Data science with AIML- Tailored For Working Professionals
Durations – 360 Hrs.
Modules
Foundational Modules
Database:
- RDBMS using MySQL
- Python Programming & Advanced Python
- Problem Solving and Data Structures using Python
Data Analytics
- Advanced Excel
- Data Analysis & Reporting using Power BI
- Exploratory Data Analysis with Pandas
- Capstone project – Data Analysis
- Machine Learning Fundamentals & Advanced ML Certification – Diploma in Data Science with AIM
Experiential Project-Based Learning
- An end-to-end machine learning model development using scikit-learn and real-world datasets
Projects:
- Apply statistical methods to make decisions in various business problems, including banking, stock market, etc.
- Apply regression to predict future flight prices
- Apply classification to classify customers
- Use clustering to segment banking customers
- Gesture recognition captured through image or video data
Software & Framework required
- Anaconda Distribution (Jupyter, Spyder)
- Google Colab
- PyCharm
- MySQL
- Microsoft Excel
- Power BI
- Libraries: Pandas, Matplotlib, Seaborn
- ML Libraries: scikit-learn (sklearn), TensorFlow
| Core Programming | ||
|---|---|---|
| RDBMS using MySQL – 40 hrs. – 10 Days – 2 weeks | ||
| Introduction to databases and RDBMS, | Database creation, concept of relation and working examples | Creating tables. Design view of the table, Alter table operations & Key Constraints |
| Read, update and delete operations on tables. Working with nulls | Querying tables: Select statement, examples and its variations | Filtering, Sorting, Predicates and working examples |
| Joins in SQL and working examples | Insert, Update, delete operations and working examples | Scalar functions in SQL and working examples |
| SQL set-based operations and data aggregation Sub-queries in SQL |
Normalization and de-normalization: Views and Temporary tables Transactions in SQL |
SQL programming Creating stored procedures, Cursors in SQL |
| Learners Outcome | ||
| Design, query, and manage relational databases using SQL with transactions, procedures, and data integrity constraints. | ||
| Python Programming & Advanced Python – 80 hrs. – 20 Days – 4 weeks | ||
| Introduction to Python | Python Data Types and Conditions | Control Statements |
| Python Functions | Default arguments | Functions with a variable number of args |
| Scope of Variables | Global specifier | Working with multiple files |
| List and Tuple | List Methods | List Comprehension |
| Map and filter functions | String | List comprehension with conditionals |
| Set and dictionary | Exception Handling | File Handling |
| Object-Oriented Programming | Overloading Operator | Inheritance |
| Regular Expression | Finding Patterns of Text | Meta characters |
| Testing Fundamentals | Unit Testing | Working with JSON |
| Decorators | Iterators | Generators |
| Learners Outcome | ||
| Develop robust Python applications using core, advanced, and object-oriented concepts with real-world data handling. | ||
| Problem Solving and Data Structures using Python – 40 hrs. – 10 Days – 2 weeks | ||
| Time and Space Complexity | Utopian Tre | Viral Advertising |
| Birthday Cake Candles | Migratory Birds | Kaprekar Number |
| Pangram String and Anagram String | Palindrome Index | Array Rotation |
| Analytics Specialization | ||
| Advanced Excel – 40 hrs. – 10 Days – 2 weeks | ||
| Introduction to MS-Excel | Fill Series, Flash Fill | Logical Functions – IF, AND, OR, NOT, IF Error |
| Text Functions | Date Functions | Statistical Functions |
| VLOOKUP and H-Lookup | Index and Match Functions | Sorting and Filtering Data |
| Pivot Table | Data Validation | What-if Analysis |
| Charting techniques in Excel | Interactive dashboard creation | Data analytics project using Excel |
| Learners Outcome | ||
| Perform data analysis, reporting, and dashboard creation using advanced Excel functions and analytical tools. | ||
| Data Analysis & Reporting using Power BI – 40 hrs. – 10 Days – 2 weeks | ||
| Introduction to Power BI | Getting started with Power BI Desktop | Data modelling in Power BI |
| Creating visualization | Advanced data transformation | Power BI Dashboards |
| Data Visualization Best practices | Table and Conditional Formatting | Data Cleaning and Transformation |
| Learners Outcome | ||
| Build interactive dashboards and business reports using data modeling, visualization, and transformation techniques | ||
| Exploratory Data Analysis with Pandas – 40 hrs. – 10 Days – 2 weeks | ||
| NumPy | Vectorization | Broadcasting |
| Slicing of Matrices | Filtering | Array Creation Functions |
| NumPy Functions across axis | Stacking of arrays | Matrix Calculation |
| Pandas Series | Data Cleaning | Handling Missing Data |
| Pandas Data frame | Selection Data (loc, iloc) | Filtering Data Frames |
| Working with Categorical Data | Grouping & Aggregation | Merging Data Frame (concat, merge) |
| Sorting Data Frames | Importing csv files | Importing Excel Files |
| Creating graphs using Matplotlib | Customizing Plots | Seaborn, PlotLy |
| Learners Outcome | ||
| Analyze, clean, and visualize large datasets using NumPy, Pandas, and Python visualization libraries. | ||
| AI Specialization | ||
| Machine Learning Fundamentals & Advanced ML – 80 hrs. – 20 Days – 4 weeks | ||
| Introduction to Machine Learning | Regression | Logistic regression |
| Supervised machine learning | Simple linear regression | Naïve Bayes Classification |
| Unsupervised machine learning | Multiple linear regression | Decision tress and its types |
| Train test split the data | Performance measure for regression | K Nearest Neighbour Classification |
| ML Workflow for project implementation | Classification and types | Performance Measure for Classification |
| Random Forest, | Clustering and types | Evaluate clustering results, Elbow Plot |
| Optimizing regression models with forward elimination, grid search cv | Improving classification models with Ensemble modeling | Model evaluation strategies (KFold, Stratified KFold) |
| Regularization L1 and L2 regularization | Bagging | Boosting techniques: ADA boost |
| Hyperparameter Tuning, SVM | Stacking and Voting | Dimensionality Reduction with PCA |
| Learners Outcome | ||
| Build, optimize, and evaluate machine learning models using supervised, unsupervised, and ensemble techniques | ||
| Capstone project – Data Analysis – 20 hrs. – 5 Days – 1 week | ||
| Certification – Diploma in Data Science with AIML | ||
