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Statistics for Data Science

Mode via Udacity

Description

Deepen your analytical skills with this beginner-friendly course in real-world statistics. This course will teach you the statistical concepts & techniques you need to conduct rigorous inferential analyses and draw accurate conclusions from data sets.

Tags
Electrical Engineering

Syllabus

  • Simpson’s Paradox
    • Examine a case study to learn about Simpson’s Paradox.
  • Binomial Distribution
    • Learn about binomial distribution where each observation represents one of two outcomes and derive the probability of a binomial distribution.
  • Bayes Rule
    • Build on conditional probability principles to understand the Bayes rule and derive the Bayes theorem.
  • Sampling Distributions and Central Limit Theorem
    • Use normal distributions to compute probabilities and the Z-table to look up the proportions of observations above, below or in between values.
  • Hypothesis Testing
    • Use critical values to make decisions on whether or not a treatment has changed the value of a population parameter.
  • T-Tests and A/B Tests
    • Test the effect of a treatment or compare the difference in means for two groups when we have small sample sizes.
  • Logistic Regression
    • Use logistic regression results to make a prediction about the relationship between categorical dependent variables and predictors.
  • Course Project: Analyze A/B Test Results
    • In this project, you will be provided a dataset reflecting data collected from an experiment. You’ll use statistical techniques to answer questions about the data and report your conclusions and recommendations in a report.

Statistics for Data Science

Mode
Start Learning Find study partner
Affiliate notice

  • Type
    Online Courses
  • Provider
    Udacity

Deepen your analytical skills with this beginner-friendly course in real-world statistics. This course will teach you the statistical concepts & techniques you need to conduct rigorous inferential analyses and draw accurate conclusions from data sets.

  • Simpson’s Paradox
    • Examine a case study to learn about Simpson’s Paradox.
  • Binomial Distribution
    • Learn about binomial distribution where each observation represents one of two outcomes and derive the probability of a binomial distribution.
  • Bayes Rule
    • Build on conditional probability principles to understand the Bayes rule and derive the Bayes theorem.
  • Sampling Distributions and Central Limit Theorem
    • Use normal distributions to compute probabilities and the Z-table to look up the proportions of observations above, below or in between values.
  • Hypothesis Testing
    • Use critical values to make decisions on whether or not a treatment has changed the value of a population parameter.
  • T-Tests and A/B Tests
    • Test the effect of a treatment or compare the difference in means for two groups when we have small sample sizes.
  • Logistic Regression
    • Use logistic regression results to make a prediction about the relationship between categorical dependent variables and predictors.
  • Course Project: Analyze A/B Test Results
    • In this project, you will be provided a dataset reflecting data collected from an experiment. You’ll use statistical techniques to answer questions about the data and report your conclusions and recommendations in a report.

Tags

Electrical Engineering

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