– 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

– 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

– 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

– 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

– 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

– 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

– 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

– 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