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.
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: 14 classes, 7 labs
- CS 1.1
- CS 1.2
Students by the end of the course will be able to
- Use Pands to perform data-frame processing
- Report findings in the dataset through data visualization
- Reject or accept null hypothesis
- Use time series processing
- Describe and implement a plan for finding and dealing with null values, outliers, and other problems in a dataset
- Explain the central limit theorem its importance in statistical analysis
- Use statistical methods to calculate a z-score and explain what the z-score means
Course Dates: Monday, January 21 – Wednesday, March 6, 2019 (7 weeks)
Class Times: Monday and Wednesday at 3:30–5:20pm (10 class sessions)
| Class | Date | Topics |
|---|---|---|
| - | Monday, January 21 | MLK Jr. Day |
| 1 | Wednesday, January 23 | Introduction to Data Science |
| 2 | Monday, January 28 | Simple Data Manipulation |
| 3 | Wednesday, January 30 | Data Manipulation & Visualization |
| 4 | Monday, February 4 | How to Combine DataFrames |
| 5 | Wednesday, February 6 | Applied Descriptive Statistics |
| 6 | Monday, February 11 | Applied Probability to data frame |
| 7 | Wednesday, February 13 | [NPS Project Data Wrangling Check-in] |
| - | Monday, February 18 | President's Day (Observed) |
| 8 | Tuesday, February 19 | Hypothesis Testing & Acceptable Error |
| 9 | Wednesday, February 20 | Confidence Intervals & Outliers |
| 10 | Monday, February 25 | Statistical Analysis |
| 11 | Wednesday, February 27 | Time Series Data & Applications |
| 12 | Monday, March 4 | Final Class (presentations, etc) |
| 13 | Wednesday, March 6 | Final Exams/Presentations |
- 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
- Finish all required tutorials and projects
- Pass the final exam (summative assessment). The topics for final exam would be: 1- Null hypothesis, the steps to accept or reject it 2- Statistical terms and meanings such as Z-distribution, CDF, SF, ... 3- Histogram, density estimations 4- Outlier detection 5- Correlation
Just like any job, attendance at Make School is required and a key component of your success. Attendance is being onsite from 9:30 to 5:30 each day, attending all scheduled sessions including classes, huddles, coaching and school meetings, and working in the study labs when not in a scheduled session. Working onsite allows you to learn with your peers, have access to support from TAs, instructors and others, and is vital to your learning.
Attendance requirements for scheduled sessions are:
- No more than two no call no shows per term in any scheduled session.
- No more than four excused absences per term in any scheduled session.
Failure to meet these requirements will result in a PIP (Participation Improvement Plan). Failure to improve after the PIP is cause for not being allowed to continue at Make School.
Academic Honesty
Accommodations for Students
Attendance Policy
Diversity and Inclusion Policy
Grading System
Title IX Policy
Program Learning Outcomes