Data Science 2

Course Introduction and Instructions

Welcome to Practical Data Science. We are glad to have you in the program.

The Course Orientation module will provide you with the course syllabus, requirements to earn a certificate of completion, frequently asked questions (FAQs), and an overview of the learning platform and an external coding platform. The learning platform is your central point of access to all course content including webinar recordings, assignments, quizzes, exercises, and discussions.

Key Activities for Course Orientation

  1. Take the Pre-course Survey
  2. Sign the Participant Code of Conduct and Course Agreement
  3. Introduce Yourself
  4. Install RStudio and the following libraries/packages:
    • dplyr
    • car
    • caret
    • tidyverse
    • swirl
  5. Familiarize with the Swirl package

Plan Your Time

  • Each week, on Monday, you will gain access to a new module with video lectures and corresponding activities
  • Complete all activities for the week before the due date; which is no later than Monday of the following week
  • Live sessions (webinars) and office hours have been scheduled throughout the course journey to help you interact with your Course Leader


Course Overview

The Practical Data Science course, in collaboration with Berkeley Extension, is a 12-week program that offers a fundamental understanding of the theory and practical applications of data science. During this 12-week journey, you will learn how to manipulate, clean, and format data, just as you would need to do in the industry. You will also learn how to build linear and logistic regression models, perform a simple A/B test, create a simple interactive visualization application, and version your data. You will also have the chance to receive practical guidance on how to prepare for a future career in data science.


The program is structured around 12 modules

Module Date Module Learning Outcomes Webinar Details and Key Activities
December 16, 2019 Week 1

  • Course Orientation
  • Data Science: Exploration and Processes
  • To orient yourself with the learning platform and course details.
  • To explore the data science processes and project lifecycles with an emphasis on maintaining the integrity and reproducible findings.
  • Introduction to the platform and the mechanics of the course
  • Assignment: Challenges in Irreproducible Research
December 23, 2019 Week 2

Introduction to R

To apply basic functions in R programming, including writing code, installing packages, and working with a variety of data structures.  

  • Getting acquainted with R Studio and Vocareum
  • Assignment: Key Concepts in R
  • Quiz
Holiday Break – December 25, 2019, to January 1, 2020
January 06, 2020 Week 3

Data Visualization

To apply the fundamentals of data visualization and manipulation using R packages such as {ggplot2} and {dplyr}.
  • Assignment: Visualizing and Summarizing Data
  • Quiz
January 13, 2020 Week 4

Tidying and Reshaping Data

To apply data reshaping, cleaning, and merging techniques to a data frame in R using {dplyr} and {tidyr}.
  • Assignment: Manipulating and Cleaning Data
  • Quiz
January 20, 2020 Week 5

Introduction to Statistics and Probabilities

To explore probability and statistics using R with an emphasis on sampling, distributions, and confidence intervals.
  • Assignment: Analytical Techniques
  • Quiz
January 27, 2020 Week 6

A/B Testing

To apply A/B testing, interpret results, and make recommendations about web traffic using a dataset in R.
  • Assignment: A/B Testing
  • Quiz
February 3, 2020 Week 7

Exploratory Data Analysis (EDA) and Introduction to Models

To apply techniques in Exploratory Data Analysis (EDA) to understand and engineer data features with an income dataset.
  • Assignment: Exploratory Data Analysis
  • Quiz
February 10, 2020 Week 8

Introduction to Linear Regression

To apply techniques in univariate and multivariate linear regression to a housing dataset in R and interpret output.
  • Assignment: Linear Regression
  • Quiz
Break Week – February 17, 2020, to February 23, 2020
February 24, 2020 Week 9

Introduction to Logistic Regression

To apply techniques in logistic regression to the Income dataset in R and predict income level based on demographics.
  • Assignment: Logistic Regression
  • Quiz
March 2, 2020 Week 10

Interactive Visualizations

To demonstrate foundational RShiny capability by developing an interactive visualization using {shiny}.
  • Assignment: Create a R Shiny Application
  • Quiz
March 9, 2020 Week 11

Accessing and Versioning Your Data

To develop a practical capability in accessing and versioning your data using applications such as setting up a database connection and versioning with Git/GitHub.
  • Assignment: Access a Database and Setup a Git Account
  • Quiz
March 16, 2020 Week 12

Preparing for a Data Science Career

To create goals for your future career and data science and start developing your data science portfolio.
  • Discussion



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Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed