Module 1: Data Analytics Fundamentals
- Understanding the concept of analytics
- Significance and relevance of analytics in decision-making
- Diverse categories of analytics: Descriptive, Predictive, and Prescriptive
- The role of data visualization in analytics
- Understand data warehousing, data lakes, and data marts.
- BigData Analytics Fundamentals
- Data Analytics with ChatGPT & Prompt Engineering
Module 2: Data Analytics with Microsoft Excel
- Fundamentals of data representation using Microsoft Excel
- Learn to collect, manage, and store company data effectively
- Data preparation and cleaning techniques
- Formulas, functions, and their application in data analysis
- Exploring the power of Pivot Tables for data summarization
- Creating dynamic visuals with Pivot Charts
- Leveraging advanced Excel features for effective analytics
Module 3: Microsoft Power BI & Forecasting
- Understanding the role of Power BI in data visualization
- Exploring Power BI's user interface and functionalities
- Identifying Key Performance Indicators (KPIs) for business insights
- Utilizing DAX functions for complex calculations and modeling
- Advanced techniques for data transformation using Power Query
- Creating interactive dashboards and reports with Power BI
- Forecast future trends and patterns using Power BI
- Integrating geographical data through mapping techniques
- Crafting convincing data-driven stories to engage stakeholders
Module 4: SQL & DataWarehousing
- Database Management System OverView
- Database Structure and SQL Select statements
- Filtering Data in SQL Joins and subqueries in SQL
- Data definition language (DDL)
- Sorting data & Functions in SQL
- Connecting database to Power BI
- Understanding Data warehousing
Module 5: Python for Data Analytics
- Intro to Python
- Variables, Data Types & Type Conversion in Python
- Numbers & Strings in Python
- Working with Lists in Python
- Conditionals & Loops in Python
- Functions & Built in Functions in Python
- NumPy in Python
- Dictionaries in Python
- Pandas Library in Python
- Manipulating Data Frames
- Data Visualization in Python (Matplotlib & Seaborn)
- Data Wrangling
- Data pre-processing and scaling
- Feature Extraction
- Descriptive statistics for Data Analysis
Module 6: Time Series Analytics
- An introduction to time-series and stationary data
- Key concepts that include filters, signal transformations, and anomalies
- Spot trends, seasonality and secrets hidden in your data
- How to handle and visualize time series data
- Learn tricks to forecast what's next.
- The application of time series forecasting with Python
Module 7: Machine Learning & Predictive Modelling
- Splitting the dataset as per the model requirement
- K-fold methods
- K-nearest neighbors Naive Bayes
- Linear Regression
- Multivariable Regression
- Logistic Regression
- Decision Tree
- Support Vector Machine
- Model evaluation techniques
Module 8: Profile Building & Mentoring
- Career Prospects in Sales, marketing, Supply Chain, Finance, HR Analytics and much more
- Business case studies & capstone projects in each module
- Special guest sessions from Industry leaders from Amazon, Microsoft, Nike, Jazz and more.
- Cracking interviews with skills & tricks
