Data Science and AI/ML Course with Certification
Turn Data into Opportunities. Learn how to analyze, interpret, and visualize data using industry-relevant tools and production projects. With expert mentors and practical training, build skills that companies actually need.
Start date: 7th Sept
Start date: 28th Sept
- Batch Mode: Classroom or Online
- Languages: English/ Hindi
Industry-Ready
Curriculum perfectly mapped to match current requirements across data engineering architectures
Practitioner Driven
Hands-on assignments, clean sandbox environments, and active real-world scenarios
Placement Focus
End-to-end guidance from dedicated mock runs, profile upgrades to corporate networks
Explore the Program Curriculum
A comprehensive curriculum built from foundational data layers to high-performance machine learning models
Fundamentals of Analytics
- Business Intelligence & Data Warehouse concepts
- BI / DW Lifecycle
- Business owner & consultant roleplay
- Data Modeling (Dimensional Modeling)
- Data Visualization Workshop
- Analytics Demo
- Career Opportunities discussion and guidance
- Steps to be carried out
Excel Analytics – Spreadsheet Analytics
- Fundamentals and Excel interface
- Data population and error handling
- Excel features / Data quality
- Data Protection
- Excel functions
- Pivoting
- Charts
Database and SQL
- Database Concepts
- Standard SQL Query Writing
- Advanced SQL Query Writing Techniques
Python Programming
- Setting Python in Windows
- Introduction to print()
- Variable and Data Type
- Enumerators, operators, building basic expressions
- More print(), input()
- String data type indexing and slicing
- Program flow controls
- Loops
- Data structures – List, Dictionary, Tuple, Set
- Introduction to functions
- Exception Handling
- Object Oriented Programming concepts
- Reading from text files and writing to text files, .csv files
- URL Libraries
AI ML Using Python - Part 1
- NumPy
- Dataframes using pandas library
- Datasets
- Introduction to Machine Learning
- Classification
- Dimensionality
- Types Of Supervised Learning
- Probability
- Decision Trees
- Feature Selection
- Sampling And Evaluation
- Linear Regression
- Logistic Regression
- Clustering - KMeans
AI ML Using Python - Part 2
- Data Preprocessing and Sampling Standard Scaling, Normalization
- Feature Selection, Extraction-Select KBest, PCA, chi2-squared
- Gradient Descent with reprect to Linear Regression
- Evaluations, Confusion Matrix, KCross Validation
- Support Vector Machines
- Ensemble Learning, Random Forest, Stacking, Boosting etc
- Clustering
- KNN
- Handling Categorical Data
- Artificial Neural Network - PLA, BackPropogation, MLP Classifier
Key Features of the Program
Industry-Aligned
Built precisely based on real working criteria.
Expert Faculty
Delivered by domain specialists with engineering lineage.
Practitioner View
Deep simulations based on production workloads.
Certification
Course completion credentials verifying core mastery.
Profile Overhauls
Dedicated resume writing & engineering layout optimizations.
Mock Interview Drill
Extensive evaluation rounds targeting engineering loops.
Placement Focus
Complete structural pipeline assistance into key channels.
Internships
Opportunities to track and resolve data model issues.
Job Roles
Who Should Enroll?
This course is designed for individuals from diverse academic and professional backgrounds, including:
Our teaching approach is highly adaptable and learner centric. We have successfully delivered this course to a wide audience—ranging from a 6th-grade student to an 82-year-old professional— proving that with the right guidance, data science can be learned by anyone, regardless of age or background.