Top 7 Best skills to become a Data scientist

Data Scientist :

Data Scientists are analytical experts who utilize their skills in both technology and social sciences to find trends and manage data.

Data Science:

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from noisy, structured, and unstructured data, and apply knowledge from data across a broad range of application domains.

Data Science brings value to organizations and businesses. It is one of the fastest-growing fields that offers job opportunities to graduates pursuing an MS in Data Science.

Data Science at work:

Media service provider Amazon uses data science huge, The company measures user engagement and retention, Including.

  • When you pause, re-wine, or fast-forward.
  • What day of the week and what time of day do you watch content
  • Where is the world your watching from
  • Your browsing and scrolling behavior
  • What device do you watch on
  • When and Why you leave on co

Amazon has over 100 million users worldwide! To process all of that information, Amazon 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 Amazon hit series House of Cards was developed using data science and big data. Amazon 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 Amazon to create what it believed was a perfectly engrossing show in these all to know the Data Scientist.

What are the skills to become a data scientist?

The list below are the 7 skills to become a data scientist

  1. Develop the ability to visualize the result
  2. Learn data Wrangling
  3. Having a Working knowledge of Big data tools
  4. Masters in the concepts of Machine Learning
  5. Gain database Knowledge
  6. Learn statistics, Probability, and mathematical analysis
  7. Master at least 1 programming language

Develop the ability to visualize the result:

Data visualization integrates different data sets and creates a visual display of the results using diagrams, charts, and graphs to become the Data Scientist.

Learn Data Wrangling:

which involves cleaning, manipulating, and organizing data. Popular tools for data wrangling include R, Python, Flume, and Scoop.

Having a working knowledge of Big data tools:

Apache Spark, Hadoop, Talend, and Tableau, are used to deal with large and complex data which can’t be dealt with using traditional data processing software.

Masters in 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, Naïve Bayes, SVM, K Means Clustering, KNN, and Decision Tree algorithms to name a few.

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Gain Database Knowledge:

which is required to store and analyze data using tools such as Oracle, Database, MySQL, Microsoft, SQL Server, and Teradata.

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.

Master at least 1 programming language:

Programming tools such as R, Python, and SAS are very important when performing analytics on 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.

These are all the skills that must be known to become a Data Scientist


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