mikinomura/ds-1.1-data-analysis

DS 1.1: Data Analysis & Visualization

DS 1.1: Data Analysis & Visualization

Course Description

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.

Schedule

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

Course Specifics

• 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

Prerequisites

Students must pass the following course and demonstrate mastery of its competencies:

• CS 1.2: How Data Structures Work

Learning Outcomes (Competencies)

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

Tutorials

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

Projects

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

Evaluation

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)

Make School Policies

• Academic Honesty
• Accomodation Policy
• Diversity Statement
• Evaluation Methods
• Program Learning Outcomes
• Title IX Disclaimer

Repository Setup

Please follow these instructions exactly to set up your fork of this repository.