Introduction to Statistics

Learn the fundamental concepts of statistics, its importance in data science, and how it helps in understanding data patterns and making informed decisions.

Descriptive Statistics Fundamentals

Master measures of central tendency (mean, median, mode) and dispersion (variance, standard deviation), and data distribution techniques to summarize and describe data.

Practical Example – Descriptive Statistics

Apply descriptive statistics techniques to a real-world dataset to practice summarizing, analyzing, and visualizing key characteristics of data.

Inferential Statistics Fundamentals

Understand the core concepts of inferential statistics, including sampling, probability, and estimation, to make predictions about populations based on sample data.

Confidence Intervals

Learn how to construct and interpret confidence intervals to estimate population parameters with a defined level of confidence, a key tool in statistical inference.

Practical Example – Confidence Intervals

Work through practical examples to calculate and analyze confidence intervals for various real-world scenarios, reinforcing theoretical understanding.

Hypothesis testing

Understand the principles of hypothesis testing, including null and alternative hypotheses, p-values, and statistical significance, to test claims about populations.

Practical Example – Hypothesis testing

Conduct practical hypothesis tests on given datasets, interpret results, and draw conclusions, mirroring real-world data analysis challenges.

The Basics of Probability

Understand fundamental probability concepts, including sample spaces, events, and the rules of probability, essential for data science.

Combinatorics

Explore permutations, combinations, and other counting techniques crucial for calculating probabilities in complex scenarios.

Bayesian Inference

Delve into Bayesian probability and its application in updating beliefs with new evidence, a powerful tool in machine learning.

Probability Distributions

Study common discrete and continuous probability distributions (e.g., Binomial, Normal) and their significance in modeling random phenomena.

Probability in Other Fields

Explore the wide-ranging applications of probability in various fields, showcasing its versatility beyond pure data science.

Introduction to Linear Algebra (11 Sub-Modules)

Gain foundational knowledge of linear algebra, including vectors, matrices, and their operations, essential for understanding many data science algorithms. This module covers:

• What is a Matrix?
• Scalars and Vectors
• Linear Algebra and Geometry
• Scalars, Vectors, and Matrices as Python Arrays
• What is a Tensor?
• Addition and Subtraction
• Errors when Adding Matrices
• Transpose of a Matrix
• Dot Product
• Dot Product of Matrices
• Why is Linear Algebra Useful?