Data Science: Visualization Pace Chart
Data Science: Visualization
We strongly recommend participants commit to the following weekly schedule. Learners who follow this pace are more likely to earn the Verified Certificate than those who take a more leisurely approach. For more specifics on your grade, please click on the “Progress” tab within the course.
Participants must earn at least 108 points to meet the minimum passing score of 70% to receive a Verified Certificate.
|
Section |
Topics |
Assessment(s) |
Pts |
|
0 |
Introduction and Welcome 0.1 Important Pre-Course Survey |
None |
NA |
|
1 |
Introduction to Data Visualization and Distributions 1.0 Section 1 Overview 1.1 Introduction to Data Visualization 1.2 Introduction to Distributions 1.3 Quantiles, Percentiles and Boxplots 1.4 Exploratory Data Analysis |
DC = Data Camp · DC Assessment 1.1 Data Types · DC Assessment 1.2 Distributions · DC Assessment 1.2 Normal Distribution · DC Assessment 1.3 Quantiles, percentiles, and boxplots · DC Assessment 1.4 Robust Summaries with Outliers |
5.5 6 8 5 8 |
|
2 |
Introduction to ggplot2 2.0 Section 2 Overview 2.1 Basics of ggplot2 2.2 Customizing Plots |
DC Assessment 2.2 Introduction to ggplot2 |
19.5 |
|
3 |
Summarizing with dplyr 3.0 Section 3 Overview 3.1 Summarizing with dplyr |
DC Assessment 3.1 Summarizing with dplyr |
8.5 |
|
4 |
Gapminder 4.0 Section 4 Overview 4.1 Introduction to Gapminder 4.2 Using the Gapminder Dataset |
DC Assessment 4.2 Exploring the Gapminder Dataset |
14.5 |
|
5 |
Data Visualization Principles 5.0 Section 5 Overview 5.1 Data Visualization Principles, Part, 1 5.2 Data Visualization Principles, Part 2 5.3 Data Visualization Principles, Part 3 |
DC Assessment 5.1 Data Viz Principles Part1 DC Assessment 5.2 Data Viz Principles Part2 DC Assessment 5.3 Data Viz Principles Part3 Assessment 11 Titanic Survival |
3.5 4.0 4.5 20.5 |
|
Section |
Topics |
Assessment(s) |
Pts |
|
6 |
Comprehensive Assessment and End of Course Survey
|
Assessment Part 1: Properties of Stars Assessment Part 2: Climate Change |
15 31 |
|
Total Points Possible |
153.5 |
||
Grading
The eleven DataCamp programming exercises are worth 90% of your grade. They show up as 10 total grades because there are 2 quizzes for Section 1.2. The comprehensive assessments at the end of the course combine to be worth 10% of your grade.
All other components of the course, such as the the discussion boards, are not for credit. Assignments in the course are written in R. R is a flexible programming language designed partly for readability and ease of programming.
Some assignments are on DataCamp and do not require you to install R on your own computer. Verified learners also have access to a comprehensive assessment that requires writing code on a local installation of R.
HarvardX has partnered with a platform called DataCamp External link to create programming assignments that you can do online. There is no need to install anything on your computer. You will be given a set of code to modify and run in your browser. You can take as many attempts as you need on DataCamp problems. When your code works correctly, you will be awarded points. You can also request hints, which will help you out, but hints decrease your point total. Remember that you can always go to the Discussion Board on edX.org for help that doesn’t cost points.
Assignments on the edX platform require you to write code in your own version of R and input the answers on edX. You will have 10 attempts for short answer questions and 2 attempts for multiple choice questions.
