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.
Module 2 – Diving Deeper into Data
- 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
Module 3 – Communicating Data Insights
- 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
Module 4 Hands on - Pivot Tables for Data Analysis
- 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
Module 5 Hands on - Exploring ETL (Extract, Transform & Load) in Power Query
- 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
Module 2 - Hands-on Data Analysis in Python with real datasets
- 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
Module 3 – Introducing Machine Learning (ML)
- What is Machine Learning?
- Why Machine Learning?
- Types of Machine Learning.
- Machine Learning Algorithms
- Challenges of Machine Learning.
Module 4 – Hands on - Regression Machine Learning Project
- Problem Definition
- Load and Analyze Data
- Data Visualization
- Model Training
- Evaluate Algorithm
- Improve results with tuning
- Finalize Model
Module 5 – Hands on - Classification Machine Learning Project
- Problem Definition
- Load and analyze Data
- Data visualization
- Model Training
- Evaluate Algorithm
- Improve results with tuning
- Finalize Model
May Intake
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Don't miss out!
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