At 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.
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.
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.
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.
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 Pythoninterpreter.
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:
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.
Notepad++
VSCode
Sublime Text
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!
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.
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.
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 Pythoninterpreter.
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.
In today’s digital age, we’re constantly bombarded with information about new apps, transformative technologies, and the latest and greatest artificial intelligence system. While these technologies may serve very different purposes in our life, all of them share one thing in common: They rely on data. More specifically, they all use databases to capture, store, retrieve, and aggregate data. This begs the question: How do we actually interact with databases to accomplish all of this? The answer: We use Structured Query Language, or SQL (pronounced “sequel” or “ess-que-el”).
Put simply, SQL is the language of data — it’s a programming language that enables us to efficiently create, alter, request, and aggregate data from those mysterious things called databases. It gives us the ability to make connections between different pieces of information, even when we’re dealing with huge data sets. Modern applications are able to use SQL to deliver really valuable pieces of information that would otherwise be difficult for humans to keep track of independently. In fact, pretty much every app that stores any sort of information uses a database. This ubiquity means that developers use SQL to log, record, alter, and present data within the application, while analysts use SQL to interrogate that same data set in order to find deeper insights.
Finding SQL in Everyday Life
Think about the last time you looked up the name of a movie on IMDB. I’ll bet you quickly noticed an actress on the cast list and thought something like, “I didn’t realize she was in that,” then clicked a link to read her bio. As you were navigating through that app, SQL was responsible for returning the information you “requested” each time you clicked a link. This sort of capability is something we’ve come to take for granted these days.
Let’s look at another example that truly is cutting-edge, this time at the intersection of local government and small business. Many metropolitan cities are supporting open data initiatives in which public data is made easily accessible through access to the databases that store this information. As an example, let’s look at Los Angeles building permit data, business listings, and census data.
Imagine you work at a real estate investment firm and are trying to find the next up-and-coming neighborhood. You could use SQL to combine the permit, business, and census data in order to identify areas that are undergoing a lot of construction, have high populations, and contain a relatively low number of businesses. This might be a great opportunity to purchase property in a soon-to-be thriving neighborhood! For the first time in history, it’s easy for a small business to leverage quantitative data from the government in order to make a highly informed business decision.
Leveraging SQL to Boost Your Business and Career
There are many ways to harness SQL’s power to supercharge your business and career, in marketing and sales roles, and beyond. Here are just a few:
Increase sales: A sales manager could use SQL to compare the performance of various lead-generation programs and double down on those that are working.
Track ads: A marketing manager responsible for understanding the efficacy of an ad campaign could use SQL to compare the increase in sales before and after running the ad.
Streamline processes: A business manager could use SQL to compare the resources used by various departments in order to determine which are operating efficiently.
SQL at General Assembly
At General Assembly, we know businesses are striving to transform their data from raw facts into actionable insights. The primary goal of our data analytics curriculum, from workshops to full-time courses, is to empower people to access this data in order to answer their own business questions in ways that were never possible before.
To accomplish this, we give students the opportunity to use SQL to explore real-world data such as Firefox usage statistics, Iowa liquor sales, or Zillow’s real estate prices. Our full-time Data Science Immersive and part-time Data Analytics courses help students build the analytical skills needed to turn the results of those queries into clear and effective business recommendations. On a more introductory level, after just a couple of hours of in one of our SQL workshops, students are able to query multiple data sets with millions of rows.
Michael Larner is a passionate leader in the analytics space who specializes in using techniques like predictive modeling and machine learning to deliver data-driven impact. A Los Angeles native, he has spent the last decade consulting with hundreds of clients, including 50-plus Fortune 500 companies, to answer some of their most challenging business questions. Additionally, Michael empowers others to become successful analysts by leading trainings and workshops for corporate clients and universities, including General Assembly’s part-time Data Analytics course and SQL/Excel workshops in Los Angeles.
“In today’s fast-paced, technology-driven world, data has never been more accessible. That makes it the perfect time — and incredibly important — to be a great data analyst.”
– Michael Larner, Data Analytics Instructor, General Assembly Los Angeles
Data is the engine driving today’s digital world. From major companies to government agencies to nonprofits, business leaders are hunting for talent that can help them collect, sort, and analyze vast amounts of data — including geodata — to tackle the world’s biggest challenges.
In the case of emergency management, disaster preparedness, response, and recovery, this means using data to expertly identify, manage, and mitigate the risks of destructive hurricanes, intense droughts, raging wildfires, and other severe weather and climate events. And the pressure to make smarter data-driven investments in disaster response planning and education isn’t going away anytime soon — since 1980, the U.S. has suffered 246 weather and climate disasters that topped over $1 billion in losses according to the National Centers for Environmental Information.
Employing creative approaches for tackling these pressing issues is a big reason why New Light Technologies (NLT), a leading company in the geospatial data science space, joined forces with General Assembly’s (GA) Data Science Immersive (DSI) course, a hands-on intensive program that fosters job-ready data scientists. Global Lead Data Science Instructor at GA, Matt Brems, and Chief Scientist and Senior Consultant at NLT, Ran Goldblatt, recognized a unique opportunity to test drive collaboration between DSI students and NLT’s consulting work for the Federal Emergency Management Agency (FEMA) and the World Bank.
The goal for DSI students: build data solutions that address real-world emergency preparedness and disaster response problems using leading data science tools and programming languages that drive visual, statistical, and data analyses. The partnership has so far produced three successful cohorts with nearly 60 groups of students across campuses in Atlanta, Austin, Boston, Chicago, Denver, New York City, San Francisco, Los Angeles, Seattle, and Washington, D.C., who learn and work together through GA’s Connected Classroom experience.
Taking on Big Problems With Smart Data
DSI students present at NLT’s Washington, D.C. office.
“GA is a pioneering institution for data science, so many of its goals coincide with ours. It’s what also made this partnership a unique fit. When real-world problems are brought to an educational setting with students who are energized and eager to solve concrete problems, smart ideas emerge,” says Goldblatt.
Over the past decade, NLT has supported the ongoing operation, management, and modernization of information systems infrastructure for FEMA, providing the agency with support for disaster response planning and decision-making. The World Bank, another NLT client, faces similar obstacles in its efforts to provide funding for emergency prevention and preparedness.
These large-scale issues served as the basis for the problem statements NLT presented to DSI students, who were challenged to use their newfound skills — from developing data algorithms and analytical workflows to employing visualization and reporting tools — to deliver meaningful, real-time insights that FEMA, the World Bank, and similar organizations could deploy to help communities impacted by disasters. Working in groups, students dived into problems that focused on a wide range of scenarios, including:
Using tools such as Google Street View to retrieve pre-disaster photos of structures, allowing emergency responders to easily compare pre- and post-disaster aerial views of damaged properties.
Optimizing evacuation routes for search and rescue missions using real-time traffic information.
Creating damage estimates by pulling property values from real estate websites like Zillow.
Extracting drone data to estimate the quality of building rooftops in Saint Lucia.
“It’s clear these students are really dedicated and eager to leverage what they learned to create solutions that can help people. With DSI, they don’t just walk away with an academic paper or fancy presentation. They’re able to demonstrate they’ve developed an application that, with additional development, could possibly become operational,” says Goldblatt.
Students who participated in the engagements received the opportunity to present their work — using their knowledge in artificial intelligence and machine learning to solve important, tangible problems — to an audience that included high-ranking officials from FEMA, the World Bank, and the United States Agency for International Development (USAID). The students’ projects, which are open source, are also publicly available to organizations looking to adapt, scale, and implement these applications for geospatial and disaster response operations.
“In the span of nine weeks, our students grew from learning basic Python to being able to address specific problems in the realm of emergency preparedness and disaster response,” says Brems. “Their ability to apply what they learned so quickly speaks to how well-qualified GA students and graduates are.”
Here’s a closer look at some of those projects, the lessons learned, and students’ reflections on how GA’s collaboration with NLT impacted their DSI experience.
Leveraging Social Media to Map Disasters
The NLT engagements feature student work that uses social media to identify “hot spots” for disaster relief.
During disasters, one of the biggest challenges for disaster relief organizations is not only mapping and alerting users about the severity of disasters but also pinpointing hot spots where people require assistance. While responders employ satellite and aerial imagery, ground surveys, and other hazard data to assess and identify affected areas, communities on the ground often turn to social media platforms to broadcast distress calls and share status updates.
Cameron Bronstein, a former botany and ecology major from New York, worked with group members to build a model that analyzes and classifies social media posts to determine where people need assistance during and after natural disasters. The group collected tweets related to Hurricane Harvey of 2017 and Hurricane Michael of 2018, which inflicted billions of dollars of damage in the Caribbean and Southern U.S., as test cases for their proof-of-concept model.
“Since our group lacked premium access to social media APIs, we sourced previously collected and labeled text-based data,” says Bronstein. “This involved analyzing and classifying several years of text language — including data sets that contained tweets, and transcribed phone calls and voice messages from disaster relief organizations.”
Contemplating on what he enjoyed most while working on the NLT engagement, Bronstein states, “Though this project was ambitious and open to interpretation, overall, it was a good experience and introduction to the type of consulting work I could end up doing in the future.”
Quantifying the Economic Impact of Natural Disasters
Students use interactive data visualization tools to compile and display their findings.
Prior to enrolling in General Assembly’s DSI course in Washington D.C., Ashley White learned early in her career as a management consultant how to use data to analyze and assess difficult client problems. “What was central to all of my experiences was utilizing the power of data to make informed strategic decisions,” states White.
It was White’s interest in using data for social impact that led her to enroll in DSI where she could be exposed to real-world applications of data science principles and best practices. Her DSI group’s task: developing a model for quantifying the economic impact of natural disasters on the labor market. The group selected Houston, Texas as its test case for defining and identifying reliable data sources to measure the economic impact of natural disasters such as Hurricane Harvey.
As they tackled their problem statement, the group focused on NLT’s intended goal, while effectively breaking their workflow into smaller, more manageable pieces. “As we worked through the data, we discovered it was hard to identify meaningful long-term trends. As scholarly research shows, most cities are pretty resilient post-disaster, and the labor market bounces back quickly as the city recovers,” says White.
The team compiled their results using the analytics and data visualization tool Tableau, incorporating compelling visuals and story taglines into a streamlined, dynamic interface. For version control, White and her group used GitHub to manage and store their findings, and share recommendations on how NLT could use the group’s methodology to scale their analysis for other geographic locations. In addition to the group’s key findings on employment fluctuations post-disaster, the team concluded that while natural disasters are growing in severity, aggregate trends around unemployment and similar data are becoming less predictable.
Cultivating Data Science Talent in Future Engagements
Due to the success of the partnership’s three engagements, GA and NLT have taken steps to formalize future iterations of their collaboration with each new DSI cohort. Additionally, mutually beneficial partnerships with leading organizations such as NLT present a unique opportunity to uncover innovative approaches for managing and understanding the numerous ways data science can support technological systems and platforms. It’s also granted aspiring data scientists real-world experience and visibility with key decision-makers who are at the forefront of emergency and disaster management.
“This is only the beginning of a more comprehensive collaboration with General Assembly,” states Goldblatt. “By leveraging GA’s innovative data science curriculum and developing training programs for capacity building that can be adopted by NLT clients, we hope to provide students with essential skills that prepare them for the emerging, yet competitive, geospatial data job market. Moreover, students get the opportunity to better understand how theory, data, and algorithms translate to actual tools, as well as create solutions that can potentially save lives.”
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New Light Technologies, Inc. (NLT) provides comprehensive information technology solutions for clients in government, commercial, and non-profit sectors. NLT specializes in DevOps enterprise-scale systems integration, development, management, and staffing and offers a unique range of capabilities from Infrastructure Modernization and Cloud Computing to Big Data Analytics, Geospatial Information Systems, and the Development of Software and Web-based Visualization Platforms.
In today’s rapidly evolving technological world, successfully developing and deploying digital geospatial software technologies and integrating disparate data across large complex enterprises with diverse user requirements is a challenge. Our innovative solutions for real-time integrated analytics lead the way in developing highly scalable virtualized geospatial microservices solutions. Visit our website to find out more and contact us athttps://NewLightTechnologies.com.
Ever wonder how apps, websites, and machines seem to be able to predict the future? Like how Amazon knows what your next purchase may be, or how self-driving cars can safely navigate a complex traffic situation?
Machine learning is a branch of artificial intelligence (AI) that often leverages Python to build systems that can learn from and make decisions based on data. Instead of explicitly programming the machine to solve the problem, we show it how it was solved in the past and the machine learns the key steps that are required to do the same task on its own.
Machine learning is revolutionizing every industry by bringing greater value to companies’ years of saved data. Leveraging machine learning enables organizations to make more precise decisions instead of following intuition.
There’s an explosive amount of innovation around machine learning that’s being used within organizations, especially given that the technology is still in its early days. Many companies have invested heavily in building recommendation and personalization engines for their customers. But, machine learning is also being applied in a huge variety of back-office use cases as well, like to forecast sales, identify production bottlenecks, build efficient traffic routing systems, and more.
Machine learning algorithms fall into two categories: supervised and unsupervised learning.
Supervised Learning
Supervised learning tries to predict a future value by relying on training from past data. For instance, Netflix’s movie-recommendation engine is most likely supervised. It uses a user’s past movie ratings to train the model, then predicts what their rating would likely be for movies they haven’t seen and recommends the ones that score highly.
Supervised learning enjoys more commercial success than unsupervised learning. Some common use cases include fraud detection, image recognition, credit scoring, product recommendation, and malfunction prediction.
Unsupervised Learning
Unsupervised learning is about uncovering hidden structures within data sets. It’s helpful in identifying segments or groups, especially when there is no prior information available about them. These algorithms are commonly used in market segmentation. They enable marketers to identify target segments in order to maximize revenue, create anomaly detection systems to identify suspicious user behavior, and more.
For instance, Netflix may know how many customers it has, but wants to understand what kind of groupings they fall into in order to offer services targeted to them. The streaming service may have 50 or more different customer types, aka, segments, but its data team doesn’t know this yet. If the company knows that most of its customers are in the “families with children” segment, it can invest in building specific programs to meet those customer needs. But, without that information, Netflix’s data experts can’t create a supervised machine learning system.
So, they build an unsupervised machine learning algorithm instead, which identifies and extracts various customer segments within the data and allows them to identify groups such as “families with children” or “working professionals.”
How Python, SQL, and Machine Learning Work Together
To understand how SQL, Python, and machine learning relate to one another, let’s think of them as a factory. As a concept, a factory can produce anything if it has the right tools. More often than not, the tools used in factories are pretty similar (e.g., hammers and screwdrivers).
What’s amazing is that there can be factories that use those same tools but produce completely different products (e.g., tables versus chairs). The difference between these factories is not the tools, but rather how the factory workers use their expertise to leverage these tools and produce a different result.
In this case, our goal would be to produce a machine learning model, and our tools would be SQL and Python. We can use SQL to extract data from a database and Python to shape the data and perform the analyses that ultimately produce a machine learning model. Your knowledge of machine learning will ultimately enable you to achieve your goal.
To round out the analogy, an app developer, with no understanding of machine learning, might choose to use SQL and Python to build a web app. Again, the tools are the same, but the practitioner uses their expertise to apply them in a different way.
Machine Learning at Work
A wide variety of roles can benefit from machine learning know-how. Here are just a few:
Data scientist or analyst: Data scientists or analysts use machine learning to answer specific business questions for key stakeholders. They might help their company’s user experience (UX) team determine which website features most heavily drive sales.
Machine learning engineer: A machine learning engineer is a software engineer specifically responsible for writing code that leverages machine learning models. For example, they might build a recommendation engine that suggests products to customers.
Research scientist: A machine learning research scientist develops new technologies like computer vision for self-driving cars or advancements in neural networks. Their findings enable data professionals to deliver new insights and capabilities.
Machine Learning in Everyday Life: Real-World Examples
While machine learning-powered innovations like voice-activated robots seem ultra-futuristic, the technology behind them is actually widely used today. Here are some great examples of how machine learning impacts your daily life:
Recommendation engines: Think about how Spotify makes music recommendations. The recommendation engine peeks at the songs and albums you’ve listened to in the past, as well as tracks listened to by users with similar tastes. It then starts to learn the factors that influence your music preferences and stores them in a database, recommending similar music that you haven’t listened to — all without writing any explicit rules!
Voice-recognition technology: We’ve seen the emergence of voice assistants like Amazon’s Alexa and Google’s Assistant. These interactive systems are based entirely on voice-recognition technology powered by machine learning models.
Risk mitigation and fraud prevention: Insurers and creditors use machine learning to make accurate predictions on fraudulent claims based on previous consumer behavior, rather than relying on traditional analysis or human judgement. They also can use these analyses to identify high-risk customers. Both of these analyses help companies process requests and claims more quickly and at a lower cost.
Photo identification via computer vision: Machine learning is common among photo-heavy services like Facebook and the home-improvement site Houzz. Each of these services use computer vision — an aspect of machine learning — to automatically tag objects in photos without human intervention. For Facebook, these tend to be faces, whereas Houzz seeks to identify individual objects and link to a place where users can purchase them.
Why You and Your Business Need to Understand Data Science
As the world becomes increasingly data-driven, learning to leverage key technologies like machine learning — along with the programming languages Python (which helps power machine learning algorithms) and SQL — will create endless possibilities for your career and your organization. There are many pathways into this growing field, as detailed by our Data Science Standards Board, and now’s a great time to dive in.
In our paper A Beginner’s Guide to SQL, Python, and Machine Learning, we break down these three data sectors. These skills go beyond data to bring delight, efficiency, and innovation to countless industries. They empower people to drive businesses forward with a speed and precision previously unknown.
Individuals can use data know-how to improve their problem-solving skills, become more cross-functional, build innovative technology, and more. For companies, leveraging these technologies means smarter use of data. This can lead to greater efficiency, employees who are empowered to use data in innovative ways, and business decisions that drive revenue and success.
In the past few years, much attention has been drawn to the dearth of women and people of color in tech-related fields. A recent article in Forbes noted, “Women hold only about 26% of data jobs in the United States. There are a few reasons for the gender gap: a lack of STEM education for women early on in life, lack of mentorship for women in data science, and human resources rules and regulations not catching up to gender balance policies, to name a few.” Federal civil rights data further demonstrate that “black and Latino high school students are being shortchanged in their access to high-level math and science courses that could prepare them for college” and for careers in fields like data science.
As an education company offering tech-oriented courses at 20 campuses across the world, General Assembly is in a unique position to analyze the current crop of students looking to change the dynamics of the workplace.
Looking at GA data for our part-time programs (which typically reach students who already have jobs and are looking to expand their skill set as they pursue a promotion or a career shift), here’s what we found: While great strides have been made in fields like web development and user experience (UX) design, data science — a relatively newer concentration — still has a ways to go in terms of gender and racial equality.
Apache Spark is an open-source framework used for large-scale data processing. The framework is made up of many components, including four programming APIs and four major libraries. Since Spark’s release in 2014, it has become one of Apache’s fastest growing and most widely used projects of all time.
Spark uses an in-memory processing paradigm to speed up computation and run programs 10 to 100 times faster than other big data technologies like Hadoop MapReduce. According to the 2016 Apache Spark Survey, more than 900 companies, including IBM, Google, Netflix, Amazon, Microsoft, Intel, and Yahoo, use Spark in production for data processing and querying.
Apache Spark is important to the big data field because it represents the next generation of big data processing engines and is a natural successor to MapReduce. One of Spark’s advantages is that its use of four programming APIs — Scala, Python, R, and Java 8 — allows the user flexibility to work in the language of their choice. This makes the tool much more accessible to a wide range of programmers with different capabilities. Spark also has great flexibility in its ability to read all types of data from various locations such as Hadoop Distributed File Storage (HDFS), Amazon’s web-based Simple Storage Service (S3), or even the local filesystem.
Production-Ready and Scalable
Spark’s greatest advantage is that it maximizes the capabilities of data science’s most expensive resource: the data scientist. Computers and programs have become so fast, that we are no longer limited by what they can do as much as we are limited by human productivity. By providing a flexible language platform and having concise syntax, the data scientist can write more programs, iterate through their programs, and have them run much quicker. The code is production-ready and scalable, so there’s no need to hand off code requirements to a development team for changes.
It takes only a few minutes to write a word-count program in Spark, but would take much longer to write the same program in Java. Because the Spark code is so much shorter, there’s less of a need to debug or use version control tools.
Spark’s concise syntax can best be illustrated with the following examples. The Spark code is only four lines compared with almost 58 for Java.
Faster Processing
Spark utilizes in-memory processing to speed up applications. The older big data frameworks, such as Hadoop, use many intermediate disc reads and writes to accomplish the same task. For small jobs on several gigabytes of data, this difference is not as pronounced, but for machine learning applications and more complex tasks such as natural language processing, the difference can be tremendous. Logistic regression, a technique taught in all of General Assembly’s full- and part-time data science courses, can be sped up over 100x.
Spark has four key libraries that also make it much more accessible and provide a wider set of tools for people to use. Spark SQL is ideal for leveraging SQL skills or work with data frames; Spark Streaming has functions for data processing, useful if you need to process data in near real time; and GraphX has pre-written algorithms that are useful if you have graph data or need to do graph processing. The library most useful to students in our Data Science Immersive, though, is the Spark MLlib machine learning library, which has prewritten distributed machine learning algorithms for use on data frames.
Spark at General Assembly
At GA, we teach both the concepts and the tools of data science. Because hiring managers from marketing, technology, and biotech companies, as well as guest speakers like company founders and entrepreneurs, regularly talk about using Spark, we’ve incorporated it into the curriculum to ensure students are fluent in the field’s most relevant skills. I teach Spark as part of our Data Science Immersive (DSI) course in Boston, and I previously taught two Spark courses for Cloudera and IBM. Spark is a great tool to teach because the general curriculum focuses mostly on Python, and Spark has a Python API/library called PySpark.
When we teach Spark in DSI, we cover resilient distributed data sets, directed acyclic graphs, closures, lazy execution, and reading JavaScript Object Notation (JSON), a common big data file format.
Joseph Kambourakis has over 10 years of teaching experience and over five years of experience teaching data science and analytics. He has taught in more than a dozen countries and has been featured in Japanese and Saudi Arabian press. He holds a bachelor’s degree in electrical and computer engineering from Worcester Polytechnic Institute and an MBA with a focus in analytics from Bentley University. He is a passionate Arsenal FC supporter and competitive Magic: The Gathering player. He currently lives with his wife and daughter in Needham, Massachusetts.
“GA students come to class motivated to learn. Throughout the Data Science Immersive course, I keep them on their path by being patient and setting up ideas in a simple way, then letting them learn from hands-on lab work.”
Joseph Kambourakis, Data Science Instructor, General Assembly Boston
