Harvardx Data Science Professional Certificate

This repository includes my course notes, assessments, and resources from Module 2: Visualization of the 17-month-long Harvardx Professional Certificate Program in Data Science.

Module 2: Data Science - Visualization

What I Learned:

Section 1: Data Visualization, Distributions, Quantiles, Percentiles & Boxplots: Exploratory Data Analysis

• Understood importance of data visualization for communicating data-driven findings
• Used distributions to summarize data
• Used average & standard deviation to understand normal distribution
• Assessed how well a normal distribution fits data using a quantile-quantile plot
• Interpreted data from a boxplot

Section 2: ggplot2, Customizing Plots

• Used ggplot2 to create data visualizations in R
• Explained what data components of a graph is
• Identified geometry components of a graph and knew when to use which type of geometry
• Explained what the aesthetic mapping component of a graph is
• Understand and selected appropriate scale components of a graph

Section 3: Summarizing with dplyr

• Understood the importance of summarizing data in exploratory data analysis.
• Used "summarize" verb in dplyr to facilitate summarizing data
• Used "group_by" verb in dplyr to facilitate summarizing data
• Accessed values using the dot placeholder
• Use "arrange" to examine data after sorting

Section 4: Gapminder

• Use effective data visualization to convey data-based trends
• Applied ggplot2 techniques from the previous section to answer questions using data
• Understood how fixed scales across plots can ease comparisons
• Modified graphs to improve data visualization

Section 5: Data Visualization Principles

• Understood basic principles of effective data visualization
• Understood importance of keeping your goal in mind when deciding on a visualization approach
• Understood principles for encoding data, including position, aligned lengths, angles, area, brightness & color
• Knew when to include the number zero in visualizations
• Used techniques to ease comparisons, such as using common axes, putting visual cues to be compared adjacent to one another & using color effectively

The Harvardx Data Science Professional Certificate

Courses in this program:

1. Data Science: R Basics
2. Data Science: Visualization
3. Data Science: Probability
4. Data Science: Inference & Modeling
5. Data Science: Productivity Tools
6. Data Science: Wrangling
7. Data Science: Linear Regression
8. Data Science: Machine Learning
9. Data Science: Capstone

What I Learned:

• Fundamental R programming skills
• Statistical concepts such as probability, inference, and modeling and how to apply them in practice
• Gained experience with the tidyverse, including data visualization with ggplot2 and data wrangling with dplyr
• Became familiar with essential tools for practicing data scientists such as Unix/Linux, git and GitHub, and RStudio
• Implemeneted machine learning algorithms
• In-depth knowledge of fundamental data science concepts through motivating real-world case studies

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The course, including its assessment questions and data sets were provided by Rafael A Irizarry, Professor of Biostatistics at Harvard Chan School of Public Health and Professor of Biostatistics and Computational Biology at the Dana-Farber Cancer Institute.