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
- Take the Pre-course Survey
- Sign the Participant Code of Conduct and Course Agreement
- Introduce Yourself
- Install RStudio and the following libraries/packages:
- dplyr
- car
- caret
- tidyverse
- swirl
- 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.
Modules
The program is structured around 12 modules
| Module Date | Module | Learning Outcomes | Webinar Details and Key Activities |
| December 16, 2019 | Week 1
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| 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. |
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| 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}. |
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| 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}. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| 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. |
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| March 2, 2020 | Week 10
Interactive Visualizations |
To demonstrate foundational RShiny capability by developing an interactive visualization using {shiny}. |
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| 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. |
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| 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. |
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