/DS-1.1-Data-Analysis

DS 1.1: Data Analysis & Visualization

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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

Course Specifics

Course Delivery: online | 7 weeks | 14 sessions

Course Credits: 3 units | 37.5 Seat Hours | 75 Total Hours

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, January 19 – Thursday, March 4, 2021 (7 weeks)

Class Times: Tuesday and Thursday at 2:30–5:15pm (13 class sessions)

Class Date Topics Assignments and Quizzes
- Tue, Jan 19 No Class - MLK Day
1 Thu, Jan 21 Introduction to Data Science
2 Tue, Jan 26 Simple Data Manipulation
3 Thu, Jan 28 Data Manipulation & Visualization Released: Quiz 1
4 Tue, Feb 2 Applied Descriptive Statistics
5 Thu, Feb 4 Applied Probability to data frame
6 Tue, Feb 9 PDFs, CDFs, and Normal Distributions Due: Data Visualization Challenge
7 Thu, Feb 11 Hypothesis Testing & Acceptable Error Released: Quiz 2
8 Tue, Feb 16 Hypothesis Testing & Acceptable Error II Due: Applied Probability and Statistics Challenge
9 Thu, Feb 18 Confidence Intervals, Outliers, and Statistical Analysis
10 Tue, Feb 23 Intro to Machine Learning Models Released: Quiz 3
11 Thu, Feb 25 Foundational Machine Learning Pipeline
12 Tue, Mar 2 Lab Day
13 Tue, Mar 4 Final Presentations Due: Machine Learning Challenge

Assignment Submissions

We will be using Gradescope, which allows us to provide fast and accurate feedback on your work. All assigned work will be submitted through Gradescope, and assignment and exam grades will be returned through Gradescope.

As soon as grades are posted, you will be notified immediately so that you can log in and see your feedback. You may also submit regrade requests if you feel we have made a mistake.

Your Gradescope login is your Make School email, and your password can be changed at https://gradescope.com/reset_password. The same link can be used if you need to set your password for the first time.

Evaluation

To pass this course you must meet the following requirements:

  • Complete all assignments and quizzes (one assignment or quiz will be dropped)
  • Pass all assignments according to the associated assignment rubric
  • Pass all quizzes with a score 70% or higher
  • If an assignment or quiz is not passing you will have up to a week after your grade is received to retake and bring your score up to passing
  • Actively participate in class and abide by the attendance policy
  • Make up all classwork from all absences

Information Resources

Any additional resources you may need (online books, etc.) can be found here. You can also find additional resources through the library linked below:

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