Learn from Industry Experts • Hands-on Training
Data Science & Machine Learning
This advanced Data Science & Machine Learning course is designed to equip you with industry-level skills in data analysis, predictive modeling, and intelligent system development. You will learn how to collect, process, and analyze data, build and train machine learning models, and apply real-world techniques to solve complex business problems and create data-driven, scalable solutions.
Watch Video
Data Science & Machine Learning – Analyze Better, Predict Smarter
Our Advanced Data Science & Machine Learning program is designed to help learners master data-driven techniques for extracting insights and building intelligent predictive models. This course focuses on data analysis, model development, and applying machine learning algorithms to solve real-world problems, enabling scalable, efficient, and industry-ready solutions for business intelligence and decision-making.
Duration
6 Months
Sessions
48
Classes Days
Mon, Wed, Fri
Summary Of The Course
This Data Science & Machine Learning course is designed for complete beginners and gradually
builds them into industry-ready professionals. The course focuses not only on tools and techniques, but on developing strong analytical thinking, problem-solving ability, and real-world decision-making skills.
Students will learn how to translate business problems into data problems, work with real datasets, perform exploratory data analysis (EDA), engineer meaningful features, train and evaluate machine learning models, and understand how models are deployed and monitored in production environments. The course follows a practical, end-to-end approach, ensuring that students understand the full lifecycle of a data science problem rather than isolated concepts.
- Problem Framing and Data Thinking
- Python Programming for Data Work
- SQL for Data Querying and Analysis
- Data Cleaning and Exploratory Data Analysis (EDA)
- Statistical Thinking and Interpretation
- Feature Engineering (Basic to Advanced)
- Machine Learning Model Development
- Model Evaluation and Business Impact Understanding
- Deployment Basics and Production Awareness
- Monitoring, Drift, and Iterative Improvement
After completing this course, students will be able to:
- Build complete AI-powered applications from scratch
- Develop backend systems integrated with databases
- Design and deploy RAG-based AI systems
- Build intelligent agents with tools, memory, and workflows
- Create portfolio-ready projects for freelance or job opportunities
- Python
- Jupyter Notebook
- NumPy
- Pandas
- Matplotlib / Seaborn
- SQL
- Scikit-learn
- Model Serialization (Pickle/Joblib)
- API Basics (FastAPI)
- Data Thinking Frameworks
- Analytical Reasoning and Problem Solving
📚 Lectures
Industry Orientation, Problem Framing & Data Thinking
Subjects Discussed
1 Introduction to Data Science, Machine Learning, and Industry Applications
2 Types of Business Problems (Fraud, Churn, Recommendation, Forecasting)
3 Mapping Business Problems to ML Problems (Classification, Regression)
4 End-to-End ML Lifecycle and Iterative Loop Thinking
5 Data Thinking Frameworks: Units, Time, Targets, and Context
6 When NOT to Use Machine Learning (Rules vs ML, Cost vs Value)
Python Foundations for Problem Solving
Subjects Discussed
8 Data Structures: Lists, Dictionaries, Sets, and Their Practical Use
9 Conditional Logic, Loops, and Problem-Solving Patterns
10 Functions, Code Reusability, and Clean Coding Practices
11 File Handling and Working with External Data
12 Writing Structured and Readable Data Processing Code
Python for Data Analysis (Pandas & NumPy)
Subjects Discussed
14 Introduction to Pandas and DataFrame Operations
15 Reading, Inspecting, and Understanding Datasets
16 Filtering, Sorting, Grouping, and Aggregation
17 Handling Missing Values, Duplicates, and Data Type Issues
18 Building a Clean and Reusable Data Processing Pipeline
SQL for Analytical Thinking
Subjects Discussed
19 Databases, Tables, and Analytical vs Transactional Thinking
20 SELECT, WHERE, ORDER BY, and Basic Filtering
21 Aggregations: COUNT, SUM, AVG, GROUP BY, HAVING
22 JOINs and Combining Multiple Data Sources
23 Subqueries, CTEs, and Structured Query Writing
24 Thinking in SQL: Translating Questions into Queries
Statistics, Data Understanding & EDA
Subjects Discussed
25 Types of Variables and Descriptive Statistics (Mean, Median, Variance)
26 Distributions, Outliers, and Data Behavior
27 Probability Intuition, Sampling, Bias, and Noise
28 Correlation vs Causation and Common Statistical Pitfalls
29 Data Visualization and Storytelling with Charts
30 Full EDA Workflow: Understanding Data Before Modeling
Data Preparation & Feature Engineering (Basic to Advanced)
Subjects Discussed
31 Data Cleaning Pipeline for Machine Learning
32 Encoding Categorical Variables and Feature Scaling
33 Handling Imbalanced Data and Preventing Data Leakage
34 Basic Feature Engineering: Counts, Ratios, Aggregations
35 Advanced Feature Engineering: Time-Based and Behavioral Features
36 Feature Thinking Framework: Signal vs Noise and Predictive Power
Machine Learning Model Development
Subjects Discussed
37 What Machine Learning Does and Model Selection Thinking
38 Train/Test Split, Validation Strategy, and Baselines
39 Linear and Logistic Regression
40 Decision Trees and Random Forests
41 Gradient Boosting and Model Complexity
42 Iterative Modeling: Improving Models Through Error Analysis
Model Evaluation, Deployment & Production Thinking
Subjects Discussed
43 Evaluation Metrics: Accuracy, Precision, Recall, F1, ROC-AUC
44 Business Metrics vs Model Metrics and Threshold Tuning
45 Overfitting, Cross-Validation, and Model Comparison
46 Introduction to Deployment: Saving Models and APIs
47 Monitoring: Drift, Performance Tracking, and Retraining Strategy
48 Portfolio Development, Interview Readiness, and Career Guidance
Why Learn Agentic AI Systems?
Master next-generation autonomous AI systems, learn how to design and deploy intelligent agents, and build solutions that can plan, reason, and execute tasks independently to solve real-world business and technical problems with high impact.
Better Career Opportunities
Gain in-demand Data Science & Machine Learning skills for roles in data analysis, AI, and predictive modeling across industries.
Freelancing & Online Income
Work with global clients on data analysis, dashboards, and ML projects, and earn online from anywhere.
Business & Brand Growth
Leverage data-driven insights and machine learning models to optimize decisions, improve performance, and scale your business effectively.
We will contact
Get a call back
Get testimonial widget now for Elementor along with a fully responsive & mobile friendly interface to help you manage your client testimonials
Get testimonial widget now for Elementor along with a fully responsive & mobile friendly interface to help you manage your client testimonials
Get testimonial widget now for Elementor along with a fully responsive & mobile friendly interface to help you manage your client testimonials
FAQ About ETA
What will I learn in Agentic AI Systems?
You will learn how to design, build, and deploy autonomous AI agents that can plan, reason, and execute complex real-world tasks.