
Introduction of the Instructor and the Course
At the end of this course, you will learn the following
•A Case Study of becoming an Excellent Business Analyst
Business Analytics in Action: Quick Exercise
At the end of this lecture, you will learn the following
•What is Business Analytics?
At the end of this lecture, you will learn the following
•How is Business Analytics different from Business Analysis?
At the end of this lecture, you will learn the following
•How is Business Analytics different from Data Analytics?
At the end of this lecture, you will learn the following
•What roles can you get after doing Business Analytics
At the end of this lecture, you will learn the following
Overview
At the end of this lecture, you will learn the following
Amazon Case Study
At the end of this lecture, you will learn the following
Netflix Case Study
At the end of this lecture, you will learn the following
Walmart Case Study
At the end of this lecture, you will learn the following
How to become excellent in Business Analytics?
At the end of this lecture, you will learn the following
How to become excellent in Business Analytics?
A case study of becoming excellent in business analytics
At the end of this lecture, you will learn the following
•Data Collection
At the end of this lecture, you will learn the following
•Manual Data Collection
At the end of this lecture, you will learn the following
•How to collect data using Automated Tools?
At the end of this lecture, you will learn the following
•How to use Data Acquisition Tools like Google Analytics?
At the end of this lecture, you will learn the following
•How to use Data Acquisition Tools Like Scrapy?
At the end of this lecture, you will learn the following
•How to use Data Acquisition Tools like SQL?
At the end of this lecture, you will learn the following
•Let us now look at Data Cleaning
At the end of this lecture, you will learn the following
•Let us now look at Data Preprocessing
At the end of this lecture, you will learn the following
•How to use StandardScaler for data standardization
At the end of this lecture, you will learn the following
Label Encoding
At the end of this lecture, you will learn the following
One Hot Encoding
At the end of this lecture, you will learn the following
•Correlation analysis
At the end of this lecture, you will learn the following
•How to determine Feature Importance Scores?
At the end of this lecture, you will learn the following
•PCA - Principal Component Analysis
At the end of this lecture, you will learn the following
•How to use SMOTE to handle imbalanced data?
At the end of this lecture, you will learn the following
•Data Preprocessing Automation Tools, Practice and Resources and Real-World Applications
At the end of this lecture, you will learn the following
•How to master descriptive analytics?
At the end of this lecture, you will learn the following
•How to compute and interpret descriptive statistics?
At the end of this lecture, you will learn the following
•What are the data visualization principles?
At the end of this lecture, you will learn the following
•How to master tools like Excel, Tableau, Power BI, and Python (Pandas, Matplotlib, Seaborn)
At the end of this lecture, you will learn the following
How to use Data Aggregation Techniques like Grouping, filtering, pivot tables, and summarizing large datasets
At the end of this lecture, you will learn the following
•Analyze sales trends, customer behavior, or operational metrics using real-world datasets
At the end of this lecture, you will learn the following
•Create dashboards and reports to communicate insights
At the end of this lecture, you will learn the following
•Purpose and steps to master predictive analytics
At the end of this lecture, you will learn the following
•Regression Analysis
At the end of this lecture, you will learn the following
How to learn Probability
•Classical Probability
At the end of this lecture, you will learn the following
•Conditional Probability
At the end of this lecture, you will learn the following
•Bayes' Theorem
At the end of this lecture, you will learn the following
Normal Distribution
At the end of this lecture, you will learn the following
•Binominal Distribution
At the end of this lecture, you will learn the following
•Poisson Distribution
•Probability Distribution Comparisons and Examples in Python
At the end of this lecture, you will learn the following
•Real-world applications in risk analysis, A/B testing, and predictive modeling
At the end of this lecture, you will learn the following
•Hypothesis Testing
At the end of this lecture, you will learn the following
•How to learn Time Series Forecasting?
At the end of this lecture, you will learn the following
•Supervised Learning
At the end of this lecture, you will learn the following
•Unsupervised Learning
At the end of this lecture, you will learn the following
•How to focus on Python (Scikit-learn, TensorFlow, PyTorch) or R for implementing machine learning models?
At the end of this lecture, you will learn the following
•How to use datasets from Kaggle or UCI Machine Learning Repository to build and test predictive models like customer churn prediction, demand forecasting, or fraud detection
At the end of this lecture, you will learn the following
•How to learn evaluation metrics such as accuracy, precision, recall, F1-score, and RMSE?
At the end of this lecture, you will learn the following
•Purpose and steps to Master Prescriptive Analytics
At the end of this lecture, you will learn the following
•How to learn linear programming, integer programming, and constraint optimization
At the end of this lecture, you will learn the following
•Learn the Mathematical Foundation
At the end of this lecture, you will learn the following
•Learn How to Solve Optimization Problems
At the end of this lecture, you will learn the following
How to study decision trees, Markov decision processes
At the end of this lecture, you will learn the following
•Markov Decision Processes (MDPs)
At the end of this lecture, you will learn the following
•Simulation Techniques
At the end of this lecture, you will learn the following
•How to integrate predictive insights into optimization models
At the end of this lecture, you will learn the following
•How to learn tools like IBM CPLEX, Gurobi, or SAS Optimization
At the end of this lecture, you will learn the following
•How to practice creating optimization models for resource allocation, scheduling, or pricing strategies
At the end of this lecture, you will learn the following
•How to apply prescriptive analytics in industries like supply chain, finance, healthcare, or marketing
Business Analytics Capstone Challenge: Solving a Real Business Problem
At the end of this lecture, you will learn the following
•How to learn Python or R for data analysis and modeling
At the end of this lecture, you will learn the following
•How to gain proficiency in SQL to retrieve and manipulate data
At the end of this lecture, you will learn the following
•How to use Tableau, Power BI, or Excel to create meaningful visual reports
At the end of this lecture, you will learn the following
•How to learn frameworks like CRISP-DM (Cross-Industry Standard Process for Data Mining) to structure your analytics project?
Can you confidently solve a real business problem using data and present recommendations that management can act upon?
Many professionals can create reports. Some can build dashboards. A few can perform statistical analysis. But successful Business Analysts do much more. They know how to define the right business problem, collect the right data, prepare it for analysis, uncover meaningful insights, predict future outcomes, evaluate alternatives, and confidently recommend the best course of action.
That complete journey is exactly what you will master in this course.
Unlike courses that focus only on analytics techniques or statistical concepts, this course is designed around the real work performed by Business Analysts. Every section builds logically on the previous one, taking you through the complete Business Analytics lifecycle—from understanding a business challenge to delivering data-driven business recommendations.
You will begin by learning how businesses identify problems worth solving and how analysts collect relevant data from multiple sources. You will then develop the practical skills required to clean inconsistent data, preprocess raw information, and prepare reliable datasets for meaningful analysis. These are critical capabilities that are often overlooked but form the foundation of successful analytics projects.
Once the data is ready, you will progressively master Descriptive Analytics to understand what has happened, Predictive Analytics to estimate what is likely to happen next, and Prescriptive Analytics to determine the best actions organizations should take. Rather than learning these concepts in isolation, you will apply them to practical business situations that mirror the challenges faced by Business Analysts across industries.
Learning by doing is the core philosophy of this course.
Throughout the program, you will strengthen your skills through carefully designed real business case studies, practical exercises, quizzes, and six hands-on assignments. These assignments are integrated into the learning journey, allowing you to immediately apply every major concept instead of simply watching lectures. You will work with realistic business datasets, analyze customer satisfaction, improve data quality, interpret sales performance, prepare data for predictive models, forecast business outcomes, and develop recommendations supported by evidence.
The learning experience culminates in a comprehensive Business Analytics Capstone Project where you will integrate everything you have learned to solve a complete business problem using the end-to-end Business Analytics process. This project is designed to help you think and perform like a professional Business Analyst by bringing together data collection, preparation, analysis, interpretation, and business decision-making into one structured solution.
Throughout the course, you will also develop one of the most valuable skills employers look for—the ability to transform analytical findings into clear, actionable business recommendations. Organizations do not create value from data alone; they create value when analytical insights lead to better business decisions. This course continuously reinforces that mindset.
Whether you are an aspiring Business Analyst, Data Analyst, MBA student, manager, consultant, engineer, or working professional looking to strengthen your analytical decision-making skills, this course provides a structured, practical, and comprehensive learning experience that prepares you to contribute confidently to real business challenges.
If your goal is not just to learn Business Analytics, but to become a Business Analyst who can confidently solve business problems through End-to-End Business Analytics, Real Case Studies, and Hands-on Assignments, then this course has been designed specifically for you. Enroll Now!
This Course is Part of a Structured Learning Path
Learning Path: ANALYTICS PATH (Starter → Builder → Advanced)
This course is your BUILDER step.
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