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  1. 1. DATA SCIENCE TODAY EVERYTHING IS GOING ONLINE AND AMOUNT OF DATA IS INCREASING EVERY SECOND. SO, THE DEMAND FOR GETTING MEANINGFUL INSIGHTS FROM THIS HUGE AMOUNT OF DATA IS ALSO INCREASING AND THAT’S WHY DATA SCIENCE IS THE FUTURE
  2. 2. WHAT IS 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 and actionable insights from data across a broad range of application domains. Data science is a "concept to unify statistics, data analysis, informatics, and their related methods" in order to "understand and analyse actual phenomena" with data. However, data science is different from computer science and information
  3. 3. WHY DATA SCIENCE IS FUTURE Everything is changing to digital nowadays and because of that more and more data gonna be recorded and maintained, so to improve the performance of any firm we need to study data and make better business strategies for continuous growth. So its gonna be obvious demand of any data scientist increases to great extent. If we look a little bit more ahead, the US Bureau of Labor Statistics predicts that by 2026—so around six years from now—there will be 11.5 million jobs in data science and analytics. The estimated total pay for a
  4. 4. WHO IS A DATA SCIENTIST Data scientists are big data wranglers, gathering and analyzing large sets of structured and unstructured data. A data scientist’s role combines computer science, statistics, and mathematics. They analyze, process, and model data then interpret the results to create actionable plans for companies and other organizations. Data scientists are analytical experts who utilize their skills in both technology and social science to find trends and manage data. They use industry knowledge, contextual understanding, skepticism of existing
  5. 5. HOW TO BECOME DATA SCIENTIST • Skill 1: Gain database knowledge which is required to store and analyze data • Skill 2: Learn statistics, probability and mathematical analysis. • Skill 3: Master at least one programming language. Programming tools such as R, Python are very important when performing analytics in data. • Skill 4: Learn Data Wrangling which involves cleaning, manipulating, and organizing data. Popular tools for
  6. 6. • 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. • 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

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