Data science with AIML- Tailored For Working Professionals
Durations – 360 Hrs.
Modules
Foundational Modules
Database:
- RDBMS using MySQL
Core Programming
- Python Programming & Advanced Python
- Problem Solving and Data Structures using Python
SPECIALIZATIONS
Data Analytics
- Advanced Excel
- Data Analysis & Reporting using Power BI
- Exploratory Data Analysis with Pandas
Machine Learning
- Machine Learning Fundamentals & Advanced ML
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
- Pandas
- Matplotlib
- Seaborn
- scikit-learn (sklearn)
- TensorFlow
Core Programming
| RDBMS using MySQL – 40hrs. – 10 Days – 2Weeks/ 20 Days – 4Weeks | ||
|---|---|---|
| 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 |
| Python Programming & Advanced Python – 80 hrs. – 20 Days – 4 weeks/ 40 Days – 8Weeks | ||
| Introduction to Python | Python Data types and Conditions | Control Statements |
| Python Functions | Default arguments | Functions with 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 |
| Problem Solving and Data Structures using Python – 60 hrs. – 15 Days – 3 weeks/ 30 Days – 6Weeks | ||
| Time and Space Complexity | Utopian Tree | Viral Advertising |
| Birthday Cake Candles | Migratory Birds | Kaprekar Number |
| Pangram String and Anagram String | Palindrome Index | Array Rotation |
Analytics Specialization
| Advanced Excel – 20 hrs. – 5 Days – 1 weeks/ 40 Days – 8Weeks | ||
|---|---|---|
| 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 |
Data Analysis & Reporting using Power BI – 40 hrs. – 10 Days – 2 weeks/ 20 Days – 4Weeks
| 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 |
Exploratory Data Analysis with Pandas – 40 hrs. – 10 Days – 2 weeks/ 20 Days – 4Weeks
| 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 |
An end-to-end machine learning model development using scikit-learn and real-world datasets
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 Neighbor 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 |
