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Big Data and Analytics



 Big data analytics is the use of advanced analytic techniques against very large, diverse data sets that include structured, semi-structured, and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.

Course Objectives:

At the end of this course the participants will be able to:

  • Understand the Big Data Platform and its Use cases
  • Provide an overview of Apache Hadoop
  • Provide HDFS Concepts and Interfacing with HDFS
  • Unstructured Data
  • Exposure to Big Data Analytics
  • Provide hands-on Hadoop Eco System

Targeted Audience:

  • Essentially, big data allows companies to know their customers in order to improve their marketing strategies and the customer experience
  • Big data provides crucial insight into your customers, including demographics, geographic location, and how they interact with your company in real time.

Course Outlines:

Unit 1: 

  • Enables organizations to separate storage from computing requirements
  • Provides cost-effective elasticity for data-centric workloads
  • Can easily be deployed and leveraged both on-premises and in the cloud

Unit 2: 

  • Data Models, Facts, and Dimensions Data
  • Structured Data
  • Unstructured Data
  • Semi-Structured Data.

Unit 3: 

  • Big Data analyst wears multiple hats, frequently switching gears
  • A Big Data analyst wears multiple hats, frequently switching gears from conducting research to mining data for information to presenting findings.
  • Collecting, analyzing, visualizing, and communicating this data to help guide these future decisions.
  • Social media, cloud applications, and machine sensor data

Unit 4:

  • A Data Analyst role is better suited for those who want to start their career in analytics
  • A Data Scientist role is recommended for those who want to create advanced machine learning models and use deep learning techniques to ease human tasks.
  • Skilled data analysts are some of the most sought-after professionals in the world

Unit 5: 

  • Big data refers to any large and complex collection of data.
  • Data analytics is the process of extracting meaningful information from data
  • Data science is a multidisciplinary field that aims to produce broader insights.
  • Data analysts rely on skills like programming in R or Python, querying databases with SQL, and performing statistical analysis.

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