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Statistics for Data Science: A Comprehensive Journey
1 students
What you'll learn
- Understand key statistical concepts like probability, distributions, and hypothesis testing for effective data-driven decisions.
- Apply descriptive and inferential statistics to summarize data, draw conclusions, and make predictions.
- Use statistical models like regression to analyze data, identifying patterns, relationships, and trends.
- Evaluate data quality and apply statistical reasoning to solve data science problems and make informed decisions.
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Course content
6 sections • 41 lectures • 4h 25m total length
Requirements
- Basic Mathematics - Familiarity with high school-level math, including algebra and basic arithmetic operations.
- Foundational Data Concepts - Understanding of data types (e.g., categorical, numerical) and basic data handling in spreadsheets or simple programming environments.
- Introductory Programming Knowledge - Basic experience in Python or R is helpful, as some examples and exercises may involve code.
- Logical Thinking - Ability to analyze problems and approach them systematically, which is essential for statistical reasoning.
Description
Statistics is the backbone of Data Science, Machine Learning, and AI, yet it remains one of the most misunderstood topics. This course is designed to take you on a clear, structured, and application-driven journey, helping you build strong statistical intuition and hands-on problem-solving skills—without unnecessary math overload.
In this course, you’ll move step by step from fundamental statistical concepts to hypothesis testing and ANOVA, using real-time examples and practical datasets, including water quality data to connect theory with real-world decision-making.
Whether you are a student, data science aspirant, working professional, or researcher, this course will give you the statistical confidence needed to analyze data, interpret results, and build reliable AI/ML models.
What You’ll Learn
Understand population, samples, and sampling techniques
Perform descriptive statistical analysis and interpret results
Apply probability concepts with solved examples
Work with marginal, joint, and conditional probabilities
Master Bayes’ Theorem with step-by-step problem solving
Understand random variables and probability distributions
Apply Binomial, Uniform, and Normal distributions
Generate and interpret Normal distributions using sample data
Understand variance, statistical significance, and hypothesis testing
Perform t-tests and ANOVA with real examples
Execute t-tests using Excel
Develop statistical thinking for Data Science and AI applications
Why This Course is Different
Real-world examples (including environmental & water quality data)
Step-by-step solved problems
Concept-first approach (no blind formula memorization)
Hands-on Excel-based statistical testing
Perfect bridge between statistics and machine learning
Who This Course Is For
Aspiring Data Scientists & Machine Learning Engineers
Students in AI, ML, Data Science, and Engineering
Professionals transitioning into analytics roles
Researchers needing strong statistical foundations
Anyone struggling to understand probability and hypothesis testing
Who this course is for:
- Aspiring Data Scientists and Analysts seeking to understand essential statistical concepts for data-driven decision-making.
- Students in STEM fields (Science, Technology, Engineering, Math) who want practical statistical skills for research or projects.
- Professionals from non-technical backgrounds (e.g., business, healthcare, social sciences) looking to leverage statistics in their roles.
- Beginners in Data Science who have basic programming knowledge and wish to improve their data literacy and analytical abilities.