Statistics for Data
Build foundational statistical intuition for data science and analysis.
Course overview
Statistics is the theoretical backbone of data analysis and machine learning. This course demystifies statistical concepts: mean, median, variance, standard deviation, normal distributions, central limit theorem, p-values, hypothesis testing (t-tests, chi-square), correlation, and linear relationships.
Prerequisites
What you'll achieve
- Calculate and interpret descriptive statistics and dispersion measures
- Understand probability distributions and the Central Limit Theorem
- Formulate hypotheses and interpret p-values and confidence intervals
- Evaluate statistical correlation versus causation in datasets
Curriculum
- 1Descriptive statistics: Central tendency, spread, skewness & kurtosis
- 2Probability basics, random variables & conditional probability
- 3Common distributions: Uniform, Binomial, Normal & Standard Normal (Z-score)
- 4Sampling techniques & Central Limit Theorem intuition
- 5Hypothesis testing framework: Null vs. alternative hypotheses, Type I/II errors
- 6Statistical tests: t-test, Z-test, ANOVA & Chi-Square test basics
- 7Correlation metrics (Pearson, Spearman) & simple linear regression
- 8Project: Statistical hypothesis testing report on an experimental dataset
Who is this for?
Students, researchers, and developers seeking real statistical rigor for data and AI.
Contact admissions for current fee and available learning formats
Enrol in Statistics for Data
- 6 weeks
- Intermediate
- Live online · One-to-one available · Group batches when scheduled
- 1-on-1 & small batches
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Ready to start your learning journey?
Apply today and our admissions team will review your application and guide you through the next steps.