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Cohort 132 - (Da/Ds/GenAi/AI - AT)
Course content
54 sections | 122 lessons
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link. passcode and updates section
1 Lessons
Orientation Call & Basics of Data Analytics/Science
2 Lessons
31st August
3 Lessons
- Loading Data from txt files to Excel - Text to columns (different delimiters) - ADDRESS Function - VLOOKUP - HLOOKUP
2 Lessons
- LOOKUP - XLOOKUP - Creating Pivot Table - Creating Reports via Pivot - Basics about Charts in Excel
2 Lessons
- Report Building And Filters in Pivot - Pivot Charts - Data Analysis - Correlation - Regression
2 Lessons
- Co-Variance - Moving Average - Descriptive Stats - Goal Seek
2 Lessons
- Scenario Manager - Introduction to Power Query tool - Walk through Power Query - Importing data and transformations using Power Query
2 Lessons
- Add column options - Merging Queries - Appending Queries - Load data from live website - Introduction to type of data - Descriptive stats
2 Lessons
- Mean, Median, Mode - Variance, Standard Deviation - Skewness - Kurtosis - Range, Quantiles - Population and Sample - Hypothesis Testing
2 Lessons
- t -test - Tables and Critical Value - Probability Distributions - Z-score - Introduction to Python Programming - Python Installation
2 Lessons
- Basics of Python Coding - Variables in Python - Basic data types - Arithmetic Operator - Comparison Operator - Logical Operators
2 Lessons
- Bitwise Operators - Membership Operators - Identity Operator - Control Flow Statements - IF, ELIF, ELSE, Nested IF
2 Lessons
While and For Loop - Range Function - Break, Continue and Pass statements - Indexing in Python (positive and negative) - Introduction to String data type
2 Lessons
String practice with different methods - List practice with different methods
2 Lessons
- Finished List - Finished Tuple - Started with set
2 Lessons
- Finished Set - Finished Dictionary - Built-In Functions - Lambda Functions - User Defined Functions - args and **kwargs
2 Lessons
4th November
1 Lessons
Arguments Rule - Exception Handling - Assertion - Math and Random Module - Applying Statistics in Python
2 Lessons
Probability Distributions applied in Python - Series and it's functions - Pandas and it's functions
2 Lessons
- Pandas and related all functions - Reading and writing file in python - Joins - Implementation of joins in python
2 Lessons
- Arrays(1-d, 2-d, 3-d,etc.) - Numpy and it's functions - Arange, Linspace - Arrays Manipulation - Reshape, may_share_memory, etc.
2 Lessons
- Flatten and Ravel in numpy - Data Wrangling in Python
3 Lessons
- Finished Data Wrangling in Python - Creating different charts in Python using matplotlib and seaborn
2 Lessons
- Data Analysis in Python with different charts - Introduction to Database - MySQL Installation - DDL, DML, DCL,etc.
2 Lessons
Practiced DDL, DML commands in MySQL. - Create, Use, Show, Describe - Insert, Update, delete, - Drop, Truncate
2 Lessons
- Aggregate Functions - Order by, group by, Having - Subqueries - Joins - Window Functions
2 Lessons
- Normalization - Dimensional Modeling - Views, Stored Procedures - Data Analysis and Wrangling in MySQL - Temporary table creation - Database Administration
2 Lessons
Project: Case Study EDA
2 Lessons
Finished with Project - Introduction to powerbi and it's installation
3 Lessons
Overview of Powerbi components and features - How to create charts in powerbi - Formatting options for different charts
2 Lessons
- Advanced charts in PowerBI - Filters, Slicers - Conditional Formatting - Tables and Matrices - Creating Hierarchy - Drill Through options - Power Query in PowerBI
2 Lessons
- Power Query features including merge and append - Modeling in PowerBI - Relationship editor - DAX 8 Attachment(s)
2 Lessons
Drill Through Options - DAX Queries - Dashboard- blinkit
2 Lessons
Finished Blinkit Dashboard 2 Attachment(s) Download as Zip
2 Lessons
Powerbi deployment & Cloud -theory - Amazon dashboard for project - Reading xml, json files in powerbi - Introduction to Machine Learning - Linear Regression
2 Lessons
- House price prediction using linear regression - Cross Validation Techniques - Evaluation metrics for Linear Regression - Logistic Regression
2 Lessons
- Finished Logistic Regression - K Nearest neighbour - Created model using KNN - Decision Trees
2 Lessons
Random Forest - Clustering - K-means and Hierarchical Clustering - Practical Implementation
2 Lessons
NLP - Project on NLP - Deep Learning concepts - Introduction to AI and Cloud Computing
3 Lessons
Streamlit - Simple App creation - Machine Learning in Streamlit - Model Deployment - SDLC 10 Attachment(s)
2 Lessons
- Github profile creation - Maintenance, tags, commit changes, etc. - Capstone Project
2 Lessons
Deep Learning & Gen AI
2 Lessons
Deep Learning & Gen AI
2 Lessons
Deep Learning & Gen AI
2 Lessons
Deep Learning & Gen AI
1 Lessons
Deep Learning & Gen AI
2 Lessons
Deep Learning & Gen AI
2 Lessons
Career Building Session
2 Lessons
Resumes building and Sample Interview Questions
16 Lessons
GEN AI session
2 Lessons
GEN AI session
2 Lessons
GEN AI session
2 Lessons
18th March
1 Lessons
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