• Installation and Setting up path
  Features
• Python variables
• Input & Output and Import
• Why Learn Python
• Who used Python

• Using PyCharm IDE

• Python Scripting

• Keywords and Identifiers

• Operators

• Indentations

• Integer (int)

• Float (float)

• String (str)

• Boolean (bool)

• Complex Numbers (complex)

• Tuple (tuple)

• List (list)

• Dictionary (dict)

• Set (set)

• Arrays (array)

• String

• String Slicing

• Date and Time (datetime module)

• Math Module

• Random Module

• Statistics Module

• PDF Data Extraction (PyPDF2, pdfplumber)

• CSV Module

• Introduction to Tuples

• Working with Tuples

• Tuple Operations

• Tuple Functions and Methods

• Introduction to Lists

• Working with Lists

• List Operations

• List Functions and Methods

• Introduction to Sets
• Working with Sets
• Sets Operations
• Function and Methods

• If...else

• Elif (Else if)

• For Loop

• For...else Loop

• While Loop

• Break and Continue

• Pass Statement

• Looping Techniques

• Types of Functions

• Function Arguments

• Recursion

• Anonymous Function

• Global, Local, and Nonlocal Variables

• Lambda Functions

• Modules

• Packages

• Reading & Writing Files

• Manipulating File Pointer

• Types of Files

• File Operations

• Directories

• File I/O Attributes

• File Methods

• Try, Except and Finally

• Try...else

• Custom Exception

• Error vs. Exception

• OS Module

• Environment Variables

• Paths, Directories, and Filenames

• Working with File Systems

• Dates and Times

• OS Module

• Environment Variables

• Paths, Directories, and Filenames

• Working with File Systems

• Dates and Times

• Pattern Matching and Searching

• Real-time Parsing of Networking or System Data using Regex

• Regex Validation Concepts

• Web Scraping

• Chatbot & Language Translation

• Finding the Similarity Ratio Between Text

• Tagging Sentence to Find Keywords

• Google Map Integration

• Bubble Sort Algorithm

• QR Code Generator

• Spell Checker

• Scraping Wikipedia

• Anagram & Screenshot App

• Python for Networking

• Getting Input from User

• Working with Images & Building a Photo Gallery

• NumPy Arrays

• Data Types in NumPy

• Array Indexing

• Array Slicing

• Array Shape

• Array Reshape

• Array Iteration

• Array Join

• Array Split

• Array Search & Sorting

• Array Filter

• Introduction to Pandas

• Pandas Series

• Pandas DataFrames

• Pandas Read CSV

• Pandas Read JSON

• Introduction
• Pyplot Plotting
• Markers Line Labels
• Grid
• Subplot Scatter Plot
• Bar Chart
• Histogram
• Pie Chart

• Introduction
• Introduction about Data Science,
• What is
  Data
• Database Table

• Central Tendency

• Max, Min, Mean Functions

• Mean

• Median

• Mode

• Skewness

• Normal Distribution

• Probability Basics

• What is Probability?

• Types of Probability

• Odds Ratio

• Standard Deviation

• Data Deviation & Distribution

• Variance

• Bias variance Tradeoff
  Underfitting 
• Overfitting
• Distance metrics
  Euclidean Distance
• Manhattan Distance

• Outlier Analysis

• What is an Outlier?

• Interquartile Range (IQR)

• Box & Whisker Plot

• Upper Whisker

• Lower Whisker

• Scatter Plot

• Cook’s Distance

• Missing Value Treatment

• What is NA?

• Central Imputation

• KNN Imputation

• Dummification

• Correlation

• Correlation Coefficient

• Correlation Matrix

• Statistics: Correlation vs Causality

• Data Science Math

• Linear Functions

• Plotting Linear Functions

• Slope and Intercept

• Descriptive Statistics

• Grouping of Data

• Handling Missing Values in Dataset

• ANOVA – Analysis of Variance

• Correlation

• Error Metrics

Classification:

• Confusion Matrix

• Precision

• Recall

• Specificity

• F1 Score

Regression:

• Mean Squared Error (MSE)

• Root Mean Squared Error (RMSE)

• Mean Absolute Percentage Error (MAPE)

• Introduction to Machine Learning

• Data Set in Machine Learning

• Data Types in Machine Learning

• Median

• Mode

• Percentile

• Data Distribution

• Big Data Distribution

• Normal Data Distribution

• Linear Regression

• Logistic Regression

• Naive Bayes

• Random Forest

Supervised Learning Models:

• K-Nearest Neighbors (KNN)

• Support Vector Machine (SVM)

• Decision Tree Classification

• Polynomial Regression

• Multiple Regression

• Categorical Data Handling

Model Evaluation and Optimization:

• Scale (Feature Scaling)

• Grid Search

• AUC-ROC Curve

• Confusion Matrix

• Bootstrap Aggregation (Bagging)

• Cross Validation

Other Concepts:

• Natural Language Processing (NLP)

• Hierarchical Clustering

• K-Means

• K-Means++