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STAR BIGDATA ANALYTICS


With the advance of IT storage, processing , computation, and sensing technologies, Big Data has become a novel norm Of life. Only until recently, computers are able to capture and analysis all sorts of large-scale data from all kinds of fields people, behavior, information, devices, Sensors, biological signals, finance, vehicles, astrology, neurology etc. Almost all industries are bracing into the challenge of Big Data and want to big out valuable information to get insight to solve their challenges. Data Analytics is the science of analyzing data to convert information to useful knowledge. This knowledge could help us understand our world better, and in many contexts, enable us to make better decisions.

Audience

A basic knowledge of statistics to a rigorous understanding of Machine Learning. Computer skills that are useful are a Querying Language (SQL,Hive,Pig), a scripting Language (Python, Matlab), a Statistical Language (R, SAS, SPSS), and a Spreadsheet(Excel).

Course Objectives

In this Course, you will learn about:

  • Big Data and how the same impact the business.
  • Analysing the data using the programming and Visualization tools.
  • Different Data Mining techniques and how to use the same in your daily operations.
  • Different Analytical techniques and their usage in multiple industries.

Course Outcome

After completing this course, you will be able:

  • Describe Big Data and Its Importance.
  • Analyse the unstructured data and apply R Programming Concepts on it.
  • Describe the Big Data usage in different Industries.
  • Implement Machine Learning concepts and Data Visualization techniques on data.
  • Work as Data Analyst and can generate prediction based on the analysed data.

Table of Contents outline

Part 1: Exploring Big Data and Hadoop

  • Introducing Data and Big Data.
  • Application of Big Data in Commercial Areas.
  • Big Data and Hadoop.

Part 2: Analysing Big Data with R

  • Exploring Analytics
  • Exploring R- Data Analytics language
  • Performing Statistics concepts with R

Part 3: Exploring Machine Learning

  • Introduction to Machine Learning
  • Machine Learning and Hadoop

Part 4: Big Data and Data Mining

  • Retrieving Text and Search Engines
  • Machine Learning and HadoopText Mining and Analytics
  • Pattern Discovery in Data Mining
  • Analysing Clusters in Data Mining

Part 5: Big Data and Data Visualization

  • Data Visualizations and Tools

Part 6: Exploring Mobile analytics

Part 7: Exploring Mobile analytics

Part 8: Exploring Real World Analytical Organizations

Part 9: Lab Exercises

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