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

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

Posted by Vivian Zhang

Updated: Apr 27, 2021

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.

80 Tools and Skills Needed to Succeed in Data Science

Technical Skills for Entry-Level Jobs

Skills for Long-Term Career Success

Tools and Related Skills

Full Stack Data Science Skills Categories

Extra Data Science Skills to Stand Out

Soft Skills and Traits of Good Data Scientists

Python programming and packages

SQL databases for business operations

Apache Spark

Business operations research

Cloud architecture

Realistic skepticism

Data analysis in R and Python

Deep Learning use cases

SAS

Data sourcing

DevOps

Data storytelling and teaching

Statistics and probability theory

Database management

Docker

Data analysis

Project management

Human behavior and psychology

R programming proficiency

Deep Learning with TensorFlow

Microsoft BI

Machine learning algorithms

Performance marketing

Meeting people where they’re at

Machine learning theories and algorithms in R and Python

Python Scikit-Learn and other modules

Hadoop

Modeling analysis

Leadership experience

Communication charisma

Big Data for business operations

Self-awareness of your legacy

Matplotlib

Visualizing and communicating

Sales and/or customer service

Patience for problem solving

Basics of Hadoop, Spark, Hive, and TensorFlow

Natural Language Processing and neural networks

GitHub

Model development

CI/CD

Knowing when to quit

Creating data visualizations

Advanced machine learning algorithms and analysis

HIVE

Monitoring and output

Risk management

Modesty with respect to the unknown

Programming tools like iPython and R Shiny

Comfort with uncertainty and failure

Tableau

 

5G and telecoms

Comfortable with imperfection

SQL databases for business operations

Commitment to a single industry

Scikit-Learn

 

Industry Big Data specialization

Work granularly with team communication

Deep Learning use cases

Department-specific skills

MATLAB

 

Martech

Adapting into broader project goals

Database management

Flexible communication styles

R Shiny and Kintr

 

Microservices

Open mind, always ready to learn

 

 

MySQL

 

Distributed systems

 

 

 

TensorFlow

 

Business management

 

 

 

iPython

 

User Experience (UX)

 

 

 

AWS

 

Healthcare research and analysis

 

 

 

 

 

Enterprise software

 

 

 

 

 

Engineering in machine learning and deep learning

 

 

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 Technical Skills Required for Entry-Level Data Scientist Jobs

1.     Python programming and packages

2.     R programming proficiency

3.     Basics of Hadoop, Spark, Hive, and TensorFlow

4.     SQL databases for business operations

5.     Data analysis in R and Python

6.     Machine learning theories and algorithms in R and Python

7.     Creating data visualizations

8.     Deep Learning use cases

9.     Statistics and probability theory

10.  Big Data for business operations

11.  Programming tools like iPython and R Shiny

12.  Database management

 

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.

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

1.     Extra programming languages

2.     Deep Learning with TensorFlow

3.     Natural Language Processing and neural networks

4.     Commitment to a single industry

5.     Big Data ecosystem

6.     Python Scikit-Learn and other modules

7.     Advanced machine learning algorithms and analysis

8.     Department-specific skills

9.     Replacing perfection with patience

10.  Self-awareness of your legacy

11.  Comfort with uncertainty and failure

12.  Flexible communication styles

 

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.

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

1.     Apache Spark

2.     Hadoop

3.     Tableau

4.     MySQL

5.     SAS

6.     Matplotlib

7.     Scikit-Learn

8.     TensorFlow

9.     Docker

10.  GitHub

11.  MATLAB

12.  iPython

13.  Microsoft BI

14.  HIVE

15.  R Shiny and Kintr

16.  AWS

 

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.

8 Common Full Stack Data Scientist Skills Categories

Business Operations Research

Data Sourcing

Data Analysis

Machine Learning Models

Modeling

Analysis

Visualization and Communication

Model Deployment

Monitoring and Output

 

 

 

 

 

 

 

 

Identifying a specific business problem data science can solve with automation, like risk management or inefficiencies

Figuring out where to source the project’s raw data and how, collecting it using machine learning models, and preparing it for modeling

Exploring what the raw data means and how it relates to the business problem using analysis and visualizations

Using the prepared data to train machine learning algorithms and models to solve the main problem

Investigate results of the machine learning model’s tests for behavior insights, adjust the model, repeat the process

Transforming the final model analysis into digestible visualizations for communicating the risk/reward to stakeholders

Prepare final model pipelines for deployment and send to production for end-user development

Decide what factors and metrics will determine the live model’s success for solving the business problem, and continue analyzing and improving with new data

 

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.

 

20 Smart Data Scientist Skills and Responsibilities to Stand Out

1.     Cloud architecture

2.     Sales and/or customer service

3.     Martech

4.     Business management

5.     DevOps

6.     CI/CD

7.     Microservices

8.     User Experience (UX)

9.     Project management

10.  Risk management

11.  Supply chain logistics

12.  Healthcare research and analysis

13.  Performance marketing

14.  5G and telecoms

15.  Distributed systems

16.  Enterprise software

17.  Leadership experience

18.  Machine learning in finance

19.  Industry Big Data specialization

20.  Engineering in machine learning and deep learning

 

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.

Important Soft Skills and Naturally Good Traits of Data Scientists

1.     Realistic skepticism

2.     Communication charisma

3.     Comfortable with imperfection

4.     Data storytelling and teaching

5.     Patience for problem solving

6.     Work granularly with team communication

7.     Human behavior and psychology

8.     Knowing when to quit

9.     Adapting into broader project goals

10.  Meeting people where they’re at

11.  Modesty with respect to the unknown

12.  Open mind, always ready to learn

 

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.

Are you ready to build the skills needed for data science entry-level jobs? See what you’ll learn in just 12 weeks with NYC Data Science Bootcamp.

Get a copy of the NYC Data Science Academy Bootcamp Syllabus!

Take the first step in finding out if a Data Science Career is for you.

Vivian Zhang

I founded and run NYC Data Science Academy and SupStat Analytics Inc. I was ranked as one of “9 Women Leading The Pack In Data Analytics" by Forbes in Aug 2016 and “top 50 data scientists in China” in Sept 2019. I enjoy meeting people and enjoy sharing experiences with young professionals and students. I am a data scientist who has many years of practical experience in data technologies and the analytics industry, after developing my expertise in statistical methodologies and software and in a variety of programming languages such as R, Python, Hadoop, Spark and etc.

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Topics from this blog: Data Scientist Jobs Skills Needed Full Stack Data Scientist

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