Executive Program on Business Analytics, Machine Learning & AI with End-To-End Projects

Neumuuv

Description

Instructor

What topics are covered in the course?

Module 1: Data Management & Data Pipelines for ML & AI
Module 2: Statistics for ML & AI
Module 3: Develop ML & AI Models on Real-Life Business Cases

Module 4: Power BI for Dashboards & Visualization
Capstone Project (Integrated Across Modules)
Certification & Outcomes

What is this course about?

This Executive Program on Data Analytics, Machine Learning & Artificial Intelligence is designed for professionals seeking to build data-driven decision-making skills and hands-on experience with modern analytical tools. The program integrates Python, Excel, SQL, Power BI, and SAS JMP to help learners manage data pipelines, perform advanced statistical analysis, build predictive and AI-driven models, and create interactive dashboards that tell impactful business stories.

Learners will work on end-to-end real-world projects, applying concepts from data preprocessing to model deployment and visualization. The curriculum blends theory with practice, focusing on business problem-solving across domains such as finance, marketing, operations, HR, and customer analytics.

Upon completion, participants will gain the ability to:

Design data pipelines and manage structured/unstructured datasets.
Apply statistical and machine learning techniques for decision intelligence.
Develop, evaluate, and deploy ML/AI models for business applications.
Create insightful dashboards using Power BI integrated with SQL databases.
Present business insights through storytelling with data.

Course Curriculum (100 Hours Total)

Module 1: Data Management & Data Pipelines for ML & AI

Duration: 25 Hours
Tools Used: Python
Objective: Learn how to collect, clean, transform, and manage data efficiently for downstream ML/AI applications.

Topics Covered:

Introduction to Data Ecosystem and Data Lifecycle
Working with Structured & Unstructured Data
Data Collection: APIs, Web Scraping, Databases
Data Cleaning & Preparation using Pandas & NumPy
Data Transformation and Feature Engineering
Building Automated Data Pipelines
Handling Missing Values & Outliers
Case Study: Creating a Data Pipeline for Predictive Analytics

Hands-on Project:
Develop a data ingestion and transformation pipeline for a retail or financial dataset using Python.

Module 2: Statistics for ML & AI

Duration: 25 Hours
Tools Used: Python, Excel
Objective: Build a strong foundation in statistical methods essential for machine learning and data-driven decision-making.

Topics Covered:

Descriptive & Inferential Statistics
Probability Distributions and Sampling Techniques
Hypothesis Testing (t-test, ANOVA, Chi-square)
Correlation & Regression Analysis
Feature Selection using Statistical Techniques
Introduction to Bayesian Inference
Statistical Simulation using Python
Case Study: Statistical Insights for Business Optimization

Hands-on Project:
Perform statistical analysis and build regression-based insights for a sales or customer churn dataset using Excel and Python.

Module 3: Develop ML & AI Models on Real-Life Business Cases

Duration: 30 Hours
Tools Used: Python, SAS JMP
Objective: Apply supervised and unsupervised machine learning algorithms to solve real-world business challenges.

Topics Covered:

Overview of ML Lifecycle: Data to Deployment
Supervised Learning: Regression & Classification
Unsupervised Learning: Clustering & Dimensionality Reduction
Model Evaluation Metrics and Cross Validation
Ensemble Techniques: Random Forest, XGBoost
Introduction to Neural Networks & Deep Learning Concepts
Explainable AI (XAI) and Model Interpretability
Case Studies: Predictive Maintenance, Credit Risk, Customer Segmentation

Hands-on Projects:

Predict customer churn for a telecom company using Python.
Build and interpret a classification model using SAS JMP.
Cluster customers using K-Means for marketing segmentation.

Module 4: Power BI for Dashboards & Visualization

Duration: 20 Hours
Tools Used: Power BI, SQL
Objective: Create compelling dashboards and visual analytics to communicate ML/AI insights effectively.

Topics Covered:

Introduction to BI & Data Visualization Principles
Data Connection & Modeling using Power BI
DAX Functions for Advanced Calculations
Building Interactive Dashboards and Reports
Integrating SQL Queries with Power BI
Publishing and Sharing Dashboards via Power BI Service
Storytelling with Data & Executive Reporting
Case Study: Executive Dashboard for Business KPIs

Hands-on Project:
Develop a Power BI dashboard connected to SQL data to visualize model outcomes and KPIs for a chosen business domain.

Capstone Project (Integrated Across Modules)

Duration: ~10 Hours (part of total 100 hours)

Objective:
Apply all learned concepts to build an end-to-end data analytics and AI solution — from data collection and statistical exploration to model building and dashboard presentation.

Sample Capstone Problem Statements:

Predict loan default risk and visualize key indicators.
Forecast product demand and create an interactive sales dashboard.
Predict employee attrition and recommend retention strategies.

Deliverables:

Data Pipeline Script (Python)
Statistical Analysis Report (Excel/Python)
ML/AI Model (Python/SAS JMP)
Power BI Dashboard (SQL + Power BI Integration)

Certification & Outcomes

Upon successful completion, participants will receive a Certificate of Completion titled:
“Executive Program on Data Analytics, Machine Learning & AI with End-to-End Projects”

Career Outcomes: