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Cohort 122 - (Da/Ds/GenAi - VC)
Course content
49 sections | 107 lessons
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Link, Passcode & Updates Section
1 Lessons
What is analytics ? A) Introduction to data qnalytics B) Types of analytics C) Scope and role of analytics in business. D) Fundamentals of data
2 Lessons
Basics of Excel - A) Introduction to Excel B) Datatypes in excel C) Basic analysis logical aptitude. D) Fundamentals analysis.
1 Lessons
3)Basics of Excel ? A) Formatting to Excel B) Functions in excel C) Data cleaning in Excel.
2 Lessons
Excel advanced functions and project. a) Beginner end to end excel project with dashboard. b) Advances excel functions. c) SUMIF,SUMIFS,COUNTIF,COUNTIFS,Pivot charts d) Dashboard creation in excel.
2 Lessons
1) Basics of SQL 2) Need of SQL
1 Lessons
1]Basic intuition and understanding of querying data and its applications. 2) Select , aggregate functions, group by , order by , where, comparison operators, Cast, Replace , Round , Floor , ceil.
1 Lessons
SQL functions
1 Lessons
10th May
1 Lessons
11th May
1 Lessons
1. Database basics 2. Excel basics for Excel project
2 Lessons
1. Excel Project - part 2 2. Database design (Relationships, Normalization, ER Diagram)
2 Lessons
1. Create database 2. Create table 3. Alter Table
2 Lessons
1. Add data to table: INSERT, LOAD FILE, using UI 2. Delete and Truncate data 3. Update existing data in the table 4. Basic Select statement
2 Lessons
WHERE Clause in SQL Query: Logical, Relational, BETWEEN, IN, LIKE
2 Lessons
1. Project 2 (SQL Project 1) 2. JOINS - complete tutorial 3. Aggregate Functions 4. Having 5. Group By 6. Order By
2 Lessons
1. Triggers 2. Views 3. Temporary Tables 4. Indexes 5. Windows functions in SQL
4 Lessons
1. Introduction to programming 2. Introduction to algorithms 3. Python installation
2 Lessons
1. Python basics 2. f string 3. variables 4. data types
2 Lessons
1. Escape sequence 2. end command in Python 3. Input() 4. Operations (Arithmetic operators & Relational operators)
2 Lessons
1. Logical operators 2. Membership operators 3. Conditions in Python 4. Nested conditions in Python
2 Lessons
1. Examples in IF Condition 2. Loops discussion 3. For loop explanation and examples 4. While loop explanation
2 Lessons
Practice examples based on Conditions and loops in Python
2 Lessons
1. Wrapping up loops and condition 2. Match Case 3. Strings in Python
2 Lessons
1. String (finishing) 2. List 3. Tuple 4. Dictionary (start)
2 Lessons
1. Dictionary in Python 2. Sets in Python
2 Lessons
1. Functions in Python : a. Return type b. Arguments: required, positional, default, keyword, variable length c. docstring 2. Modules 3. One line loops 4. One line conditions 5. One line Functions
2 Lessons
1. Map, Filter, Reduce in Python 2. Class & Objects : members, __init__()
2 Lessons
1. Inheritance and Encapsulation in Python 2. Exception handling in Python 3. Working with Database (MYSQL) from Python
2 Lessons
1. Working with files with Python 2. Statistics: a. Measure of central tendency - Mean, Median, Mode b. Measure of variability: Range, IQR, Variance, Standard Deviation c. Working with Single data points
2 Lessons
1. Statistics: Visualizing and Analysing 2 variables 2. Python: Numpy, introduction to Pandas
2 Lessons
1. Numpy 2. Pandas 3. Data Visualization 4. Projects for next 2 weeks - Complete Data analytics coverage with the project
2 Lessons
1. data cleaning and preprocessing in Python 2. Introduction to Power BI
2 Lessons
* Data Transformation * DAX Queries * Visualization * Projects
2 Lessons
1. Introduction to Machine Learning and different machine learning algorithms 2. Data cleaning in Machine Learning
2 Lessons
Simple Linear Regression 2. Multiple Linear regression 3. Support vector machine regressor 4. Evaluate regression models (MAE, MSE, RMSE, R Square)
2 Lessons
1. Polynomial regression 2. Ridge, Lasso, Elasticnet regression 3. Classification also intro 4. Classification model evaluate (Confusion matrix, Accuracy score, Classification report) 5. Models: Logistics regression, Support vector classification
2 Lessons
1. Classification algorithms: Decision tree, random forest, K Nearest Neighbor 2. Measuring distances in ML 3. Introduction to NLP
2 Lessons
1. Natural Language Processing: Data Processing, WordCloud Analytics, Sentiment Analysis 2. Github 3. Clustering: KMeans
2 Lessons
1. Hierarchical Clustering 2. Model Deployment 3. Understanding Time Series Data 4. Introduction to Deep Learning 5. Advanced Excel 6. all projects
2 Lessons
Gen AI Session
2 Lessons
Gen AI Session
2 Lessons
Gen AI Session
2 Lessons
Gen AI Session
2 Lessons
11th October - Deep Learning Session
2 Lessons
12th October - Deep Learning session
2 Lessons
Career Building Sessions
3 Lessons
27th October
1 Lessons
Resumes building and Sample Interview Questions
16 Lessons
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