Python DataAnalytics

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Introduction to Data Analytics: Importance, Uses and Examples
Overview of Python Libraries for Data Analytics
Setting up the Python Environment for Data Analytics
Introduction to NumPy: Arrays, Array Operations, Mathematical Functions, Indexing and Slicing

Descriptive Statistics: Mean, Median, Mode, Variance, Standard Deviation
Introduction to Probability and Probability Distributions: Normal, Binomial, etc.
Inferential Statistics: Hypothesis Testing, p-value, t-test, chi-square test
Correlation and Regression Analysis
Using SciPy for Statistical Analysis

Introduction to Data Visualization with matplotlib and seaborn
Basic Plotting with matplotlib: Line Plots, Scatter Plots, Bar Plots, Histograms
Plotting with seaborn: Statistical Data Visualization
Customizing Plots: Colors, Labels, Legends, Titles

Introduction to Relational Database
1. Anatomy of Database Systems
2. Client/Server Architectures
3. Distributed Databases
ER Modelling
1. Entity
2. Relationships
3. Types of Entities and their Relations
4. Design Considerations
5. Applications
Relational Modelling
1. Introduction to Relational Algebra
2. Anatomy of Relational Model
3. Design Issues
4. Applications
Structured Query Language
1. Introduction to SQL
2. Data Definition
Data Manipulation
1. SQL constructs
  1. Select
  2. Insert
  3. Update
  4. Delete
2. Expressions
3. Grouping and Aggregate Functions
4. Different types of Joins
5. Sub-Queries and Co-related Queries
6. Set Operations
Interacting with Database.
1. SqlLite
2. Connections
3. Operations
4. Login Framework

Introduction to Web
1. HTTP
2. Client-Server Architecture
3. Dynamic Content Generation
Web Page
1. Introduction to HTML
2. Introduction to CSS
3. Introducton to JS
Flask
1. Routes
2. Route Handlers
3. Forms
4. Blueprint
5. PageFlow
6. Session Management
7. Login Module
Restful Interface

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