In this course, students learn the foundational skills of data science, including data collection, scrubbing, analysis, and visualization with modern tools and libraries. Students gain a strong grounding in statistical concepts, utilize statistical techniques and master the science and art of data exploration and visualization to tell stories and persuade decision makers with data-driven insights.
By the end of this course, students will be able to...
- Conduct data manipulation and visualization
- Understand when to reject or accept a null hypothesis
- Apply descriptive statistics, probability, and other forms of data analysis techniques
- Describe and implement a plan for finding and dealing with problems in a dataset such as null values and outliers
- Perform statistical analysis on data collections using a variety of methods
Course Dates: Tuesday, March 31 – Thursday, May 14, 2020 (7 weeks)
Class Times: Tuesday and Thursday at 9:30am to 12:15pm (14 class sessions)
| Class | Date | Topics |
|---|---|---|
| 1 | Tue, March 31 | Introduction to Data Science |
| 2 | Thu, April 2 | Simple Data Manipulation |
| 3 | Tue, April 7 | Data Manipulation & Visualization |
| 4 | Thu, April 9 | How to Combine DataFrames |
| 5 | Tue, April 14 | Applied Descriptive Statistics |
| 6 | Thu, April 16 | Applied Probability to data frame |
| 7 | Tue, April 21 | [NPS Project Data Wrangling Check-in] |
| 8 | Thu, April 23 | PDFs, CDFs, and Normal Distributions |
| 9 | Tue, April 28 | Hypothesis Testing & Acceptable Error |
| 10 | Thu, April 30 | Confidence Intervals, Outliers, and Statistical Analysis |
| 11 | Tue, May 5 | Time Series Data & Applications |
| 12 | Thu, May 7 | [Lesson 12] |
| 13 | Tue, May 12 | Final Exam |
| 14 | Thu, May 14 | Presentations |
[INSTRUCTOR NOTE] REPLACE THE BELOW WITH LINKS TO YOUR ASSIGNMENTS, CORRECT DATES, AND SUBMISSION FORM
| Assignment | Date Assigned | Due Date | Submission Form |
|---|---|---|---|
| Midterm Project - NPS Data Analysis | Tue, April 7 | Tue, April 21 | Submit Assignment |
| Homework 1 - Histogram | Thu, April 23 | Thu, April 30 | Submit Assignment |
- Implement a dataset processing with Numpy only and then Pandas
- Write a function that calculate conditional probability for two arbitrary attributes and arbitrary condition
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 you must meet the following requirements:
- Do all in-class activities and one homework
- Finish all required tutorials and two projects
- Pass the final exam (summative assessment). The topics for final exam would be:
- Null hypothesis, the steps to accept or reject it
- Statistical terms and meanings such as Z-distribution, CDF, SF, ...
- Histogram, density estimations
- Outlier detection
- Correlation