Companies worldwide have always gathered and analyzed data about their customers to provide better service and improve their bottom lines. In today’s digital world, we are able to gather tremendous amounts of data, which require non-traditional data processing methods and software.

This article provide necessary information prior roadmaps to becoming a full-stack data scientist, and is originated from source and adapted from https://www.simplilearn.com/tutorials/data-science-tutorial/how-to-become-a-data-scientist

How to Become a Data Scientist?

Data science is the area of study that involves extracting knowledge from all of the data gathered. There is a great demand for professionals who can turn data analysis into a competitive advantage for their organizations. In a career as a data scientist, you’ll create data-driven business solutions and analytics.

Data Science at Work

Did you know that media services provider Netflix uses data science extensively? The company measures user engagement and retention, including:

  • When you pause, rewind or fast-forward
  • What day of the week and what time of day you watch content
  • When and why you leave content
  • Where in the world you’re watching from
  • Your browsing and scrolling behavior
  • What device you watch on

Netflix has over 120 million users worldwide! To process all of that information, Netflix uses advanced data science metrics. This allows it to present a better movie and show recommendations to its users and also create better shows for them. The Netflix hit series House of Cards was developed using data science and big data. Netflix collected user data from the show, West Wing, another drama taking place in the White House. The company took into consideration where people stopped when they fast-forwarded and where they stopped watching the show. Analyzing this data allowed Netflix to create what it believed was a perfectly engrossing show.

Now let us explore some of the important data scientist skills that an individual should possess.

7 Skills To Become A Data Scientist

To become a data scientist, you’ll need to master skills in the following areas:

  • Skill 1: Gain database knowledge which is required to store and analyze data using tools such as Oracle® Database, MySQL®, Microsoft® SQL Server and Teradata®.
  • Skill 2: Learn statistics, probability and mathematical analysis. Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data. Probability is the measure of the likelihood that an event will occur.
    Mathematical analysis is the branch of mathematics dealing with limits and related theories, such as differentiation, integration, measure, infinite series, and analytic functions.
  • Skill 3: Master at least one programming language. Programming tools such as R, Python, and SAS are very important when performing analytics in data.
    R is a free software environment for statistical computing and graphics, which supports most Machine Learning algorithms for Data Analytics such as regression, association, and clustering.
    Python is an open-source general-purpose programming language. Python libraries like NumPy and SciPy are used in Data Science.
    SAS can mine, alter, manage and retrieve data from a variety of sources as well as perform statistical analysis on the data.
  • Skill 4: Learn Data Wrangling which involves cleaning, manipulating, and organizing data. Popular tools for data wrangling include R, Python, Flume, and Scoop.
  • Skill 5: Master the concepts of Machine Learning. Providing systems with the ability to automatically learn and improve from experience without being explicitly programmed to.  Machine Learning can be achieved through various algorithms such as Regressions, Naive Bayes, SVM, K Means Clustering, KNN, and Decision Tree algorithms to name a few.
  • Skill 6: Having a working knowledge of Big Data tools such as Apache Spark, Hadoop, Talend, and Tableau, which are used to deal with large and complex data which can’t be dealt with using traditional data processing software.
  • Skill 7: Develop the ability to visualize results. Data visualization integrating different data sets and creating a visual display of the results using diagrams, chart, and graphs