How to Become a Data Scientist

Nearly everything you do generates data. Visit a website: Data. Tap an app on your phone: Data. Buy something with a credit card: Data. Like or upload a picture on social media: Data. Billions of people are generating immense amounts of data every single moment of every single day.

That’s some big data, and it’s only getting bigger. Imagine what can be done with all that information–data scientists are doing exactly that. Data science is essentially the art of solving problems with data. You might have trillions of rows of data, but on its own that information means nothing. It takes work and specialized skills to transform it from unintelligible noise into something that can be easily understood.

Woven into all this data is information that can improve quality of life, identify societal issues, and address global crises. Now more than ever the significant advancements that can result from data are essential to find–it’s no surprise that being able to understand, analyze, and interpret data is a highly desirable skill.

What do Data Scientists actually do?

Let’s get started by breaking down two of the most commonly asked questions––what is data science, and what are the responsibilities of a data scientist?

Data science is all about diving into a well of information and shaping it into a tool that you can use to accomplish a goal. Data scientists process data so it’s human-readable, building visualizations that tell a story or models that explain a process or predict behavior. Other times experiments are run to validate hypotheses in an attempt to prove them. The essence is that the raw data is used to output something that is valuable in that you can do or learn something with it.

Data Science job titles include:

  • Data Scientist
  • Data Analyst
  • Business Intelligence Analyst
  • Machine Learning Engineer
  • Junior Data Analyst

What skills do Data Scientists need?

This rapidly expanding field is tackling some of the biggest problems in the world today. But what does it take to actually be a data scientist?

Before you even begin learning the technical skills to get you into the industry, focus on the soft skills you likely already possess. These are integral to landing your next career as a data scientist:

  • Communication
  • Creative Thinking
  • Relationship Building
  • Authenticity
  • Persistence

Technical skills that are essential to get the job done, perform at a high-level, and meet career goals include:

  • Advanced programming and deep mathematical knowledge
  • Passion for finding and solving problems
  • Analytical techniques like how to make visualizations and use summary statistics
  • Understanding of A/B testing and statistical significance
  • Python to gather and present data, then identify insights
  • SQL for querying
  • Machine learning with supervised and unsupervised models

Data science is rarely cut and dry. It isn’t simply “apply this technique” or “run this program”. While necessary, that’s usually the easy part. You need a thorough understanding of the problem so that you can determine which tools are best suited to your task. One of the most important skills for a data scientist is the ability to find solvable problems. Learning data science, then, is not merely combining programming with statistics — it includes that, but also requires context. You need to understand the domain that you’re working in, so you can test your hypotheses in the real world.

How do I become a Data Scientist?

Learning anything requires a positive feedback loop. In designing our bootcamp courses at Niminq, we’ve found that students learn best with:

  • 1-on-1 mentorship and career coaching
  • A comprehensive curriculum with built-in check-ins
  • Capstone projects that build a real-world portfolio

We offer a flexible program data science course to allow you to choose the best format for your life. Our state-of-the-art curriculum will teach you all the skills you need to launch a successful data scientist career. Some of the highlights from our data science curriculum include:

  • Analytics and Experimentation using Python and SQL
  • Machine learning using supervised and unsupervised models
  • Advanced specialization skills

We’ve built our programs to fit your needs and set you up for success. All courses are delivered 100% online and include advanced project-based curriculums and current industry tools to build real-world capstone projects. 

Getting Started with Data Science Specialties


I frequently ask young people, particularly undergraduates, what they plan to do with their future. I am often less than enthused with the responses which sound something like this:

  • I hope to get a job doing statistics.
  • I just want to work with computers.
  • I want to be a data scientist.
  • I just want a job.

The responses are typically vague and void of direction. Most responses involve waiting for someone else to provide the guidance. You do not have to wait. You can get started today.

If you are just interested in getting a job, the rest of this post is not for you. If you want to make an impact with your data science career, the remainder of this post is for you.

Below is an explanation of numerous specialties in data science. You don’t need to learn them all. Just pick one and follow the first step. You will learn more along the way. Don’t stress about which one to pick, there is no wrong answer. Just pick one and start building.

Data Visualization

Data visualization is all about telling a story with data. Do you have a keen eye for color and design? Can you summarize complex data in a few simple charts? If you answer yes to those questions, then you just might be a good fit for data visualization.

First Step: Go to and make an infographic

Data Science Educator

Are you the person always explaining your homework to others? This specialty might be for you. You can take a few different paths. One is the traditional university faculty approach. Another is more of a corporate training professional. The world needs both. Plus, if you are entrepreneurial, there are ample opportunities to consult as a data science educator. Businesses realize they need to know data science, and they are looking for training.

First Step: Start a video or blog with tutorials

Data Engineer

data engineer is typically more interested in systems than just the machine learning. Data engineers are typically strong with computer science fundamentals. They love to build things that themselves and others can use. A good data engineer can also spend a lot of time cleaning data as well.

First Step: Build a solution (hint: Cortana Intelligence Solutions)

Data Programmer

Do you love to program? If so, you just might fall into this category. Data science has many needs for programmers. Everything from cleaning data to building data products needs programming.

First Step: Be on Github

Statistical Modeling (Machine Learning)

Some people just love the statistical modeling and machine learning. They love to tune models and squeeze the last bit of predictive power from a data set. If you love talking about regression, trees, random forests, AUC, cross-validation and boosting; then this specialty is most likely for you.

First Step: Enter Kaggle competitions.

Data Science Manager

If you are bossy, it does not mean you will make a good manager. The best managers know how to build strong teams and get out of the way. Managers will provide help and overall direction for projects. Plus, he/she should have a solid understanding of how data can help shape a team’s decisions.

First Step: Organize a group to help a non-profit analyze data (Similar to what DataKind does)

Data Science Researcher

A researcher is interested in pushing the boundaries of data science. Are you interested in creating your own machine learning algorithms? Do you want to build the next great data framework? Do you think data science can achieve something no one else has thought to try? If so, being a researcher is for you.

First Step: Go to graduate school

Data Science Unicorn

A data science unicorn is someone that knows all the specialties above and more. A unicorn understands all the topics of data science. Being a unicorn is not attainable for everyone, but a few people have become unicorns. If you think you can be a unicorn, go for it.

First Step: Start at visualization above

In Conclusion,

Simple: Pick a specialty and Go Make a Difference!