The Skills and Tools Every Data Scientist Must Master

Data scientists are in high demand in a wide variety of industries ranging from general business and finance to healthcare and science. Data scientists are required to know a vast amount of information pertaining to computer science, data visualization, and data analysis along with statistics and machine learning. There are many skills and tools a data scientist must master, and it all starts by obtaining a robust education.  

Data Scientist Educational Requirements  

A future data scientist must also have a background in statistics. After all, data science fields of study often include math and statistics, computer science, and engineering.

Upon graduating from a college degree program, the majority of data scientist students obtain a Master’s degree. Almost half of data scientist graduates go on to obtain a Ph.D. Graduate studies may include astrophysics, data science, or mathematics. Graduates that have a solid background in computer science often choose to advance their skills during a data science bootcamp.

Data Science Bootcamp

A data science bootcamp extends anywhere from five days to multiple months in length. During that time, data science bootcamp learners commonly learn to apply academic knowledge to analytical applications using various programming languages. Data science learners are typically required to have a background in math, science, engineering, or technology along with knowledge of coding and language formats to attend the course. Depending on the bootcamp chosen, participants might be called upon to enter various forms of data into a specific language or platform and use the technology to solve specific problems.


Calculus, linear algebra, and statistics are all areas of math that data scientists will need should they be required to create their own data analysis platforms. A background in statistics is particularly helpful for understanding statistical distributions, estimators, and tests. The results of statistical findings are commonly required by companies in order to make informed decisions.


A data scientist must know how to use code to create programs. They must have an advanced understanding ranging from basic coding to advanced analytical platforms. The many tools used include Apache Spark, C/C++, Java, Python, R, and SQL. Each program has a specific use. For example:

  • Apache Spark is preferred for analyzing data over other types of programs for its ability to store computations into memory. The platform more quickly runs complicated algorithms, which is necessary when dealing with large data sets. By caching memory, scientists are less likely to lose valuable information.
  • Hadoop is often used when data volumes exceed available memory. The platform is able to send data to different servers. Hadoop is also ideal for data exploration, filtration, sampling, and summarization.
  • Python is becoming a more and more popular programming language. The platform is useful for a variety of processes needed by data scientists. The language’s versatility enables users to accomplish many different tasks that might include creating data sets or importing SQL tables.
  • SQL is often required knowledge for data scientists to accomplish various functions that include adding, deleting, or extracting information from databases. SQL also has the capability to perform analytical functions. By using the platform’s precise commands, users are able to perform inquiries more quickly.

Data Visualization  

The amount of data that businesses and industries produce today are greater than ever before. However, in order to be useful, the data must be converted into a format that is easily comprehended. A data scientist uses d3.js, ggplot, Matplotlib, Tableau, and other tools for this purpose. By organizing and transforming data into usable formats, companies are able to make decisions based on the results.

Work with Unstructured Data

Unstructured data refers to audio or visual feeds, blog posts, customer reviews, and social media posts. The data contained within multimedia formats often requires that a data scientist have the ability to analyze, understand, and manipulate the data in order to retrieve pertinent information that may be valuable to a business or industry.

Artificial Intelligence and Machine Learning

Data scientists that have the ability to create programs with artificial intelligence may then benefit from advancing the program’s ability to learn independently. The program can use decision trees, logistic regression, and other algorithms to analyze data sets, make predictions, or solve problems once the platform receives a sufficient amount of data.

Natural Curiosity

A data scientist needs to possess an innate desire to obtain more knowledge or information. The hunger influences them to begin the educational process, to learn fields of data science along with desiring to find answers and insights contained within data sets. Curiosity drives the scientist forward despite obstacles to achieve the end result.

Business Knowledge

Data scientists need to have a good foundational comprehension of the specific industry where their skills are needed. In this way, they are better able to solve a company’s problems and facilitate solutions to help it become more effective and productive.  

Effective Communication

The diagnoses, predictions, or other findings that data scientists are able to formulate mean nothing to a company if they cannot comprehend the results. While presenting illustrated data, a data scientist must be able to explain how the results impact the business. As such, data scientists must be able to clearly translate the findings in order to make them useful to the company.

Team Player

Data scientists do not work alone. They must combine efforts with business and company executives to see out effective strategies. They may have to work with engineers or designers to manufacture better products or working with marketing firms to create more effective campaigns. Scientists may share their insights with software developers or key company stakeholders, and in both cases will need to tailor their communication strategy to do so effectively.


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