Advanced Diploma in Data science with AIML
Duration: -360 hrs. – (4 hrs./day – 2 hrs./day)
Intermediate
Modules
Foundational Modules
Database:
- RDBMS using MySQL
- 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 Fundamentals & Advanced ML
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
Sub-queries in SQL
Sub-queries in SQL
Normalization and de-normalization: Views and Temporary tables
Transactions in SQL
Transactions in SQL
SQL programming
Creating stored procedures, Cursors 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 Tre
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
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
