Data Science Statistics: Hypothesis Testing & Data Analysis Mastery

What you will learn:

Master core statistical concepts and their applications.
Utilize Python for effective data manipulation and visualization.
Understand and apply descriptive statistics effectively.
Comprehend probability theory, including Bayes' theorem.
Master various probability distributions and their applications.
Perform inferential statistics for drawing data-driven conclusions.
Apply a range of hypothesis tests (t-tests, chi-squared tests, ANOVA).
Interpret statistical results and draw meaningful insights.
Communicate findings effectively using appropriate data visualizations.
Build a strong foundation for further advanced studies in data science.

Description

Unlock the power of data with our comprehensive course, "Data Science Statistics: Hypothesis Testing & Data Analysis Mastery." This course transforms you into a confident data analyst, equipping you with the statistical skills and Python proficiency needed for a successful data science career.

Dive deep into:

Foundational Statistics: Grasp core statistical concepts and their real-world applications.
Python for Data Science: Learn to manipulate, visualize, and analyze data using Python's powerful libraries.
Descriptive Statistics: Master techniques to summarize, understand, and present data effectively (mean, median, mode, variance, etc.).
Probability & Distributions: Gain a solid understanding of probability theory, including Bayesian methods and various probability distributions.
Inferential Statistics: Master techniques to draw meaningful conclusions from data samples.
Hypothesis Testing: Learn the fundamental principles of hypothesis testing, and apply various tests like t-tests, chi-squared tests, and ANOVA.
Data Visualization: Effectively communicate your findings through data visualization best practices.

Whether you're a beginner or looking to enhance your expertise, this course will empower you to make data-driven decisions with confidence. Start your data science journey today!

Curriculum

Introduction to Statistical Foundations

This section lays the groundwork for your data science journey. You'll explore the importance of statistics in data analysis, learn about the different types of data, and receive an introduction to Python for data science. The initial lectures focus on introducing basic statistical concepts and the importance of statistics in various fields. This section includes a quiz to reinforce your understanding of these fundamental concepts.

Descriptive Statistics: Unveiling Data Patterns

Here, you'll delve into descriptive statistics, mastering essential techniques for summarizing and interpreting data. Learn to calculate and interpret measures of central tendency (mean, median, mode), measures of spread (range, variance, standard deviation), measures of dependence (correlation, covariance), and measures of shape and position (quartiles, percentiles). You'll also learn about standardizing data and calculating z-scores. A quiz will test your understanding of these key descriptive statistical measures.

Probability & Random Variables: Understanding Uncertainty

This section covers fundamental concepts in probability. You'll explore basic probability, set theory, conditional probability, and Bayes' Theorem, using both theoretical concepts and practical examples. Lectures cover permutations, combinations, and the role of random variables. The section concludes with an introduction to probability distribution functions and a quiz to assess your knowledge of probability concepts.

Inferential Statistics: Drawing Conclusions from Data

This section focuses on inferential statistics. You'll study the normal distribution, skewness, kurtosis, statistical transformations, the relationship between sample and population means, and the Central Limit Theorem. Lectures also cover bias and variance, maximum likelihood estimation, confidence intervals, correlation, and sampling methods. The section culminates in a comprehensive quiz covering all these concepts.

Hypothesis Testing: Making Data-Driven Decisions

The final section dives into hypothesis testing. You'll master the fundamentals of hypothesis testing, and learn to apply various statistical tests, including t-tests, z-tests, chi-squared tests, and ANOVA tests. Each test will be explained clearly with practical examples. This crucial section concludes with a quiz to assess your understanding of hypothesis testing techniques.

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