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DS101 Basic Statistics

Course Description: The Basic Statistics course will help students gain a fundamental understanding of statistical concepts that will be used throughout the Data Science program. Topics covered include probability, data types, common distributions, common descriptive statistics, and statistical inference.
Quarter Credit Hours: 4.5
Course Length: 60 hours
Prerequisites: None
Proficiency Exam: No
Theory Hours: 30
Laboratory Hours: 30
Externship Hours: 0
Outside Hours: 15
Total Contact Hours: 60

Required Resources:

  • Ground-based students are required to bring a late model laptop computer (either PC or MacBook) to class every day.

  • Online students are required to have a late model laptop or desktop computer with internet access.

  • Minimum: PC (Windows 10/11) or Mac (Big Sur or Monterey) laptop. 8GB ram, 512GB HD, Intel Core i5, AMD Ryzen 5, or Apple Intel or M1 Chipsets.

  • Recommended: PC (Windows 10/11) or Mac laptop(Big Sur or Monterey). 16GB ram, 1TB SSD, Intel Core i7, AMD Ryzen 7, or Apple M1/M1 Pro Chipsets.

  • Professionals: PC (Windows 10/11) or Mac(Big Sur or Monterey). 32-64 GB ram, 2-8TB SSD, Intel Core i9, AMD Ryzen 9/Threadripper, or Apple M1 Max Chipsets.

  • It is a requirement that you are able to download programming resources to your laptop/desktop for this class. (This means you need a steady internet high bandwidth connection.)

  • You are required to have a quiet place to study and to be able to focus on the material.

  • You are required to have uninterrupted weekly 1:1 video meetings with your mentor.

  • You are required to log into the Learning Management System (LMS) daily for at least 20 minutes.

  • Please follow and review each lesson's coding examples as provided. This will ensure you have a full understanding of concepts needed for your final hands-on assignment.

Course Outline:

Module Lesson Number Lesson Name
DS101 Basic Statistics 1 Introduction to Data Science
2 Probability
3 Variable Types
4 Common Descriptive Statistics
5 The Normal Distribution
6 Statistical Inference
7 Uniform, Binomial, Student’s T, and F-Distribution
8 Other Common Distributions
9 High Level Data Exploration
10 Concept Mastery Exam

Educational Objectives:

Upon successful completion of this program, students will be able to:

  1. Use probability rules and Bayes' Theorem
  2. Explain the differences between continuous and categorical variables
  3. Use common descriptive statistics for both populations and samples
  4. Use common distributions including Uniform, Normal, z-distribution, t-distribution, F-distribution, Poisson, and Weibull
  5. Use the Central Limit Theorem
  6. Explain what statistical inference is, and how to create confidence intervals
  7. Demonstrate high-level data exploration

Lessons:

Week 1

  1. Introduction to Data Science: What is Data Science, Data Science pipeline, Data Science software, Data extraction software, Data analysis software, Data visualization and reporting software, Your path through Data Science
  2. Probability: Single event probability, Rules of probability, Combinations and permutations, Calculating combinations, Combinations vs permutations, Boolean logic, Multiple event probability, Probability of two events, Probability and the Not statements, Mutually exclusive versus non-mutually exclusive events probability, Probability without replacement, The role of probability in Data Science
  3. Variable Types: Experiential design, Addition research design, Making sense of data, Recoding from quantitative to categorical, Ordinal variables, Quantitative variables

Week 2

  1. Common Descriptive Statistics: Populations and samples, Population parameters and sample statistics, Measures of central tendency, Measures of distribution, Measures of frequency
  2. The Normal Distribution and Central Limit Theorem: Normal distribution, Standard normal distribution, Standard normal population, Z-score, Probability, Using the z-score to determine a percentile, Parent and child distributions
  3. Uniform, Binomial, Student’s T-, and F- Distributions: Uniform distribution, Binomial distribution, t-distribution, f-distribution, Calculating effect size, Independent t-test
  4. Other Common Distributions: Multinomial distributions, Poisson distribution, Bathtub curve, Exponential distribution, Chi-square distribution

Week 3

  1. Statistical Inference: Statistical inference, Distribution of x versus the distribution of x-bar, Confidence intervals, Determining sample size, Courtroom analogy
  2. High Level Data Exploration: Data exploration, Graphical tools for analysis, Boxplot, Pie charts, Scatterplot, Data map, Tree map
  3. Concept Mastery Exam

Workshops

Class: DS101 Topic presented Lesson
Week 1 Github and Jupyter Basics 1
Probability 2
Week 2 The Normal Distribution and z-Scores 5
t-Tests and Chi-Squares 7
Week 3 Data Exploration 9
Basic Statistics Review 10

Points Distribution:

Exam Points
L1 19
L2 18
L3 9
L4 6
L5 13
L6 17
L7 19
L8 0
L9 12
Final 28

Points Total:

Type Points
Exam/Quiz Average: L1-9 Exam total points: 113 (80%)
Concept Mastery Exam: Final total points: 28 (20%)
Total points: 141 (100%)