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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 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:
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.
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 |
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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 |
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5G and telecoms |
Comfortable with imperfection |
SQL databases for business operations |
Commitment to a single industry |
Scikit-Learn |
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Industry Big Data specialization |
Work granularly with team communication |
Deep Learning use cases |
Department-specific skills |
MATLAB |
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Martech |
Adapting into broader project goals |
Database management |
Flexible communication styles |
R Shiny and Kintr |
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Microservices |
Open mind, always ready to learn |
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MySQL |
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Distributed systems |
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TensorFlow |
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Business management |
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iPython |
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User Experience (UX) |
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AWS |
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Healthcare research and analysis |
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Enterprise software |
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Engineering in machine learning and deep learning |
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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.
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 |
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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 |
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 |
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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 |
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 |
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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 |
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 |
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Business Operations Research |
Data Sourcing |
Data Analysis |
Machine Learning Models |
Modeling Analysis |
Visualization and Communication |
Model Deployment |
Monitoring and Output |
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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 |
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 |
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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 |
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 |
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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 |
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.
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.
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.
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.
Take the first step in finding out if a Data Science Career is for you.
Vivian is the CTO and School Director of NYC Data Science Academy and CTO of SupStat. With her extensive experience working in the data science field, she developed expertise in multiple programming languages, including R, Python, Hadoop, and Spark. In August 2016, Forbes ranked her amongst one of the nine women leading the pack in data analytics. In 2013, she created the NYC Open Data Meetup group, which stands as one of the largest data science communities offering meetups, conferences, and a weekly newsletter. In her spare time, Vivian enjoys meeting people and sharing her motivational stories with our students and other professionals
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