80+ Tools and Skills Needed to Succeed in Data Science Jobs

Imagine the last time you walked into a corner store and saw a glass jar filled with bright candy on the counter. Guess correctly. Win a prize.

Now, let’s try some data science guessing games.

How many times each day would you say that an average internet user interacts with a single piece of digital data or line of code? Even if you successfully tracked down each piece, the number would be unfathomable.  

In fact, global internet users create more than 2.5 quintillion bytes of data each day, and over 1.7MB per person every second.

As long as humans have internet access and connected devices to create more data, the world will always need more skilled data scientists.

Look no further than data’s infinite growth, and it’s easy to see why the Bureau of Labor Statistics expects operations research roles like data science to grow 25 percent over the next decade.

Physical servers, shared clouds, VPSs, caches, access points – data is quite literally everywhere, even among this brief list of storage solutions. Every tap, swipe, exit, click, bookmark, ha-ha, and thousands of other touchpoints create more data.

And more. And more.

What Skills Do You Need to Enter Data Science?

What skills do data scientists need, both for entry-level jobs and lifelong careers in the field?

The basic skills needed to become a data scientist are straightforward enough for everyone to learn individually, but applying the skills as data science demands is more nuanced.

Data scientists live on the cusp of both data analysis and computer science programming. They help businesses, government agencies, schools, and other organizations learn about the people behind the data. Before learning how to blend data with science, you’ll need expert knowledge in each field separately.

That’s why 79 percent of today’s data scientists start their journey with graduate degrees in fields such as:

• Computer science
• Statistics
• Economics
• Mathematics
• Data research
• Engineering

Bachelor’s degree holders shouldn’t disqualify themselves yet: 21 percent of data scientists hold undergraduate degrees. Computer science, software engineering, or information technology put you in an ideal position to build the skills needed for data science.

Note that most degree programs alone won’t prepare you for the other half of skills needed to enter data science: Python and R programming.

NYC Data Science Bootcamp covers the relevant Python and R processes for data analysis, visualizations, and machine learning, but you need a solid background in both languages before enrolling. Check the NYC Data Science Bootcamp Prep to see where you stand.

CHART: Top 80 Qualities, Tools, and Skills Data Scientists Need for a Lifelong Career

It probably won’t surprise you to learn that skills needed to build a data science career are equally expansive and vast, as you’ll see below.

Do you need to master every one of these skills while learning how to become a data scientist? Yes and no. The first column of entry-level data science skills are all must-haves before you consider applying for jobs.

The others, however, you can develop in an immersive bootcamp and/or explore in-depth later, as your knowledge of tools and skills grow throughout your career.

Although you won’t need the hands-on experience to apply some advanced skills at the entry level, you should at least grasp each skill’s general techniques and applications, so you can decide when it’s most worthwhile to learn them.

12 Technical Skills Required for Entry-Level Data Scientist Jobs and Internships

Every field has its deal-breaker skills. Without these technical skills needed to become a data scientist, you won’t be able to perform basic job duties and manipulate data.

Most of the technical skills needed by entry-level data scientists begin with Python, R programming, and SQL for finding data and eventually transforming it into graphs, charts, and other visualizations. Python packages like numpy, scipy, pandas, matplotlib, and seaborn are must-knows.

Data scientists also need skills in machine learning algorithms, linear regressions, and other models for both Python and R languages. Later, a data scientist learns the basics of Deep Learning applications and techniques like Natural Language Processing in TensorFlow.

Also, toward the end of a student’s NYC Data Science Bootcamp training, they’ll dabble in Big Data concepts along with the Apache Hadoop, Spark, and Hive ecosystem.

12 Skills Needed for Long-Term Successful Careers in Data Science

Your next technical skill jumps will happen in Python, with Scikit-Learn for implementing advanced machine learning regressions, Support Vector Machines, and Principal Component Analysis.

Big Data with AWS, Apache Hadoop/Spark, and Docker will also follow your beginner/intermediate NYC Data Science Bootcamp work.

Even as a bootcamp student, it’s worth exploring specific industries to plant your roots so you can simultaneously grow the specific high-level business operations knowledge required for data science promotions.

Look beyond your data science resume skills, too: How do you see yourself impacting data science research and society? Many modern data scientists lead nonprofits or teach.

16 Vital Tools and Related Skills Data Scientists Need for Entry-Level Jobs

Data scientists are nothing without the right systems, platforms, and tools for turning raw data into understandable insights.

Most start with the skills needed for data science in Tableau, R Shiny, iPython and MySQL. TensorFlow and Scikit-Learn both come later, for more advanced machine learning regressions and techniques.

What’s a Full Stack Data Scientist? 8 Skill Categories from Start to Finish

Full stack data scientists handle the entire lifecycle of data-driven insights – from discovering a business problem to testing and optimizing solutions. This chart covers the basic full stack data scientist skills broadly.

20 Extra Data Scientist Skills and Responsibilities to Stand Out

Just think about all the ways you interact with data on a daily basis. Each industry and specialization offers a unique path to carve your own niche and play a role in tomorrow’s top skills needed for data science.

12 Soft Skills and Naturally Good Traits of a Data Scientist

It’s not always about the most advanced tools and skills needed for data science. Sometimes, data science demands surprising charisma and other natural traits.

How to Showcase Data Science Skills on Your Resume

NYC Data Science Bootcamp students and alumni all receive resume reviews from relevant experts and mentors for every job, as well as LinkedIn profile guidance and more.

• Structure your resume for algorithms and humans with keywords, subheadings, and the most relevant info at the top.
• Bulleted lists with just a single skill on each line, based on the description, will ensure your resume makes it past the bot round and remains an easy read for humans.
• Hypothesize, analyze, and test your own data science resume details by Googling your name, ensuring all the data matches.
• Only mention what matters to the role or your highest relevant skill level – do keep basics like MySQL in your bulleted skill list, though.

How to Prove You Have the Skills Required for a Data Scientist Job in Interviews

Thanks to data’s omnipresence, prospective data scientists have an entire world of opportunities at their fingertips. NYC Data Science Bootcamp offers comprehensive career services, so you’ll walk into job interviews and coding challenges confident in your skills.

• Choose mock coding challenges based on your realistic dream job roles
• Investigate niche industry tools and skills needed for data science
• Join a professional data scientist network for insightful mock interviews
• Look for live project experience at relevant companies for filling skill gaps

What’s the Fastest Way to Earn the Skills Needed for Data Science?

A data science bootcamp is one of the fastest ways to earn the knowledge and experience you need for starting an entry-level career in data scientist positions at leading companies. NYC Data Science Bootcamp’s immersive program prepares students with the skills needed to enter data science in 12 weeks.

Data Science Bootcamp covers extensive programming, machine learning, and data analysis material, putting you through hands-on projects to build relevant experience for your portfolio, and much more. That’s why NYC Data Science Bootcamp has been consistently ranked by Switchup as one of the best bootcamps for five years running.

NYC Data Science Bootcamp boasts a 2,000-strong alumni community of working data scientists and professionals. These active data science experts at top companies around the world help shape the Bootcamp curriculum and projects.

Every capstone project is specifically designed to mimic current business problems, so you can hone the right Python, machine learning, and R skills for finding solutions at entry-level jobs.

NYC Data Science Bootcamp also partners with over 500 companies to offer opportunities like live project help, internships, expert resume advice, and potential job placements for qualified data scientists.

Apply for the Upcoming NYC Data Science Bootcamp

The first step in becoming a data scientist is to complete your Data Science Bootcamp Application.  Just click the button to apply.  It's free and will only take you about 5 minutes.