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Statistics for DS

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Statistics for Data Science

Statistics is the language of data. In this beginner-to-intermediate course, learners will explore the statistical foundations essential for working with data, building machine learning models, and making decisions based on numbers. Whether you're a student, job aspirant, or working professional, this course will help you think like a data scientist — using data, not guesswork.

Who is this course for?

  • Beginners in Data Science & Analytics

  • Students from non-mathematical backgrounds

  • Professionals in HR, Finance, Admin, MIS, Marketing

  • Government officials working with data-led decision-making

  • Anyone who wants to move beyond Excel charts and start understanding what the numbers really mean

What You Will Learn:

  • Key statistical concepts used in real-world data science

  • Descriptive statistics: measures of center, spread, and shape

  • Probability & distributions

  • Sampling, bias, and statistical thinking

  • Inferential statistics: hypothesis testing & confidence intervals

  • How statistics powers machine learning models

Detailed Course Curriculum

Module 1: Introduction to Statistics

  • What is statistics? Role in analytics and data science

  • Descriptive vs Inferential statistics

  • Population vs Sample

  • Real-world use cases in business, governance, and AI

Module 2: Descriptive Statistics

  • Mean, Median, Mode, Range, Variance, Standard Deviation

  • Understanding skewness and kurtosis

  • Five-number summary and box plots

  • When and how to use each metric

Module 3: Data Visualization for Statistics

  • Frequency distribution and histograms

  • Scatter plots and correlation

  • Box plots, line charts, and bar graphs

  • What visuals reveal that numbers hide

Module 4: Probability & Distributions

  • Basics of probability, rules, and independence

  • Random variables

  • Normal, Binomial, and Uniform distributions

  • Central Limit Theorem (CLT) explained simply

Module 5: Sampling & Data Bias

  • Types of sampling: random, stratified, cluster

  • Sample size and representativeness

  • Types of bias: selection bias, confirmation bias

  • Avoiding bad data and misleading insights

Module 6: Inferential Statistics

  • Hypothesis testing: null vs alternative

  • P-values, significance levels, Type I & II errors

  • Confidence intervals

  • Real-world use: A/B testing in marketing or public policy

Module 7: Statistics in Machine Learning

  • How ML models use statistical assumptions

  • Correlation vs causation in feature selection

  • Bias-variance tradeoff

  • Model performance metrics: accuracy, precision, recall

Hands-on Project:

Hands-on Project on real data sets

Prerequisites:

  • Basic understanding of data and Excel

  • No math or coding background required — we build from the ground up

Certification

Earn a Certificate of Completion powered by DigData — trusted by professionals, valued by employers, and aligned with real-world industry needs.

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