Data Science and Machine Learning
Data Analysis with Python and Pandas
Analyze data like a pro. Use Python, Pandas, and NumPy to clean, transform, and explore datasets and extract meaningful…
USD 39
Data Science and Machine Learning
Statistics for Data Science
Use probability, distributions, confidence intervals, hypothesis tests, regression and A/B testing to analyse data correctly and explain your conclusions with honest uncertainty.
About this course
Build the math behind data science. Master probability, distributions, hypothesis testing, and inference for confident analysis.
What you will learn
- Distinguish populations from samples, choose sound sampling methods and recognise the main sources of bias.
- Classify variables and summarise distributions with appropriate measures of centre, spread and shape.
- Apply probability rules, conditional probability and Bayes' theorem to practical business questions.
- Use common probability distributions and the central limit theorem to model uncertainty and sampling variation.
- Build confidence intervals and run t-tests and chi-square tests in Python, interpreting p-values correctly.
- Analyse correlations, regressions and A/B tests, and present the results in a clear statistical report.
Curriculum
Module 1Statistical Thinking4 lessons
- 1Populations, Samples and Bias Free preview
- 2Types of Variables
- 3Distributions and the Normal Curve
- 4Central Tendency and Spread
- QModule quiz (4 questions)
Module 2Probability Essentials4 lessons
- 5Probability Rules and Intuition
- 6Conditional Probability and Bayes
- 7Common Distributions in Practice
- 8The Central Limit Theorem
- QModule quiz (4 questions)
Module 3Inference4 lessons
- 9Confidence Intervals Explained
- 10Hypothesis Testing Step by Step
- 11p-Values: Use and Misuse
- 12t-Tests and Chi-Square
- QModule quiz (4 questions)
Module 4Statistics for Models3 lessons
- 13Correlation and Regression Statistics
- 14A/B Test Analysis
- 15Course Project: Statistical Report
- QModule quiz (4 questions)
PFinal project: Statistical report on a real dataset
Who this course is for
- Aspiring data scientists and analysts who need a solid statistical foundation.
- Business, finance and marketing professionals who read or commission data analysis.
- Students and programmers who can use Python and want to understand the statistics behind models.
Requirements
- A computer with Python 3 and the pandas, NumPy, SciPy, statsmodels and Matplotlib libraries, or a free online notebook such as Google Colab.
- Basic Python and pandas skills, such as loading a CSV file and selecting columns.
- Secondary-school mathematics; no prior statistics is required.
What is included
Written lessons
15 lessons, each with a practical exercise and key points to remember.
Module quizzes
A short quiz at the end of each module. Pass mark 70%, and you can retake it.
Final project
Statistical report on a real dataset. A practical brief with steps and a way to check your own work.
Certificate
A certificate of completion from EMFAD Courses with a unique code anyone can verify.
How the certificate works
Complete every lesson and pass every module quiz with 70% or more. Your certificate then appears in your learner area, ready to print or save as PDF. It shows your name, the course title, the completion date and a unique code. It is a certificate of completion from EMFAD Courses, not an accredited qualification.
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