Course objectives :

The course is designed to provide in-depth subject and knowledge of handling business data and Analytics tools that can be used for problem solving and decision making using real business case studies.

The outcome of the course, the participants will be able to:-
  • Understand the foundations of data science; the role of descriptive, predictive and prescriptive analytics
  • Understand the emergence of business analytics as a competitive strategy
  • Analyze data using statistical and data mining techniques
  • Understand relationships between the underlying business processes of an organization
  • Learn data visualization
  • Storytelling through data.
  • Learn decision-making tools
  • Operations Research techniques.
  • Use advanced analytical tools to analyse complex problems.
  • Manage business processes using analytical and management tools.
  • Analyse and solve customer problems from different industries such as manufacturing, service, retail, software, banking and finance, sports, pharmaceutical, aerospace, etc.
  • Learn analytics through case studies published by different business schools
  • Understand sources of Big Data and the technologies
  • Algorithms for analyzing big data for inferences.
  • Ability to analyze unstructured data such as social media data and machine generated data.
  • Hands on experience with software such as free tools Microsoft Excel, Python, R, Pig,Hive,Map Reduce,NoSQL, etc and commercial tools

Benefits from the course:-

  • Improving competitiveness
  • Sharing information with a business with presentations
  • Improving the decision-making process
  • Speeding up of decision-making process
  • Responding to business user needs for availability of data on timely basis

Modules :-

  • Module-1: Introduction Data science & Business Analytics
  • Module-2: Descriptive Statistics
  • Module-3: Basic Probability for Business issues
  • Module-4: Basic Distributions
  • Module-5: Sampling Technique Big Data
  • Module-6: Data Validation & Data Normality
  • Module-7: Data cleaning process Quality check
  • Module-8: Data Imputation and outlier treatment
  • Module-9: Test of Hypothesis
  • Module-10: Data Transformation
  • Module-11: Predictive modeling & Diagnostics
  • Module-12: Logistic Regression Analysis
  • Module-13: Big Data Analytics
  • Module-14: Cluster Analysis and Methods
  • Module-15: Data Mining Machine Learning and Artificial Intelligence
  • Module-16: Time series
  • Module-17: Model Validation and Testing
  • Module-18: Hadoop Ecosystem

*** Detailed Course Content and duration will be disclosed during demo session ***