- Practical hands-on learning
- Technical mentorship
- Career Coaching
- Placement Support
- Welcome
- Orientation 01:01:11
- Orientation PDF
- ENVIRONMENTAL SETUP
- Environmental Set Up - Video 01:01:46
- Git Basics
- GIT BRANCH
- GIT AND GITHUB INTRO
- ASSIGNMENT-1: GIT
- Git Basics 00:00:00
- Jupyter shortcut keys
- Python-Variables, Datatypes and Operators
- python Decision Making
- Python -Iterative statements
- Python- User Input
- Python strings
- Python Basics- Video 01:28:17
- Role of Statistics in Data Science
- Population and Sample
- Types of Data in Statistics
- Measures of Central Tendency
- Quantiles and Skewness
- Measures Of Dispersion
- Scatter Diagram
- Youtube Link for Lecture
- Application of Statistics in DS 02:02:14
- Python Data Structure 00:30:56
- Python Functions 00:30:55
- Python Functions Lambda Map Filter Reduce 00:42:27
- Python OOPS 00:32:46
- Numpy & Pandas 01:02:04
- Numpy & Pandas EDA 00:44:13
- Visualization EDA 01:50:00
- SQL-CREATE DB,TABLE AND CONSTRAINTS
- sql select
- SQL 1 00:41:21
- SQL 2 00:41:22
- SQL 3 Joins 00:37:02
- SQL 4 Views 00:50:34
- SQL 5 TRIGGERS 00:38:03
- Web Scrapping 00:36:06
- Flipkart Web Scraping 00:26:24
- Data Cleaning and Summarising
- Overview
- Assessment Learning Outcomes
- Assessment Details
- Task 1: Data Preparation
- Task 1: Data Preparation (Contd.)
- Task 2: Report
- Assessment Criteria
- Assessment Criteria (Contd.)
- Submission Format
- Academic Integrity and Plagiarism
- Dataset & Dataset_Information
- Mini Project 1 00:19:02
- GitBash Navigation Mini Project 00:30:34
- Iframe1
Section 1- Describes what data science, its applications, process and its environment setup.
Section 2- Gains hands on practice with python basics like operators, variables, conditional statements, python data structures, OOPS, iterators, decorators, file handling, modules needed for data science.
Section 3- This Introduces you to the mathematical concepts that needed for the course like statistics-descriptive, inferential, probability and distributions, hypothesis testing and A/B testing.
Section 4-Gains a hands on practice with python numpy, pandas, matplotlib and seaborn which are needed for data analytics to import datasets, analyze and visualize.
Section 5 and 6- Learn about scikit-learn, EDA , feature Engineering, machine learning models, evaluation metrics in a detailed manner.
Section 7- It covers Deep learning concepts, NLP concepts, web scraping, JSON, api, and Big data, SQl and NO SQL.
Section 8- Capstone Project- Students synthesize and apply knowledge which they gained throughout the course in data science programming for working with datasets and get the conclusion of the datasets.
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