Getting Your First Job in Data Science

Getting your first data science job might be challenging, but it’s possible to achieve this goal with the right resources.

Before jumping into a data science career, there are a few questions you should be able to answer:

  • How do you break into the profession?
  • What skills do you need to become a data scientist?
  • Where are the best data science jobs?

First, it’s important to understand what data science is. To do data science, you have to be able to process large datasets and utilize programming, math, and technical communication skills. You also need to have a sense of intellectual curiosity to understand the world through data. To help complete the picture around data science, let’s dive into the different roles within data science.

The Different Data Science Roles

Data science teams come together to solve some of the hardest data problems an organization might face. Each individual of the team will have a different part of the skill set required to complete a project from end to end.

Data Scientists

Data scientists are the bridge between programming and algorithmic thinking. A data scientist can run a project from end-to-end. They can clean large amounts of data, explore data sets to find trends, build predictive models, and create a story around their findings.

Data Analysts

Data analysts sift through data and provide helpful reports and visualizations. You can think of this role as the first step on the way to a job as a data scientist or as a career path in of itself.

Data Engineers

Data engineers typically handle large amounts of data and lay the groundwork for data scientists to do their jobs effectively. They are responsible for managing database systems, scaling data architecture to multiple servers, and writing complex queries to sift through the data.

The Data Science Process

Now that you have a general understanding of the different roles within data science, you might be asking yourself “what do data scientists actually do?

Data scientists can appear to be wizards who pull out their crystal balls (MacBook Pros), chant a bunch of mumbo-jumbo (machine learning, random forests, deep networks, Bayesian posteriors) and produce amazingly detailed predictions of what the future will hold.

Data science isn’t magic mumbo-jumbo though, and the more precise we get about to clarify this, the better. The power of data science comes from a deep understanding of statistics,algorithms, programming, and communication skills. More importantly, data science is about applying these  skill sets in a disciplined and systematic manner. We apply these skill sets via the data science process. Let’s look at the data science process broken down into 6 steps.

Step 1: Frame the problem

Before you can start solving a problem, you need to ask the right questions so you can frame the problem.

Step 2: Collect the raw data needed for your problem

Now, you should think through what raw data you need to solve your problem and find ways to get that data.

Step 3: Process the data for analysis

After you collect the data, you’ll need to begin processing it and checking for common errors that could corrupt your analysis.

Step 4: Explore the data

Once you have finished cleaning your data, you can start looking into it to find useful patterns.

Step 5: Perform in-depth analysis

Now, you will be applying your statistical, mathematical and technological knowledge to find every insight you can in the data.

Step 6: Communicate the results of the analysis

The last step in the data science process is presenting your insights in an elegant manner. Make sure your audience knows exactly what you found.

If you worked as a data scientist, you would apply this process to your work every day.

What’s next?

Before you jump into data science and working through the data science process, there are some things you need to learn to become a data scientist.

Most data scientists use a combination of skills every day. Among the skills necessary to become a data scientist include an analytical mindset, mathematics, data visualization, and business knowledge, just to name a few.

In addition to having the skills, you’ll need to then learn how to use the modern data science tools. Hadoop, SQL, Python, R, Excel are some of the tools you’ll need to be familiar using. Each tool plays a different role in the data science process.

If you’re ready to learn more about data science, take a deeper look at the skills necessary to become a data scientist, and how to get a job in data science, download Springboard’s comprehensive 60-page guide on How to get your first job in data science.


How to get a Data Science Job

About Springboard: At Springboard, we’re building an educational experience that empowers our students to thrive in technology careers. Through our online workshops, we have prepared thousands of people for careers in data science.

Data Science Papers – Summer 2019 edition

Looking for a few academic data science papers to study? Here are a few I have found interesting. The are not all from the past 12 months, but I am including them anyhow.

My Experience Taking Microsoft DP-100: Designing and Implementing a Data Science Solution on Azure

I took and passed DP-100 during the beta period. I recorded a live video talking about my experience. Below is that section of the live video. Also, here are the main topics:

  • Azure ML Studio
  • Machine Learning
  • Python
  • High-level knowledge of Azure Products

Also, if you want a checklist to prepare for the exam, I have created one, it is free.

Data Science News for May 2019

Here is the latest data science news for May 2019.

From Data Science 101

General Data Science

What is on the Microsoft Data Science Certification Exam?

I took and passed the exam during the beta period. These are my memories of the topics on the exam. You can get this information as the Microsoft Azure Data Scientist Checklist.

General Overview

Below is the basic structure of the DP-100: Designing and Implementing a Data Science Solution on Azure. Passing the exam will qualify you for the Azure Data Scientist Associate certification. It can be taken in a traditional exam center or at your home (but you will be watched via video camera).

  • 180 minutes
  • 60 questions (45 Q&A, 15 about Case Studies)

The exam can be broken down into 4 components: Machine Learning, Azure ML Studio, Azure Products, and Python. Below is a breakdown of the topics I remember from the exam.

Machine Learning

These are topics which would be covered in a traditional machine learning course. Here are some of the specific topics I remember.

  • Evaluation of Linear Regression
  • Evaluation of Classification
  • Positive/negative skew
  • Poisson regression
  • Fisher’s exact test
  • Pearson
  • PCA
  • Deep learning – high-level, what is is for
  • Neural Networks (RNN vs CNN vs DCN vs GAN)

Azure ML Studio

Azure ML Studio is a major focus of the exam, so you need to be fluent in how to use it. Questions ranged from the basics of how to import data all the way to specifics about certain modules.

  • Model Building
  • Model Evalutation
  • SMOTE
  • MICE
  • feature extraction
  • missing data questions
  • AutoML

Azure Products

There were a number of questions from this category. The question would present you a scenario problem and ask which products would be useful for solving the problem. The questions did not go very deep into any of the products, but you will need to know the purpose of these products.

  • Azure Machine Learning Service
  • Blob storage – specifically how to get data in/out
  • Azure Notebooks
  • Azure Cognitive Services (high level)
  • Kubernetes
  • HDInsight
  • Data Science Virtual Machine

Python

Python was the language of choice for the exam, so focus on it.

  • Scikit-learn
  • Azure Machine Learning SDK for Python
  • Hyperparameters

Not on the exam

The following topics were not covered on my exam. The exam questions are pulled from a pool of questions, so it is possible these topics may be cover on a different person’s exam. In any case, these are definitely not major portions of the exam.

  • R
  • Power BI
  • Publishing Azure ML models

Again, if you want this information in an easy to follow checklist, just visit, Microsoft Azure Data Scientist Checklist.

Real Talk with A Data Scientist: The Future of Data Wrangling

Sponsored Post by T.J. DeGroat of Springboard

At Springboard, we recently sat down with Michael Beaumier, a data scientist at Google, to discuss his transition into the field, what the interview process is like, the future of data wrangling, and the advice he has for aspiring data professionals.

The full video Q&A is below, but here are some of the highlights.

You started off with a Ph.D. in physics and now you’re a data scientist. How did that transition happen?

I think in physics one of the things that attracted me most to the field that I studied, which was particle physics, was the ability to leverage computer science mathematical modeling and data visualization to solve big questions. The longer I was in that field, the more I realized that that process of problem-solving with those techniques and tools, I actually enjoyed that more than the physics itself. So it was a natural transition for me to move to a career that felt like it had higher impact, a wide variety of work, and good career opportunity—and data science was that was that move for me.

Before Google, you worked at Mercedes. What were some of the projects that you worked on there?

The big problem that we were solving at Mercedes is basically: how do you build machine learning into an environment where there’s limited-to-no data connectivity? One of those pieces was: how do you do that in a car interface with a very low-power computer like a Raspberry Pi and basically create an interface that can customize itself to the users’ needs based on context? This was released as MBUX in 2017 and that was a pretty cool project because there were a lot of challenges that you wouldn’t normally have to solve if you had access to a massive data set or connectivity.

We pulled questions from some of our Springboard students and this one is from Miguel, who is in our data science vertical. He asked, “Do you have a process for thinking about the data before you start implementing the tools?”

That’s a great question and I think I would add another piece to that, which is that it kind of depends on where you’re at. The Mercedes answer would be that you have to build the tools, and usually the way that you approach that is you think about the data you have and what kind of features you have. And specifically, where is the information hiding that sort of links your input to output? And what kind of transformation do you need to apply to the data set to enable modeling? That’s what I would say.

The key is to think about—do all your work up front, I would say, because models are kind of a dime a dozen, with scikit-learn you can just like, it’s the same API so you can just model.fit a thousand times and then combine the outputs and then do a model on top of that. But all the work now, I think, is: how do you think about engineering features and building the model into a pipeline?

Another one of our data science students wants to know a little bit more about your interview process at Google. What was that like and how did you search for your job?

I actually interviewed at Google twice. The first time I failed utterly and the second time I got through. There are many paths into interviewing at Google and I think a pretty common one, if you don’t know anyone at Google, is that a recruiter reaches out to you—once you have a job and that’s all posted on LinkedIn and your LinkedIn profile looks pretty good, you’re gonna get contacted by Facebook, Amazon, Google, Netflix. Like, they’re gonna talk to you. So when you take the recruiter intake process, usually what they will do is they’ll kind of hold your hand through the whole process. The first piece is a technical screen. If the recruiter feels like that’s unnecessary, they may move you directly to an onsite.

I’ve interviewed with a lot of companies and one of the things that I always do is I just ask the recruiter: what are the metrics that you will be evaluating me on in these interviews and what topics will be covered? I think some people feel like that’s off-limits, but, you know, this isn’t school, you’re allowed to ask what’s on the test before you get the test, so I encourage people to do that. it’s not cheating.

I think a lot of people, when they interview, they get really fixated on “I got to get to the right answer” and maybe freeze because they get really nervous. And these are totally natural reactions. I would say with the interview process, if you think of it more of a performance, you’ll have a much better time, you’ll probably enjoy it more, and I think the outcome will be better.

We also have a question from Guy, who is one of our community managers. He wanted to know, “How do you develop a knack for data wrangling? Do you have a process for that?”

Honestly, I think that data wrangling is going to be one of the things that is replaced by modeling. There’s a whole field called data learning, where basically a model tries to learn—you have a data set and it may be super messy and you just throw that into a data learning model and the model figures out what’s important. OK, but that’s not the question. The question is: how do you develop an intuition for data?

So, it’s tricky because sometimes it, I would say it depends on the size of the data set. When the data set becomes very large in terms of the number of observations or features… you know, I work on projects that regularly have tens of thousands of features and so that just can’t fit into your head as a human being, so I think the only way in that space that you can develop an intuition for data is actually from a modeling perspective. So, you try models, you understand what over and underfitting is, and how to tune your model…

In the case where you have a smaller number of features, I think the best way to build an intuition for that is to just try it. There’s this data set that I send out to people that’s from the UCI Machine Learning Repository. It’s the Auto MPG Data Set and I love it because it has a mix of categorical features. It has numeric features that are really actually categorical features and then the goal is: can you model the miles per gallon of these cars? It’s so rich and it’s so small and there’s only like 300 observations and like eight features, but you can do a lot with it. And so I think the crucial piece to developing intuition is to practice. And if you’re not practicing, you can’t develop that intuition.

Just to kind of like put the nail in that, you need to be comfortable with handling categorical features, you need to understand how to turn numerical features into categorical features, you need to understand how to treat numerical features, you need to understand what normalization is and what models are robust to feature normalization and what models it doesn’t matter for. And there’s no easy way, I would just say: try it. And when people ask me, like, help me prepare for data science interviews, I just tell them to do data challenges over and over again and then come talk to me and tell me how you feel about the static data challenges.

We have one last question from a student, Cory. He asks, “How important is SQL in comparison to Python in 2019?”

That’s an interesting question because I think people love putting SQL on this mega pedestal as like a core thing that all data scientists have to use. My opinion on it is that if your data set fits into memory, it’s like it literally doesn’t matter what you do because computers are fast and human brains are slow and you’re not going to notice the difference between doing something in a pandas dataframe versus an R data frame versus SQL.

However, you have to know SQL. Like, the universe has decided that in data science interviews, you must know SQL. So, the question is: what’s important in 2019? I would say if your data set doesn’t fit into memory, then you have to use SQL and if it does, it doesn’t matter—and there’s like a million caveats to that statement.

This Q&A has been lightly edited and condensed for clarity.

Ready to start (or grow) your data science career? Check out Springboard’s Data Science Career Track—you’ll learn the skills and get the personalized guidance you need to land the job you want.

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