What you'll learn
By the end of this course, learners will have a solid understanding of Python's core concepts, including data types, control structures, functions, and modules,
Learners will gain proficiency in using libraries such as NumPy and Pandas to perform complex data manipulation and analysis tasks, including data cleaning
Participants will learn to create a wide range of data visualizations using Matplotlib, from basic plots like line and bar charts to more complex visualizations
learners will have an introductory understanding of machine learning concepts and will be able to implement basic machine learning models

Requirements
While not required, familiarity with basic programming concepts (like variables, loops, and conditionals) can be helpful.

Description
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.
Who this course is for:
who need to utilize Python for coursework, research projects, or to develop a skill set that is highly sought after in various scientific and technical fields.