Advanced Diploma in Data science with AIML
Duration – 360 hrs. – (4 hrs./day – 2 hrs./day)
Modules
Foundational Modules
Database:
- RDBMS using MySQL – 40hrs.
- Python Programming & Advanced Python – 80 hrs.
- Problem Solving and Data Structures using Python – 60 hrs.
- Advanced Excel – 20 hrs.
- Data Analysis & Reporting using Power BI – 40 hrs.
- Exploratory Data Analysis with Pandas – 40 hrs.
SPECIALIZATIONS
Machine Learning
- Machine Learning Fundamentals & Advanced ML – 80 hrs.
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 decision in various business problems, including bank, stock market etc.
- Apply regression to predict future flight price
- Apply classification to classify customer
- Use clustering to cluster banking customers
- Gesture recognition captured through image or video data
Software & Framework required
- Anaconda Distribution Jupyter, Spyder, Google Colab, Pycharm
- MySQL
- Microsoft Excel
- POWERBI.
- Libraries: Pandas, Matplotlib, Seaborn
- ML Libraries: 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 | Normalization and de-normalization: Views and Temporary tables | SQL programming |
| Sub-queries in SQL | Transactions in SQL | 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 trees and its types |
| Train test split the data | Performance measure for regression | K Nearest Neighbor Classification |
