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Top Data Scientist Skills: Here’s What You Should Know

Data Scientist Skills

Data science is one of the fastest-growing careers today. With massive amounts of data generated daily, professionals who can derive actionable insights from it are essential in industries across the board.

If you’re interested in becoming a data scientist, you’re in the right place. In this article, we’ll discuss what a data scientist is and what top data scientist skills you should have in 2023 and beyond.

What Is a Data Scientist?

Data scientists are analytics professionals responsible for collecting, analyzing, and interpreting data to help the organizations they work for make better and more informed decisions.

The role of a data scientist combines several elements of traditional and technical jobs, including mathematician, scientist, statistician, and computer programmer. Data scientists use advanced analytics techniques, like machine learning and predictive modeling.

Basic roles that most data scientists will be asked to fill include:

  • Gathering and preparing relevant data to use in analytics applications
  • Analyzing data sets with different tools to detect patterns, trends, and relationships
  • Working to develop statistical and predictive models to run against the data sets
  • Communicating your findings through data visualizations, dashboards, and reports
  • Defining and promoting best practices for data collection, preparation, and analysis

Are There Any Must-Have Data Science Skills?

Skills required for data scientists to be successful include:

Programming Languages

Data scientists communicate with computers and give them instructions via programming languages, so it’s critically important that you are up to speed with all the lingo. There are hundreds of programming languages out there. But don’t worry. You don’t need to learn all of them. Here are some of the best languages to learn:

  • Python programming: A general-purpose programming language popular across various sectors, including data science.
  • R programming: R is an open-source language that was specifically designed for data science. Its uses include statistical computing, machine learning, data manipulation, and visualization.
  • SQL: Structured Query Language, or SQL, is a domain-specific language that was specially designed to interact with databases. Instead of competing with Python and R, SQL is used with them to edit and extract data from different relational databases.

Mathematics, Statistical Analysis, Probability

Mathematics, statistical analysis, and probability are all critical data scientist skills you must master. Having a good grasp of statistics is critical when choosing and applying different data techniques, building strong data models, and properly understanding all data you’re dealing with.

The four areas of math that are most important when honing your data science skills include:

  1. Calculus
  2. Algebra
  3. Probability
  4. Statistics

Data scientists use probability and statistics to:

  • Find data anomalies
  • Identify trends or patterns in data
  • Identify dependencies between two or more variables
  • Forecast future trends

Probability and statistics concepts you should work to master include:

  • Measurement level of data
  • Population or sample data
  • Measures of central tendency
  • Measures of variability
  • Measures of asymmetry

Data Mining

Data mining is a process that involves gathering, sorting, and analyzing large data sets. Data mining is akin to mining for gold because, within these vast data sets, a ton of not-so-useful data must be sifted through to find the golden nuggets that will provide valuable insights.

Data scientists use the following data mining techniques to sort and analyze data from different prescriptive to glean the insights they need:

  • Linear regression analysis
  • Clustering analysis
  • Anomaly detection

Data Visualization

Visualizing data is a critical part of communicating the insights you’ve uncovered using data science. Data visualization takes data and turns it into tables, graphs, pie charts, and other visualizations that help you explain the information to stakeholders.

There are many tools you can use for data visualization, including:

  • Tableau: One of the most effective data visualization and analysis tools, Tableau enables users to extract the desired output without any coding required. Tableau is used by industry behemoths like Amazon, Nike, and Coca-Cola.
  • Power BI: This business analytical tool prepares data sets and gives you different scales to analyze them on.
  • D3.js: This tool was introduced in 2011 to support data visualization in web browsers. It also empowers data scientists to map their data easily.

Machine Learning and Artificial Intelligence (AI)

Machine learning is a branch of AI that’s hyper-focused on developing algorithms that learn to perform tasks without being explicitly programmed. As machine learning plays a more central role in consumers’ lives, from Netflix recommendations to Snapchat filters, the need for data scientists with machine learning skills continues to rise. In fact, statistics show that 82 percent of companies needed people with machine learning skills in 2021. At the same time, only 12 percent reported having access to a sufficient pool of candidates with machine learning skills.

Cloud Computing

Big data is usually stored in the cloud. Knowing how to interact with the cloud and understanding how it works is one of the most useful data scientist skills.

Today’s cloud computing landscape is dominated by big tech giants, including Amazon Web Services, Google Cloud, and Microsoft Azure. These providers offer data tools and solutions that create a data science workflow that can be conducted without leaving the cloud.

Data Scientist Soft Skills

The soft skills required for data scientists include:

  • Business sense: It’s not enough to harvest the data. You need to make sense of it to understand its meaning and implications — and pass that information down to company stakeholders. Data scientists should thoroughly understand the industry they work in to make sense of data and conduct better analysis effectively.
  • Communication skills: If you can’t effectively communicate what the data means and why people should care, there’s no sense in you analyzing and tracking data at all. If you can’t help people understand the results of your data, your work as a scientist won’t be valuable for your organization. A good data scientist doesn’t just relay information. They create a compelling story using data.
  • Data ethics skills: Data scientists need strong data ethics skills and beliefs to ensure data results in positive impacts and not the perception that all data does is steal consumer information. It’s important to work to build ethical awareness surrounding data. That means getting familiar with concepts like data privacy, algorithm bias, and feedback loops while also working to develop fair, transparent, and accountable algorithms.

How to Develop Essential Data Science Skills

To develop, practice, and fine-tune your data scientist skills, consider the following:

Take a Course

If you’re trying to break into the data science industry, the best way to get started is to familiarize yourself with the best industry practices and the latest industry tools. Online courses can equip you with all the most critical data scientist skills so that you’re ready to take on your career head-first.

Soak Up as Much Information as Possible

If you don’t want to take a traditional certificate course to brush up on your data science skills, you can use the internet, LinkedIn, and books to enrich your knowledge.

Get Active in the Data Science Community

Connections matter in any career path. Learning the key industry players and connecting with people who can help you advance your career are important for growth. Connect with people on social media, join online groups, join groups at your school, attend data science events in your area, and find other ways to make lasting connections with like-minded data scientists. When it comes to making an impact, you should get involved with the community instead of passively learning from the sidelines.

Participate in Open-Source Projects

One of the best ways to get your foot in the door as a data scientist is by contributing to open-source projects.

Become a part of open-source projects that align with your interests and career goals. You can find opportunities to get involved on GitHub.

If you’re looking to dive deeper into big data and explore and experiment while growing your data scientist skills, participating in open-source data science projects is the way to do that.

Master Data Science Technical Skills

Master all the top skills for a data scientist we went over above, including:

  • Mathematics
  • Statistics
  • Programming
  • Deep learning
  • Machine learning
  • Statistical analysis
  • Data mining
  • Data visualization

Upskilling to Gain the Skills Required for a Data Scientist

Are you ready to fine-tune your data scientist skills? Sign up for this data science bootcamp to master data science and become a certified data scientist. This comprehensive six-month bootcamp features an applied learning model consisting of a mix of live classes, self-paced videos, and hands-on projects.

Some of the skills and tools you will learn in this online bootcamp include descriptive statistics, exploratory data analysis, machine learning, model building, supervised and unsupervised learning, ensemble learning, Python, data visualization with Tableau, Microsoft Excel, NumPy, SciPy, Pandas, TensorFlow, and much more.

You might also like to read:

A Data Scientist Job Description: The Roles and Responsibilities in 2023

Data Scientist vs. Data Analyst – The Differences Explained

All About Data Scientist Salaries

An Ultimate Guide to Full Stack Developer Skills

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