Learn the foundational skills of data science, including data collection, cleaning, analysis, and visualization with modern tools and libraries. Master the science and art of data exploration and visualization to tell stories with discoveries and persuade decision makers with data-driven insights. Collect a dataset, explore, analyze, and visualize it to discover trends, then present original insights. Gain a strong grounding in statistical concepts including measures of center and spread, distributions, sampling, and the central limit theorem. Utilize statistical techniques to calculate z-scores and confidence intervals, perform hypothesis tests, and identify outliers.
Course Dates: Tuesday, August 28 – Thursday, October 11, 2018 (7 weeks)
Class Times: Tuesday and Thursday at 3:30–5:20pm (13 class sessions)
| Class | Date | Topics |
|---|---|---|
| 1 | Tuesday, August 28 | Data Science Process & Tools |
| 2 | Thursday, August 30 | Data Manipulation & Visualization |
| 3 | Tuesday, September 4 | Data Cleaning & Descriptive Statistics |
| 4 | Thursday, September 6 | Conditional Probability |
| 5 | Tuesday, September 11 | Statistical Distributions & Sampling |
| 6 | Thursday, September 13 | Normal Distribution, CLT & Z-Scores |
| 7 | Tuesday, September 18 | NPS Project Data Wrangling Check-in |
| 8 | Thursday, September 20 | NPS Project Data Analysis Presentations |
| 9 | Tuesday, September 25 | Hypothesis Testing & Acceptable Error |
| 10 | Thursday, September 27 | Confidence Intervals & Outliers |
| 11 | Tuesday, October 2 | Statistical Analysis |
| 12 | Thursday, October 4 | Time Series Data & Applications |
| Tuesday, October 9 | No Class (Indigenous Peoples' Day) | |
| 13 | Thursday, October 11 | Custom Project Presentations |
- Weeks to Completion: 7
- Total Seat Hours: 37.5 hours
- Total Out-of-Class Hours: 75 hours
- Total Hours: 112.5 hours
- Units: 3 units
- Delivery Method: Residential
- Class Sessions: 13 classes, 7 labs
Students must pass the following course and demonstrate mastery of its competencies:
- CS 1.2: How Data Structures Work
By the end of this course, students will be able to:
- Use industry-standard libraries (Pandas and NumPy) to clean and preprocess a dataset
- Describe and implement a plan for finding and dealing with null values, outliers, and other problems in a dataset
- Explain the central limit theorem and why it is important in statistical analysis
- Use statistical methods to calculate a z-score, then interpret and explain what it means
Students will complete the following guided tutorials in this course:
- Exploratory Data Analysis & Visualization with App Store Dataset
- Advanced Data Analysis & Visualization with Pokémon Dataset
Students will complete the following self-guided projects in this course:
- Make School Summer Academy NPS Data Wrangling & Analysis
- Custom Project: Students will select a problem, identify data sources, analyze and present findings
To pass this course, students must meet the following requirements:
- No more than two unexcused absences ("no-call-no-shows")
- No more than four excused absences (communicated in advance)
- Make up all classwork from all absences
- Finish all required tutorials and projects
- Pass the final exam (summative assessment)
- Academic Honesty
- Accomodation Policy
- Diversity Statement
- Evaluation Methods
- Program Learning Outcomes
- Title IX Disclaimer
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