Statistics is a fundamental part of data science. It provides the mathematical foundations for understanding and analyzing data, as well as the tools for building models and making predictions. Data scientists use statistical methods to clean, organize, and analyze data, and to build and evaluate models. They use these models to make predictions and draw insights from data. Additionally, they also use statistical techniques to validate their findings and evaluate the uncertainty in their predictions. Statistics and machine learning are closely related fields. Both are used to make predictions and gain insights from data. Machine learning is a subset of artificial intelligence that uses algorithms and statistical models to enable systems to learn from data and improve their performance over time. Statistics provides the mathematical foundations for many machine learning algorithms. For example, statistical methods such as probability and regression analysis are used in supervised learning to train models on labeled data. Unsupervised learning algorithms such as clustering and dimensionality reduction also heavily rely on statistical concepts. Additionally, statistical techniques like hypothesis testing and cross-validation are used to evaluate and validate the performance of machine learning models. In summary, Statistics provides the mathematical and conceptual background for many machine learning algorithms and techniques, which are used to understand and make predictions from data in a automated way. Statistics and Python programming are closely related in the field of data science and machine learning. Python is a popular programming language for data analysis and scientific computing, and it has many libraries and frameworks for statistical analysis and machine learning, such as NumPy, pandas, matplotlib, seaborn, scikit-learn and statsmodels. Python libraries like NumPy and pandas provide powerful tools for manipulating and analyzing data. For example, NumPy provides functions for performing mathematical operations on arrays of data, while pandas provides data structures and data analysis tools that are particularly useful for working with tabular data. Python libraries such as matplotlib and seaborn are useful for data visualization. They provide functions for creating various types of plots and charts, which can be used to visualize data and gain insights from it. statsmodels is a Python library for estimation and statistical modeling, it provides functions for fitting various statistical models, including linear regression, time series analysis, and more. In summary, Python programming and statistics are closely related in data science and machine learning. Python provides a wide range of powerful libraries and frameworks for statistical analysis and machine learning, which make it an excellent choice for data analysis and modeling. This course is designed to teach the learner basic statistics using the Python programming language. there are three main sections to this course, being:-1. Learn basic statistics to excel in Data Science,2. Code basic statistical formulas in Python, and3. Learn basic statistics for machine learning projects. In the first section, the student will learn basic statistics, probability, estimation, and hypothesis testing. In the second section of the course, the student will learn how to code in Python many statistical formulas, as well as cover important concepts like the five values, measure of centre, measure of variability, normalisation and standardisation, and more. In the third section of the course, the student will learn how to incorporate statistics into his machine learning projects. In the project, his work will include analysing data, visualisations, descriptive statistics, plotting graphs, correlation, proving the central limit theorem, confidence interval, linear regression, and hypothesis testing.