samiringley/ds-1.1-data-analysis

DS 1.1: Data Analysis & Visualization

DS 1.1: Data Analysis & Visualization

Course Description

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.

Prerequisites:

CS 1.2

Learning Outcomes

By the end of this course, students will be able to...

1. Conduct data manipulation and visualization
2. Understand when to reject or accept a null hypothesis
3. Apply descriptive statistics, probability, and other forms of data analysis techniques
4. Describe and implement a plan for finding and dealing with problems in a dataset such as null values and outliers
5. Perform statistical analysis on data collections using a variety of methods

Schedule

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

Assignment Schedule

[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

Class Assignments

• Implement a dataset processing with Numpy only and then Pandas
• Write a function that calculate conditional probability for two arbitrary attributes and arbitrary condition

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 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

Make School Course Policies

• Program Learning Outcomes
• Grading System
• Diversity and Inclusion Statement
• Academic Honesty
• Attendance Policy
• Course Credit Policy
• Disability Services (Academic Accommodations)
• Student Handbook