as an internal or external command. By default, Python only installs the py command, but VSCode is quite intent on using the python command which is not currently in your PATH. Don¡¯t worry, we can easily fix that.

Step 3.1: Locate your Python installation binary or download another copy from www.python.org/downloads. Run it, then select Modify.

Step 3.2: Click next without modifying anything until you get to the Advanced Options, then check the box next to Add Python to environment variables. Then click Install, and let it do its thing.

Step 3.3: Go back to VSCode and try again. Hopefully, it should now look a bit more like this:

A screenshot of a code editor showing how to run a Python script.

3. Sublime Text

Sublime Text is a popular text editor to use on Mac, and setting it up to run a Python program is super simple.

Step 1: In the menu, go to Tools ¡ú Build System and select Python.

A screenshot of a code editor showing how to run a Python script.

Step 2: Press command ? +b or in the menu, go to Tools ¡ú Build.

4. Vim

Vim is my text editor of choice when it comes to developing on Linux/Mac operating systems, and it can also be used to easily run a Python program.

Step 1: Enter the command :w !python3 and hit enter.

A terminal window showing how to run a Python script.

Step 2: Profit.

A terminal window showing how to run a Python script.

Now that you can successfully run your Python code, you¡¯re well on your way to speaking parseltongue!

– – – – –

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A Beginner’s Guide to Learn Python Programming

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Estimated reading time: 7 minutes

WHAT IS PYTHON?: AN INTRODUCTION

Python is one of the most popular and user-friendly programming languages out there. As a developer who¡¯s learned a number of programming languages, Python is one of my favorites due to its simplicity and power. Whether I¡¯m rapidly prototyping a new idea or developing a robust piece of software to run in production, Python is usually my language of choice.

The Python programming language is ideal for folks first learning to program. It abstracts away many of the more complicated elements of computer programming that can trip up beginners, and this simplicity gets you up-and-running much more quickly!

For instance, the classic ¡°Hello world¡± program (it just prints out the words ¡°Hello World!¡±) looks like this in C:

However, to understand everything that¡¯s going on, you need to understand what #include means (am I excluding anyone?), how to declare a function, why there¡¯s an ¡°f¡± appended to the word ¡°print,¡± etc., etc.

Not only is this an easier starting point, but as the complexity of your Python programming grows, this simplicity will make sure you¡¯re spending more time writing awesome code and less time tracking down bugs! 

Since Python is popular and open-source, there¡¯s a thriving community of Python application developers online with extensive forums and documentation for whenever you need help. No matter what your issue is, the answer is usually only a quick Google search away.

If you¡¯re new to programming or just looking to add another language to your arsenal, I would highly encourage you to join our community.

What Type of Language is Python?

Named after the classic British comedy troupe Monty Python, Python is a general-purpose, interpreted, object-oriented, high-level programming language with dynamic semantics. That¡¯s a bit of a mouthful, so let¡¯s break it down.

足球竞彩网-Purpose

Python is a general-purpose language which means it can be used for a wide variety of development tasks. Unlike a domain-specific language that can only be used for specific types of applications (think JavaScript and HTML/CSS for web development), a general-purpose language like Python can be used for:

Web applications: Popular frameworks like the Django web application and Flask are written in Python.

Desktop applications: The Dropbox client is written in Python.

Scientific and numeric computing: Python is the top choice for data science and machine learning.

Cybersecurity: Python is excellent for data analysis, writing system scripts that interact with an operating system, and communicating over network sockets.

Interpreted

Python is an interpreted language, meaning Python program code must be run using the Python interpreter.

Traditional programming languages like C/C++ are compiled, meaning that before it can be run, the human-readable code is passed into a compiler (special program) to generate machine code ¡ª a series of bytes providing specific instructions to specific types of processors. However, Python is different. Since it¡¯s an interpreted programming language, each line of human-readable code is passed to an interpreter that converts it to machine code at run time.

In other words, instead of having to go through the sometimes complicated and lengthy process of compiling your code before running it, you just point the Python interpreter at your code, and you¡¯re off!

Part of what makes an interpreted language great is how portable it is. Compiled languages must be compiled for the specific type of computer they¡¯re run on (i.e. think your phone vs. your laptop). For Python, as long as you¡¯ve installed the interpreter for your computer, the exact same code will run almost anywhere!

Object-Oriented

Python is an Object-Oriented Programming (OOP) language which means that all of its elements are broken down into things called objects. A Python object is very useful for software architecture and often makes it simpler to write large, complicated applications. 

High-Level

Python is a high-level language which really just means that it¡¯s simpler and more intuitive for a human to use. Low-level languages such as C/C++ require a much more detailed understanding of how a computer works. With a high-level language, many of these details are abstracted away to make your life easier.

For instance, say you have a list of three numbers ¡ª 1, 2, and 3 ¡ª and you want to append the number 4 to that list. In C, you have to worry about how the computer uses memory, understands different types of variables (i.e., an integer vs. a string), and keeps track of what you¡¯re doing.

Implementing this in C code is rather complicated:

However, implementing this in Python code is much simpler:

Since a list in Python is an object, you don¡¯t need to specifically define what the data structure looks like or explain to the computer what it means to append the number 4. You just say ¡°list.append(4)¡±, and you¡¯re good.

Under the hood, the computer is still doing all of those complicated things, but as a developer, you don¡¯t have to worry about them! Not only does that make your code easier to read, understand, and debug, but it means you can develop more complicated programs much faster.

Dynamic Semantics

Python uses dynamic semantics, meaning that its variables are dynamic objects. Essentially, it¡¯s just another aspect of Python being a high-level language.

In the list example above, a low-level language like C requires you to statically define the type of a variable. So if you defined an integer x, set x = 3, and then set x = ¡°pants¡±, the computer will get very confused. However, if you use Python to set x = 3, Python knows x is an integer. If you then set x = ¡°pants¡±, Python knows that x is now a string.

In other words, Python lets you assign variables in a way that makes more sense to you than it does to the computer. It¡¯s just another way that Python programming is intuitive.

It also gives you the ability to do something like creating a list where different elements have different types like the list [1, 2, ¡°three¡±, ¡°four¡±]. Defining that in a language like C would be a nightmare, but in Python, that¡¯s all there is to it.

Being so powerful, flexible, and user-friendly, the Python language has become incredibly popular. Python¡¯s popularity is important for a few reasons.

Python Programming is in Demand

If you¡¯re looking for a new skill to help you land your next job, learning Python is a great move. Because of its versatility, Python is used by many top tech companies. Netflix, Uber, Pinterest, Instagram, and Spotify all build their applications using Python. It¡¯s also a favorite programming language of folks in data science and machine learning, so if you¡¯re interested in going into those fields, learning Python is a good first step. With all of the folks using Python, it¡¯s a programming language that will still be just as relevant years from now.

Dedicated 足球竞彩网

Python developers have tons of support online. It¡¯s open-source with extensive documentation, and there are tons of articles and forum posts dedicated to it. As a professional Python developer, I rely on this community everyday to get my code up and running as quickly and easily as possible.

There are also numerous Python libraries readily available online! If you ever need more functionality, someone on the internet has likely already written a library to do just that. All you have to do is download it, write the line ¡°import <library>¡±, and off you go. Part of Python¡¯s popularity in data science and machine learning is the widespread use of its libraries such as NumPy, Pandas, SciPy, and TensorFlow.

Conclusion

Python is a great way to start programming and a great tool for experienced developers. It¡¯s powerful, user-friendly, and enables you to spend more time writing badass code and less time debugging it. With all of the libraries available, it will do almost anything you want it to.

The final answer to the question ¡°What is Python”? Awesome. Python is awesome.