Section 1: Getting Started With Python

Lecture 1: Data Types In Python

• Overview of different data types: integers, floats, strings, lists, tuples, sets, dictionaries.

• Practical examples and exercises to illustrate each data type.

• Common operations and methods for each data type.

Section 2: Python Basic Constructs

Lecture 2: Functions

• Definition and syntax of functions in Python.

• Writing simple functions and understanding function parameters.

• The concept of return values and scope.

• Practical examples and exercises.

Section 3: Introduction To NumPy

Lecture 3: Performing Mathematical Functions Using NumPy

• Overview of NumPy and its importance in scientific computing.

• Basic operations using NumPy arrays.

• Mathematical functions and operations with NumPy.

• Examples and exercises demonstrating these functions.

Section 4: NumPy Advanced

Lecture 4: NumPy Vs List

• Differences between NumPy arrays and Python lists.

• Performance comparison and use cases.

• Practical examples to illustrate the differences.

Lecture 5: SciPy Introduction

• Introduction to SciPy and its ecosystem.

• Key modules and functionalities in SciPy.

• Examples of using SciPy for scientific computations.

Lecture 6: Sub-Package Cluster

• Detailed look into the sub-packages within SciPy.

• Focus on the cluster sub-package for clustering data.

• Practical examples and exercises.

Section 5: Data Manipulation Using Pandas

Lecture 7: Introduction To Pandas

• Overview of the Pandas library.

• Importance of data manipulation in data science.

• Basic data structures in Pandas: Series and DataFrame.

Lecture 8: DataFrame In Pandas

• Creating and manipulating DataFrames.

• Indexing, selecting, and filtering data.

• Practical exercises to create and manipulate DataFrames.

Lecture 9: Merge, Join And Concatenate

• Techniques to combine data in Pandas.

• Using merge, join, and concatenate functions.

• Practical examples and exercises.

Lecture 10: Importing And Analyzing Data Set

• Methods to import data from different sources.

• Initial analysis and exploration of data.

• Practical exercises on importing and analyzing datasets.

Lecture 11: Cleaning The Data Set

• Importance of data cleaning.

• Techniques for handling missing data, duplicates, and outliers.

• Practical examples and exercises.

Lecture 12: Manipulating The Data Set

• Advanced data manipulation techniques.

• Using apply, map, and groupby functions.

• Practical exercises to manipulate datasets.

Lecture 13: Visualizing The Data Set

• Basic principles of data visualization.

• Creating visualizations using Pandas built-in functions.

• Practical exercises on visualizing datasets.

Section 6: Data Visualization Using Matplotlib

Lecture 14: What Is Data Visualization?

• Definition and importance of data visualization.

• Different types of visualizations and their use cases.

Lecture 15: Introduction To Matplotlib

• Overview of Matplotlib library.

• Basic plotting functions and customization options.

Lecture 16: How To Create A Line Plot?

• Step-by-step guide to creating line plots.

• Customization options for line plots.

• Practical examples and exercises.

Lecture 17: How To Create A Bar Plot?

• Step-by-step guide to creating bar plots.

• Customization options for bar plots.

• Practical examples and exercises.

Lecture 18: How To Create A Scatter Plot?

• Step-by-step guide to creating scatter plots.

• Customization options for scatter plots.

• Practical examples and exercises.

Lecture 19: How To Create A Histogram?

• Step-by-step guide to creating histograms.

• Customization options for histograms.

• Practical examples and exercises.

Lecture 20: How To Create A Box And Violin Plot?

• Step-by-step guide to creating box and violin plots.

• Customization options for these plots.

• Practical examples and exercises.

Lecture 21: How To Create A Pie Chart And Doughnut Chart?

• Step-by-step guide to creating pie and doughnut charts.

• Customization options for these charts.

• Practical examples and exercises.

Lecture 22: How To Create An Area Chart?

• Step-by-step guide to creating area charts.

• Customization options for area charts.

• Practical examples and exercises.

Section 7: Statistics

Lecture 23: What Is Data?

• Definition and types of data.

• Data collection methods and sources.

• Practical examples to illustrate different types of data.

Lecture 24: Introduction To Statistics

• Basic concepts of statistics.

• Descriptive vs. inferential statistics.

• Practical examples and exercises.

Lecture 25: Sampling

• Importance of sampling in statistics.

• Different sampling methods.

• Practical examples and exercises.

Lecture 26: Probability

• Basic concepts of probability.

• Probability rules and theorems.

• Practical examples and exercises.

Lecture 27: Probability Distribution

• Types of probability distributions.

• Characteristics and applications of different distributions.

• Practical examples and exercises.

Lecture 28: Inferential Statistics

• Concepts of hypothesis testing and confidence intervals.

• Techniques for making inferences about a population.

• Practical examples and exercises.

Section 8: Machine Learning Using Python

Lecture 29: Types Of Machine Learning

• Overview of supervised, unsupervised, and reinforcement learning.

• Practical examples of each type.

Lecture 30: What Can You Do With Machine Learning?

• Applications of machine learning in various industries.

• Practical examples and case studies.

Lecture 31: Machine Learning Demo

• Demonstration of a simple machine learning project.

• Step-by-step guide to implementing a machine learning model.

• Practical exercises to build and evaluate a model.