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nd002-syllabus 2018-June v9

Workflow for Data 
Analysis

Learn about how to carry out analysis outside Jupyter
notebook using IPython or the command line interface


Data Analyst | 5
Need Help? Speak with an Advisor: 
www.udacity.com/advisor
Course 2: Practical Statistics
Learn how to apply inferential statistics and probability to real-world scenarios, such as analyzing A/B tests 
and building supervised learning models.
LEARNING OUTCOMES
LESSON ONE
Simpson’s Paradox
• 
Examine a case study to learn about Simpson’s Paradox
LESSON TWO
Probability 
• 
Learn the fundamental rules of probability.
LESSON THREE
Binomial 
Distribution
• 
Learn about binomial distribution where each observation
represents one of two outcomes
• 
Derive the probability of a binomial distribution
LESSON FOUR
Conditional
Probability
• 
Learn about conditional probability, i.e., when events are not
independent.
LESSON FIVE
Bayes Rule
• 
Build on conditional probability principles to understand the
Bayes rule
• 
Derive the Bayes theorem
LESSON SIX
Standardizing
• 
Convert distributions into the standard normal distribution
using the Z-score.
• 
Compute proportions using standardized distributions.
Course Project
Analyze Experiment 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.


Data Analyst | 6

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