What will you learn in HarvardX: Data Science: Inference and Modeling course
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Understand the principles of statistical inference used in data science.
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Learn how to quantify uncertainty using probability models and sampling distributions.
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Apply hypothesis testing and confidence intervals to real-world problems.
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Build and interpret statistical models for data-driven decision-making.
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Understand variability, bias, and trade-offs in modeling choices.
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Strengthen analytical reasoning for evidence-based conclusions.
Program Overview
Foundations of Statistical Inference
⏳ 1–2 weeks
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Learn the role of inference in data science.
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Understand populations vs samples and sampling variability.
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Explore probability concepts that underpin statistical reasoning.
Probability Models and Random Variables
⏳ 2–3 weeks
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Learn common probability distributions used in data analysis.
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Understand expectations, variance, and randomness.
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Apply probability models to describe real-world phenomena.
Hypothesis Testing and Confidence Intervals
⏳ 2–3 weeks
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Learn how to test hypotheses using data.
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Construct and interpret confidence intervals.
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Understand p-values, statistical significance, and common pitfalls.
Statistical Modeling and Interpretation
⏳ 2–3 weeks
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Build statistical models to explain and predict outcomes.
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Interpret model parameters and assess model assumptions.
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Understand model limitations and sources of uncertainty.
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Job Outlook
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Essential knowledge for Data Analysts, Data Scientists, and Researchers.
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Core preparation for advanced machine learning and predictive modeling.
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Valuable across industries including healthcare, finance, public policy, and marketing.
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Builds strong foundations for evidence-based decision-making roles.
