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Top 5 Tech Skills for Data Scientists

More companies are edging beyond their comfort zone, unlocking the power of data to push the boundaries of technology.


As data science takes the driver’s seat to answer today’s biggest challenges, major industries demand data scientists skilled at wrangling mountains of raw, unstructured data.


In fact, data scientist job postings have increased by 256% since December 2013. And in today’s market, the role falls in a list of top paying AI-related jobs that can expect to earn an average salary of $130,000.


With so many companies adopting a data-driven approach, data scientists should keep their skills current—most importantly, staying sharp with the skills employers specifically want. Based on the most asked-for skills in Indeed job postings, we’ve put together the five tech skills employers look for most in data scientists.


Keep reading to discover what skills are in highest demand, plus how you can develop them to increase your marketability and create a stand-out data scientist resume.


1) Machine Learning


A cornerstone of artificial intelligence, machine learning analyzes and learns from data to make predictions and decisions without being programmed to do so. The ability to learn from patterns and past experiences with minimal human intervention makes machine learning key for breakthrough technological advancements—think self-driving cars, cancer detection and eerily accurate personalized shopping recommendations.


Because machine learning combines data science, math and software engineering, it requires an extensive skill set. According to Udacity, machine learning skills include:


  • Computer science fundamentals and programming
  • Probability and statistics
  • Data modeling and evaluation
  • Machine learning algorithms and libraries
  • Software engineering and system design


Want to build your machine learning skills? Head over to Kaggle, where a community of data scientists and machine learners come together to publish data sets, build models and participate in friendly competition to solve data science problems. (Before you go: Know that there is some debate among data scientists about how well Kaggle translates to real-world production work, but it’s a great place to start.)


2) Python


Python is a general-purpose, object-oriented programming language that runs on most operating systems. Along with its flexible nature, Python uses English keywords, simple syntax and fewer lines of code than other languages like C++ or Java—major contributors in its readability and gentle learning curve. And with its easy accessibility, Python’s popularity continues to climb as one of the fastest-growing programming languages in the world.


Beyond web development and automation, Python is celebrated as a powerful data and visualization tool. Its rich set of libraries include a number specific to machine learning, including NumPy, SciPy, scikit-learn and Pandas.


Want to build your Python skills? On top of practicing Python at home with resources like PyBites, spend time reading one (or many) of Real Python’s online tutorials.


3) R


R is a statistical software package designed to simplify the analysis of large data sets with features like linear and non-linear modeling, clustering and time-series analysis. Although R has a steeper learning curve than other computer programming languages, it continues to climb in popularity with an active community passionate about swapping ideas and developing new packages.


And it’s easy to see why this open-source scripting language is a top choice in the data science community—R shines in statistical manipulation and graphical representation. Powered by R, data scientists can perform statistical and predictive analysis on real-time data, then turn around to create compelling visualizations that communicate results to stakeholders.


Want to build your R skills? Filled with easy-to-read insights and exercises covering R concepts and commands, R for Data Science is a smart addition to the data scientist’s library. Can’t wait to get your hands on a physical copy? Read it online now.


4) SQL


SQL makes retrieving data possible. It communicates with databases, giving data scientists a way to access and manipulate large volumes of data found in a relational database management system. In addition to capturing and breaking down data, SQL commands can edit database table and index structures to help keep information accurate.


Moving beyond the basic functions of SQL, data scientists comfortable with concepts like aggregation and filtering can produce targeted, summarized data with dramatically reduced rendering times. And because data is at the heart of impactful business decisions, SQL is (not surprisingly) fundamental in the data science realm and a key language for any data scientist resume.


Want to build your SQL skills? What’s free, interactive and lets users test and share SQL queries in their browser? SQL Fiddle. Use this online tool to hone your skills, from running experiments and changing database systems to solving database problems and challenges.


5) Hadoop


Hadoop is a leader in big data technologies, capable of handling large amounts of structured and unstructured data (whereas SQL excels in managing low volume, structured data). It’s a software framework that stores and quickly processes massive amounts of data across clusters of computing devices. Flexible, scalable and fault-tolerant, this highly efficient data-processing tool helps companies pinpoint trends and predict outcomes to make better decisions.


Landing a data scientist job with limited Hadoop experience may be possible. However, a solid understanding of the framework is a strong selling point that can lead to more opportunities and help negotiate a higher salary.


Want to build your Hadoop skills? Cognitive Class offers free courses specifically to grow your data science skills, including a number of Hadoop fundamentals and programming courses based on your skill level. Each course you complete gets you a badge for your portfolio and one step further in your data science career.

Virtually every industry is collecting data with the intent to drive value and growth. And it’s clear that data scientists with the right mix of skills can best interpret this information. So whether you’re breaking into the data science field or giving your tech skills a boost, it’s a good idea to tailor your learning based on employer demand.


When you’re ready to find a data scientist role you love, Indeed Prime is waiting. Designed for your best job search, we connect you with open data science roles that match your skills and preferences, like salary and location. (Not to mention, we offer free career coaching services like resume review, mock interviews and salary negotiation.)


New opportunities are waiting. Apply now to answer the call.

Verified skills listed in job postings on Indeed.com with “data” and either “science” or “scientist” in the job title were calculated as a share of all postings from September 14, 2018 to March 14, 2019.

Joy Garza is a member of the content development team for Indeed Prime, the service that connects tech talent to leading brands and startups.

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