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Statistics for Data Science (with Python)

Descriptive stats, probability, hypothesis tests, regression and A/B testing, explained simply and coded in Python

Ghosla Academy · Data Team

Data science interviews and real projects both lean heavily on statistics: which average to report, whether a difference is real or just noise, and how confident you can be in a number. This course builds that thinking from scratch with small, India-flavoured examples such as monthly expenses, delivery times, exam marks and app conversions, and every idea is shown in Python with pandas, NumPy and SciPy. You move from descriptive statistics and visual exploration to probability, sampling, confidence intervals, hypothesis tests, regression and A/B testing. Each section ends with a quiz, and two hands-on projects give you portfolio pieces that run free in Google Colab. Explanations are in simple English with a little Hinglish where it helps.

What you will be able to do

  • Summarise any dataset with the right average and spread, and spot outliers with the IQR rule
  • Explore data with histograms, box plots and correlation in pandas and Matplotlib
  • Work out probabilities, including conditional probability and Bayes' rule, for real situations
  • Use binomial and normal distributions in SciPy and explain the Central Limit Theorem
  • Build confidence intervals and run t-tests and chi-square tests, and read p-values correctly
  • Fit a simple linear regression and analyse an A/B test, then write a clear decision report

What you build

  • Exploratory analysis of a monthly household expense dataset
  • A/B test report for a food-delivery discount offer

What you need to start

  • Basic Python (variables, lists, functions) and Class 10 level maths
  • A laptop or phone browser with a free Google account for Google Colab

Curriculum · 5 sections, 20 lectures

Data and Descriptive Statistics4 lectures
  1. Why Statistics Matters in Data ScienceFree preview12 min
  2. Mean, Median, Mode and When Each MisleadsFree preview14 min
  3. Spread: Range, IQR, Variance and Standard Deviation12 min
  4. Quiz: Descriptive Statistics6 min
Visualising and Exploring Data4 lectures
  1. Histograms, Box Plots and Outliers with pandas14 min
  2. Correlation Is Not Causation11 min
  3. Project: Explore an Indian Monthly Expense Dataset20 min
  4. Quiz: Exploring Data6 min
Probability and Distributions4 lectures
  1. Probability Basics and Conditional Probability15 min
  2. Binomial and Normal Distributions in SciPy15 min
  3. Sampling, the Central Limit Theorem and Standard Error12 min
  4. Quiz: Probability and Distributions7 min
Inference: Confidence Intervals and Hypothesis Tests4 lectures
  1. Confidence Intervals Step by Step14 min
  2. Hypothesis Testing, p-values and Errors15 min
  3. t-tests and Chi-square Tests in SciPy13 min
  4. Quiz: Inference7 min
Regression and A/B Testing4 lectures
  1. Simple Linear Regression and R²15 min
  2. A/B Testing for Product Decisions12 min
  3. Project: A/B Test Report for a Food-Delivery Offer20 min
  4. Quiz: Regression and A/B Testing7 min

Who this is for

  • College students and freshers preparing for data analyst or data science roles
  • Python learners who can write code but feel unsure about statistics
  • Working professionals who read reports and dashboards and want to judge numbers correctly