7 In-Demand Technical Skills for Data Scientists in 2021

Data science is a constantly-evolving field. Here are seven in-demand skills that all aspiring data scientists should seek to master in 2021 and beyond.

data scientist skills in 2021

Data science is a fast-growing career field, with a 650% growth in jobs since 2012 and a median salary of around $125,000—well above the national average.

What’s more, data science has not been as impacted by COVID-19 hiring freezes and layoffs as other industries. In fact, data science as a career path is more lucrative now than it has ever been, painting an optimistic job landscape for 2021 and beyond.

If you're looking to become a data scientist right now, there are seven core technical skills you need to know.

Skill 1: Math

It may be time to break out the math textbooks. Data scientists need a solid foundation in the following math concepts:

  • Statistics. It is important to know key terms like mean, median, mode, maximum likelihood indicators, standard deviation, and distributions. Data scientists should understand sampling techniques and how to avoid bias in experiments. Descriptive statistics paint a picture of the data through charts and graphs, while inferential statistics help you make predictions using that data.
  • Probability. Topics like Bayes theorem, probability distribution functions, Central Limit Theorem, expected values, standard errors, random variables, and independence will come in hand as a data scientist. Probability helps you perform statistical tests, so you can tell if you are truly uncovering meaningful trends in the data—or just getting lucky!
  • Linear algebra. Linear algebra is the backbone of important algorithms, and knowledge of matrices and vectors will definitely help, especially if you specialize more in machine learning. Need more convincing? Check out data science applications of linear algebra here.
  • Multivariate calculus. Brush up on mean value theorems, gradient, derivatives, limits, the product and chain rules, Taylor series, and beta and gamma functions. You will use these concepts in logistic regression algorithms and may face calculus problems in interviews.

How do I learn this skill?

Much of this knowledge is an extension of high school math. You can find many awesome MOOCs online covering the basics, such as Coursera’s Mathematics for Data Science as well as other books and university courses.

Skill 2: Programming

Python is the golden standard in data science. In 2018, 66% of data scientists reported using Python every day and it overtook R as the most popular language for data science. It is a multi-purpose, object-oriented programming language that is easy to deploy in applications or websites and comes with an active data science community, making it an easy choice for top tech companies.

  • Python. As you advance in Python fundamentals, you will also want to explore Python libraries, reusable pieces of code that you can use in place of having to rewrite simple commands. The top Python libraries for data science include Pandas, NumPy, Matplotlib, SciPy, Seaborn, TensorFlow, and Scikit-learn.
  • R. R is an open-source language used for statistical analysis, which has tools for presenting and communicating data-driven results. R programming may be more suited for research and academic work.
  • SAS. SAS is a software suite with built-in statistical functions and GUI (graphical user interface) to guide less technical users. Since SAS is a very expensive enterprise software and Python and R are free to use, it makes sense to start with one of the other languages.

When deciding which language to use, you might want to consider the industry and company you are looking to enter.

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When moving into data engineering or machine learning, other computer science languages may become useful, like Java and C (C++). Thankfully, programming involves a specific type of structured thinking and problem solving, so as you learn one primary language, other hard skills will become easier to pick up.

How do I learn this skill?

Athough you can choose to learn data science through university or formal degree programs, you don’t need a bachelor’s degree or a Master’s degree. Online courses, bootcamps, and certification programs provide a great foundation at a fraction of the cost.

For those just getting started, Springboard’s Data Science Prep Course provides an introduction to Python and data science basics, lasting 4-6 weeks.

Springboard’s data science bootcamp, which comes with mentorship and a job guarantee, teaches Python and is ideal for those with six months of active coding with a general-purpose coding language. Springboard’s Intro to Python course offers an introduction to Jupyter notebooks and pandas, providing the opportunity to apply your new technical skills on a real business case with a Yelp data set.

Skill 3: Analytical tools (SQL, Spark, and Hoop)

Analytical tools can help you extract meaningful insights from data and provide useful frameworks for big data processing, like SQL, Spark, Hoop, Hive, and Pig.

  • SQL. SQL allows you to store, query, and manipulate data in relational database management systems. You can connect multiple data sets through joins.
  • Spark. Spark is a processing engine that is easy to integrate with Hadoop and can work with large, unstructured datasets. It can store data operations internally but requires a third-party file distribution system for data analysis.
  • Hadoop. Hadoop is an open-source software library developed by the Apache Software Foundation that distributes big data processing across a cluster of computing devices. Hadoop uses its own Hadoop Distributed File System (HDFS) to store large data sets and stream the data to user applications like MapReduce, which can then take care of data analytics.

How do I learn this skill?

You can learn Spark and Hadoop through a number of books and online tools. Skillcrunch also has an excellent list of how to pick up SQL for free through online tutorials.

Skill 4: Machine learning

The more data a company handles, the more likely machine learning will be part of your day-to-day. Although not all data science roles require deep learning, data engineering skills, or knowledge of Natural Language Processing, take the time to become acquainted with terms like k-nearest neighbors, random forests, and ensemble methods, especially if you are interested in working with big data.

How do I learn this skill?

Springboard’s Machine Learning Career Track may be the entry point you need to master regression, classification, decision tree, and anomaly detection modeling; recommendation systems and time series prediction models; and how to select the right model.

Skill 5: Data visualization

Data means nothing unless different people can understand it! Data visualization tools like Matplotlib, Ggplot, or D3.js help turn data into charts, graphs, and other visuals that key decision-makers at global companies will love.

  • Power BI. Power BI comes in desktop, mobile, and service forms and uses Azure, SQL, and Excel to generate a variety of visualizations. It is easy to learn for beginners.

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  • Tableau. Tableau is a more complex tool with greater speed and expanded capabilities. It features drag-and-drop functions that enable users to produce reports (heat maps, line charts, scatter plots, etc.) and create beautiful dashboards.

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How do I learn this skill?

Here are a number of free data visualization tools to get you started.

Skill 6: Data wrangling

After collecting data from multiple data sources, you’ll probably find messy data that needs to be cleaned up. Data wrangling builds off of coding languages and helps you address data imperfections, like missing values, string formatting, and date formatting. For example, date time stamps can come in forms like "2020-05-06" or "05/06/2020" and you will need to consistently transform all entries for smooth analysis.

How do I learn this skill?

Learn more about data wrangling in Springboard's comprehensive guide.

Skill 7: Business acumen

Last but not least, data scientists need communication skills and a deep understanding of business problems, so they can convey their discoveries to other stakeholders. By understanding data, companies learn how to maximize efficiency, minimize costs, and seize new revenue opportunities.

How do I learn this skill?

Finding ways to affect large-scale business decisions and come up with creative ideas of experiments based on trends in existing internal or external data will help you stand out on the job and make a big impact. Keep up-to-date on industry trends and practice soft skills through teamwork and relationship building.

Don't forget to also check out Springboard's guide on how to become a data scientist with no experience.

Is data science the right career for you?

Springboard offers a comprehensive data science bootcamp. You’ll work with a one-on-one mentor to learn about data science, data wrangling, machine learning, and Python—and finish it all off with a portfolio-worthy capstone project.

Check out Springboard’s Data Science Career Track to see if you qualify.

Not quite ready to dive into a data science bootcamp?

Springboard now offers a Data Science Prep Course, where you can learn the foundational coding and statistics skills needed to start your career in data science.

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