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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 — 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.

First Things First: 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 — 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 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 — “Which countries the company’s products have been shipped to?” — 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 General 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 — on campus and online. You can also get a taste in our interactive tableau training with these classes and workshops.

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 General Assembly London since 2016.

Samanta Dal Pont, Data Analytics Instructor, General Assembly London

Computer Science vs. Data Science: What is the Difference?

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Maybe you want to learn more about data science since you’ve heard it’s “the sexiest job of the 21st century.” Or maybe your software engineer friend is trying to talk you into learning computer science. Either way, both data science and computer science skills are in demand. In this article, we will cover the major differences between data science and computer science to clarify the distinction between these two fields.

Before we dive into the differences, let’s define these two sciences:

Data Science vs. Computer Science

Data science is an interdisciplinary field that uses data to extract insights and inform decisions. It’s often referred to as a combination of statistics, business acumen, and computer science. Data scientists clean, explore, analyze, and model with data using programming languages such as Python and R along with techniques such as statistical models, machine learning, and deep learning.

While it’s one part of data science, computer science is its own broader field of study involving a range of both theoretical and practical topics like data structures and algorithms, hardware and software, and information processing. It has many applications in fields like machine learning, software engineering, and mathematics.

History

While many of the topics used in data science have been around for a while, data science as a field is in its infancy. In 1974, Peter Naur defined the term “data science” in his work, Concise Survey of Computer Methods. However, even Naur couldn’t have predicted the vast amount of data that our modern world would generate on a daily basis only a few decades later. It wasn’t until the early 2000s that data science was recognized as its own field. It gained popularity in the early 2010s, leading to the field as we know it today — a blend of statistics and computer science to drive insights and make data-driven business decisions. “Data science,” “big data,” “artificial intelligence,” “machine learning,” and “deep learning” have all become buzzwords in today’s world. These are all components of data science and while trendy, they can provide practical benefits to companies. Historically, we did not have the storage capacity to hold the amount of data that we are able to collect and store today. This is one reason that data science has become a popular field only recently. The emergence of big data and the advancements in technology have paved the way for individuals and businesses to harness the power of data. While many of the tools that data scientists use have been around for many years, we have not had the software or hardware requirements to make use of these tools until recently.

Computer science, on the other hand, has been a field of study for centuries. This is one of the main differences between it and data science. Ada Lovelace is known for pioneering the field of computer science as the person who wrote the first computer algorithm in the 1840s. However, computing devices such as the abacus date back thousands of years. Computer science is a topic that has been formally researched for much longer than data science, and companies have been using computer science tools for decades. It’s an umbrella field that has numerous subdomains and applications. 

Applications

The applications of each of these fields in the industry differs as well. Computer science skills are used in many different jobs including that of a data scientist. However, common roles involving computer science skills include software engineers, computer engineers, software developers, and web developers. Two roles that use computer science, front end engineer and Java developer, ranked first and second respectively on Glassdoor’s 50 Best Jobs in America for 2020 list. While these roles do not formally require degrees, many people in these jobs hold a degree or come from a background in computer science. 

Common computer science job tasks include writing, testing, and debugging code, developing software, and designing applications. Individuals that use computer science in their roles often create new software and web applications. They need to have excellent problem solving skills and be able to write code in programming languages such as Python, Ruby, JavaScript, Java, or C#. They also need to have a fundamental understanding of how these languages work, and be well-versed in object oriented programming.

Data science is applied in job titles such as data scientist, data analyst, machine learning engineer, and data engineer. Data scientist and data engineer ranked third and sixth respectively on Glassdoor’s 50 Best Jobs in America for 2020. Individuals in these roles come from a variety of backgrounds including computer science, statistics, and mathematics. 

Common data science job tasks include cleaning and exploring data, extracting insights from data, and building and optimizing models. Data scientists analyze and reach conclusions based on data. They need to be well versed in statistics and mathematics topics including linear algebra and calculus as well as programming languages such as Python, R, and SQL. They also need to have excellent communication skills as they are often presenting insights, data visualizations, and recommendations to stakeholders.

Since computer science is one component of data science, there is often crossover in these roles and responsibilities. For example, computer science tasks like programming and debugging are used in both computer science jobs and data science jobs. Both of these fields are highly technical and require knowledge of data structures and algorithms. However, the depth of this knowledge required for computer science vs. data science varies. It’s often said that data scientists know more about statistics than a computer scientist but more about computer science than a statistician. This reinforces the interdisciplinary nature of data science.

The Use of Data

Data, or information such as numbers, text, and images, has applications in both computer science and data science. The study and use of data structures is a topic in computer science. Data structures are ways to organize, manage, and store data in ways that it can be used efficiently; a sub-domain of computer science, it allows us to store and access data in our computer’s memory. Data science benefits from data structures to access data, but the main goal of data science is to analyze and make decisions based on the data, often using statistics and machine learning.

The Future of Computer Science and Data Science

Today, all companies and industries can benefit from both of these fields. Computer scientists drive business value by developing software and tools while data scientists drive business value by answering questions and making decisions based on data. As software continues to integrate with our lives and daily routines, computer science skills will continue to be critical and in demand. As we continue to create and store vast amounts of data on a daily basis, data science skills will also continue to be critical and in demand. Both fields are constantly evolving as technology advances and both computer scientists and data scientists need to stay current with the latest tools, methods, and technologies.

The field of data science would not exist without computer science. Today, the two fields complement each other to further applications of artificial intelligence, machine learning, and personalized recommendations. Many of the luxuries that we have today — a favorite streaming service that recommends new movies, the ability to unlock our phones with facial recognition technology, or virtual home assistants that let us play our favorite music just by speaking — are made possible by computer science and made better by data science. As long as bright, motivated individuals continue to learn data science and computer science, these two fields will continue to advance technology and improve the quality of our lives.

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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 General 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 — as well as the libraries available for data cleaning, visualization, and machine learning — 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.

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

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BreakintoDataScienceAt first glance, data science seems to be just another business buzzword — 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 — 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 — 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.

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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 — 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:

●  Research what to expect in the interview.

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

●  Research common interview questions and company information.

●  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 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.

Explore Data Workshops

Free Lesson: Data Science Essentials in Under 30 Minutes

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Demand for data scientists has increased 663% in five years, and the call for machine learning skills is up 809%.* In this free lesson, GA instructor Danny Malter will give you a better understanding of data science, including:

  • What data science skills could do for your career.
  • Examples of how data science impacts the real world.
  • Algorithms in action.

When you’re ready to go further, explore our upcoming Data Science course to cement your foundation in machine learning, predictive models, and Python programming. Or get inspired by these resources:

  • What does a data scientist do all day?
  • How one GA data science alum went from barista to analyst (and a threefold pay increase)
  • Get a beginner’s guide to machine learning, Python, and SQL

*Burning Glass, The Hybrid Job Economy. The study covers the time period of 2013–2018.

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 — 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 General 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. Community Recognition

The second piece is the community piece, especially if you have plenty of time before the data science interview. Community 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 — 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!


[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 – 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:\General 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 — 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:

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 → Build System and select Python.

Step 2: Press command +b or in the menu, go to Tools → 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.

Step 2: Profit.

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

– – – – –

3 Major Uses for Python Programming

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Python is a popular and versatile programming language. But what is Python used for? If you’re interested in learning Python or are in the process of learning how to code in Python, your efforts will be greatly rewarded as there’s so much you can do with it. In this article, we’ll explore the top three major uses for Python.

Before we dive into the uses, let’s briefly discuss why Python has so many uses in the first place. What characteristics does Python have that allow it to be so useful? Python is:

  • General-purpose: The language was designed to be “general purpose”, meaning it doesn’t have language constructs to force it into a specific application domain. Other programming languages that are general-purpose include (but are not limited to): C++, Go, Java, JavaScript, and Ruby. 
  • Readable: Python is a high-level programming language, meaning it has a higher level of abstraction from machine language and has a simple syntax and semantics (e.g., indentation instead of curly brackets to indicate blocks), which lends to its readability. 
  • Versatile: Python has a large standard library, meaning it comes equipped with a lot of specialized code to handle different tasks. For example, instead of writing your own Python code to read and write CSV files, you can use the csv module’s reader and writer objects. In addition, there are many open-source libraries and frameworks that provide additional value for Python programmers — especially those in machine learning, deep learning, application development, and game development — and scientific computing will find an ample supply of libraries and modules.

What is Python used for? There are so many different tasks that Python can accomplish. You can use it to build recommender systems, create cool charts and graphs, build restful APIs, program robots, conduct scientific computing, manipulate text data or extract text from images; the list goes on and on. 

The best way to think about uses for Python is through the most active and popular disciplines that rely on Python programming:

  1. Artificial intelligence and machine learning
  2. Data analysis and data visualization
  3. Web development

1. Artificial Intelligence and Machine Learning

What it is: Artificial Intelligence is a concept that’s more or less the idea of machines or computers that mimic human cognitive functions such as “learning” and “problem-solving.” Activities like driving a car, playing chess, and answering a question are all structured, logic-based things that humans can do that are being implemented by computers today. At the heart of this activity is machine learning, which is the process that a computer takes to learn the relationships between variables in data so well that it can predict future outcomes (usually on unseen data). If data is the input (“knowledge”), the machines understand the relationships between variables (“learning”) and it can predict what the next step is (“outcome”) — then you have machine learning. 

How Python is used: Artificial intelligence requires a lot of data, which in turn requires appropriate storage, pre-processing, and data modeling techniques to be implemented. Deep learning is the intermediary component; it’s the use of specialized models (neural networks) that can handle “big data” at scale. Python is a programming language of choice for the machine learning, deep learning, and artificial intelligence community due to it being a minimalistic and intuitive language with a significant number of libraries dedicated to machine learning activities, which reduces the time required to implement and get results. R is another popular language used by machine learning enthusiasts and practitioners, but Python tends to be more popular because of the number of machine learning and AI-related efforts coming from the tech community, which uses Python. For example, TensorFlow is Google’s AI platform and open-source software library used for machine learning and the creation of neural networks for AI purposes.

Helpful links: Machine Learning, Python Libraries for Machine Learning

2. Data Analysis and Data Visualization

What it is: Data analysis is the specialized practice of analyzing data, both big and small, for information and insights. Results of data analyses are often visualized, for the benefit of the recipient, and the tools and techniques used to communicate results visually requires the specialization that is known as data visualization. Data analysis and data visualization are not unique to any industry. It’s better to think of them as process-focused roles than industry-specific roles. After all, every company and industry has its own data to work with. What data analysis is not is the management of data from servers and storage, although some data analysts specialize in data management.  

How Python is used: Data analysis and data visualization are specialized roles that can implement Python in ways that are integral to the mission of each role. A data analyst will use Python for data wrangling and data transformation, which is converting data from its raw format to a usable, analyzable format. Then, using open-sourced libraries like Pandas, NumPy, and SciPy, data analysts can manipulate and analyze both numerical and categorical data. In order to visualize data locally, additional libraries such as Seaborn, matplotlib, ggplot, and bokeh, can be used. Some data visualization professionals prefer using Python over business intelligence platforms like PowerBI and Tableau because it’s free, easy to learn, and reduces the need to have to use additional software to create visualizations. 

Helpful links: Data Analysis in Python, Python Libraries for Data Visualization

3. Web Development 

What it is: Web development is a catch-all term for creating web applications and application programming interfaces (APIs) for the web. Web development is a highly specialized role that can be explained by the design pattern known as model, view, and controller (MVC). These terms represent the specialized layers of code of a web application or API. The model involves the code for an application’s dynamic data structure, the view involves the code that directly interacts with the user, and the controller is the code that handles user interactions and works to facilitate input going from the view to the model.

How Python is used: Python has several MVC frameworks that can be used for web application development straight out of the box, and this includes Django, turbogears, and web2py. While a web framework is not required for web development, it’s beneficial to use them as they greatly speed up the development progress. For beginners, learning Python’s syntax and the libraries needed for building a web application or API is a high level of effort, but the alternative would involve a much greater effort, as it would require the knowledge and correct use of multiple programming languages instead of Python.

Helpful links: Full Stack Python: Web Development, Web Frameworks for Python

Conclusion

We’ve explored the major uses for Python, which include machine learning and artificial intelligence, data analysis and data visualization, and web development. If you’re currently learning Python programming, then you’re off to a good start, especially if you’re considering pursuing work in any of the aforementioned areas. For those unsure how to start learning Python, I encourage you to read some of our other posts, which provide more details and tips on how to get started.

Python: An Introduction

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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.

In Python, the same program looks like this:

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 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.

General-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 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 — 1, 2, and 3 — 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.

It’s Popular. Like, Super Popular.

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 Community

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.