Data Analytics


Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.

Ebodhi stepped ahead to bring expert knowledge and experience packaged together into a well-designed course content suiting industry needs.

Course Content:

Course Description

  • What is Analytics & Data Science?
  • Common Terms in Data Science
  • Data Science VS Machine Learning VS Artificial Intelligence
  • Relevance in industry and need of the hour
  • Types of problems and Business Objectives solved in Data Science
  • How leading companies using the power of Data Science to boost growth?
  • Critical success drivers
  • Overview of Analytics tools, their popularity and usage
  • Project plan for Analytics project
  • Why Python for Data Science?
  • Overview of Python
  • Introduction to installation of Python
  • Introduction to Python Editors and IDEs (PyCharm, Jupyter, Rodeo, Ipython, etc)
  • Concept of packages in Python (Numpy, Scikit-Learn, Pandas, Matplotlib, Seaborn, SciPy, etc)
  • Installing & Loading Packages in Python Environment
  • Python Data Types/ Structures (Lists, Tuples, Dictionaries)
  • List and Dictionary Comprehensions
  • Basic Operation – Mathematical – String – Date
  • Reading & Writing Data
  • Control Flow & Conditional Statements
  • Debugging & Code Profiles
  • Python Classes & Modules – Concept & Implementation
  • Importing Data in Python through various sources (Excel, CSV, TXT, etc)
  • Viewing Data Objects – Subsetting, Methods
  • Exporting Data to Various formats
  • Important Python Packages : Pandas, Beautifulsoup
  • What is Segmentation & why it is used?
  • Mechanics and Concept of Segmentation
  • K-Means Clustering
  • Hierarchal Clustering & Spectral Clustering Concepts
  • Concept of Conditional Probablity
  • Bayes Theorem & Applications
  • Naive Bayes in Machine Learning Modeling
  • Concept & Applications
  • Mechanics of SVM
  • Implementation of SVM on Models
  • Kernels of SVM
  • Introduction to NLP & Text Mining
  • Touring Powerful NLP libraries in Python
  • Data Preprocessing & Solving real Text Mining Problem
  • Text Analysis – Sentiment Analysis using Python
  • Text Analysis – Word Cloud Analysis using Python
  • Applications of Social Media Analytics
  • Examples & Actionable Insights using Social Media Analytics

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