As the world becomes more data driven, future-focused leaders need to develop the quantitative skills to inform corporate decision-making and managerial strategy. The course in its 2-levels equips participants with the knowledge and practical tools to understand, interpret, and communicate data relevant to their roles and organization. They then develop an understanding of how data-driven models can improve your ability to make decisions in a fast-paced world.

LEVEL ONE

Module 1 - Introducing Data Analytics
  • What is Data Analytics?
  • Need for Data Analytics.
  • Types of Data Analytics.
  • Data in Decision making.
  • Data Privacy and Ethics.
  • Data Challenges Managers and Organizations Face.
  • Data Types
  • Basic Statistical Parameters
  • Data Analytics Process Lifecycle
    • Defining the objective
    • Collecting the Data
    • Cleaning the Data
    • Analyzing the Data (exploratory, descriptive, predictive analysis etc.)
    • Sharing Results (visualization and interpretation)
  • Tools for Data Analytics
  • Understanding the Data
  • Representing and Exploring Data Visually
  • Art of Storytelling with Data
  • Understand the context
  • Choose an appropriate visual
  • Eliminate clutter
  • Draw attention where you want it
  • Tell a story
  • Cleaning and Formatting source data
  • Pivoting Fields & Multiple Dimensions
  • Methods of Aggregation
  • Hierarchies and Grouping
  • Updating and refreshing pivot table with more data
  • Slicers – Interactive filtering
  • Charts & Dashboard build
  • Exploring Power Query Editor
  • Importing Multiple Files from Folder
  • Combine Data from Multiple Tables
  • Unpivoting Data
  • Columns Operations (Split, Merge etc.)
  • Filtering & Sorting Data
  • Grouping and Aggregating Data
  • Conditional Columns in Power Query

LEVEL TWO

Module 1 - Crash Course in Python and SciPy
  • Python Crash Course
  • NumPy Crash Course
  • Matplotlib Crash Course
  • Pandas Crash Course
  • Loading data into Data frame
  • Concatenating files (Reading multiple files)
  • Handling missing values in data
  • Handling duplicates
  • Manipulating columns
  • Filtering records based on condition
  • Transforming data with various methods (string methods, apply etc.)
  • Aggregating and Summarizing data 
  • Working with dates and time
  • Visualizing and interpreting results in Matplotlib
  • What is Machine Learning?
  • Why Machine Learning?
  • Types of Machine Learning.
  • Machine Learning Algorithms
  • Challenges of Machine Learning.
  • Problem Definition
  • Load and Analyze Data
  • Data Visualization
  • Model Training
  • Evaluate Algorithm
  • Improve results with tuning
  • Finalize Model
  • Problem Definition
  • Load and analyze Data
  • Data visualization
  • Model Training
  • Evaluate Algorithm
  • Improve results with tuning
  • Finalize Model
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