Practical Data Science Course: Learn Hands-On Skills with UC Berkeley

Table of Contents

1. Introduction
2. Overview of UC Berkeley Extension and Emeritus
3. Faculty and Course Details
4. Structure and Learning Experience
5. Course Content Breakdown
   • Week 1: The Data Science Process and Lifecycle
   • Week 2: Introduction to R and Basic Functions
   • Week 3: Data Visualization with ggplot
   • Week 4: Statistics and Probability Basics
   • Week 5: Hypothesis testing and Analysis
   • Week 6: Exploratory Data Analysis
   • Week 7: Linear Regression
   • Week 8: Logistic Regression
   • Week 9: Building Data Applications with Shiny
   • Week 10: Creating a Data Science Portfolio
6. Course Benefits and Certification
7. Course Fees and Application
8. Q&A with Participants
9. Conclusion
10. Resources

📚 Introduction

Welcome to this informational session on practical data science! In this article, we will explore the details of the course offered by the Faculty of UC Berkeley Extension, in collaboration with Emeritus. Whether you are new to data science or looking to enhance your existing knowledge, this course will provide you with practical skills and insights to succeed in the field. We will cover everything from the course structure to the benefits and certification you can obtain upon completion. So let's dive in and discover what this course has in store for you!

🏫 Overview of UC Berkeley Extension and Emeritus

Before we delve into the course details, let's take a moment to understand the institutions behind this program. UC Berkeley Extension, founded in 1891, is renowned for offering continuing and professional education for adults. With over 65 professional certificates and programs, UC Berkeley Extension has been a hub for lifelong learning. In collaboration with Emeritus, a leading online education provider, UC Berkeley Extension aims to bring top-quality courses to a global audience. Emeritus has partnered with prestigious universities worldwide, including MIT, Dartmouth, and Columbia University. Together, UC Berkeley Extension and Emeritus offer online courses that provide students with a rich learning experience.

👨‍🏫 Faculty and Course Details

In this course, you will be guided by experienced instructors from UC Berkeley Extension. Your primary instructors for this course are Danielle Quinn and a master's degree holder in applied statistics. Both instructors bring a wealth of knowledge and industry experience to the course, ensuring a comprehensive and practical learning experience. Additionally, there will be course leaders available during office hours to answer your questions and provide guidance as you progress through the course. This course is designed for beginners in coding and those with a coding background, making it accessible to a wide range of learners.

🏢 Structure and Learning Experience

This practical data science course spans over 12 weeks and is conducted entirely online. The course is divided into live webinars, assignments, Quizzes, and discussions. The live webinars, which will be approximately 60-90 minutes each, will cover different topics ranging from the data science process to machine learning and data visualization. If you are unable to attend a live webinar, don't worry, as the recordings will be available for you to watch at your convenience. The course is expected to require a time commitment of 4 to 6 hours per week, including completing assignments, participating in discussions, and studying the course material.

📖 Course Content Breakdown

Now, let's take a closer look at the course content and what each week's topics will cover:

Week 1: The Data Science Process and Lifecycle

This week will provide an overview of the data science process and its lifecycle. You will learn about scoping a project, data cleaning, exploratory data analysis, machine learning, and presenting results to stakeholders. The importance of reproducibility and ethics in data science will also be discussed.

Week 2: Introduction to R and Basic Functions

In this week, you will get hands-on experience with R, a popular programming language used in data science. You will learn the basic functions of R and how to manipulate data using RStudio. This practical knowledge will serve as a foundation for the rest of the course.

Week 3: Data Visualization with ggplot

Data visualization is a crucial aspect of data science. In week 3, you will dive into the world of data visualization using the ggplot Package. You will learn how to create visually appealing and informative plots to Present your analysis effectively.

Week 4: Statistics and Probability Basics

To understand the foundations of data science, it is essential to have a solid understanding of statistics and probability. Week 4 will cover the most vital concepts in statistics and probability, including sampling, probability distributions, p-values, and hypothesis testing.

Week 5: Hypothesis Testing and Analysis

Building upon the knowledge gained in Week 4, this week will focus on hypothesis testing and analysis. You will learn how to design, scope, and analyze hypothesis tests using real-world datasets. Understanding the significance of variables and interpreting results accurately will be emphasized.

Week 6: Exploratory Data Analysis

Before diving into modeling, it is crucial to conduct thorough exploratory data analysis (EDA). Week 6 will guide you through various EDA techniques, including correlation plots and handling missing variables. These techniques will provide insights into the dataset and help inform the modeling process.

Week 7: Linear Regression

Linear regression is one of the fundamental techniques in data science. In Week 7, you will learn about linear regression models and their applications. Real-world datasets, such as predicting house prices, will be used to demonstrate the practical implementation of linear regression.

Week 8: Logistic Regression

Expanding on linear regression, Week 8 will cover logistic regression. You will explore classification problems and learn how to predict binary outcomes. The focus will be on using real data, such as predicting income levels based on demographic information, to impart practical knowledge.

Week 9: Building Data Applications with Shiny

In the world of data science, the ability to create interactive and user-friendly applications is valuable. Week 9 will introduce you to Shiny, a web application framework in R. You will learn how to build and deploy data applications, enhancing your portfolio and presenting your results effectively.

Week 10: Creating a Data Science Portfolio

Week 10 will focus on building a data science portfolio. You will learn the best practices for creating an impressive portfolio that showcases your skills and projects. Setting up a GitHub account and understanding Resume and interview best practices will also be covered.

✅ Course Benefits and Certification

Upon successful completion of the course, you will receive a certificate of completion from UC Berkeley Extension, in collaboration with Emeritus. This certificate will demonstrate your proficiency in practical data science and enhance your credentials in the job market. The course provides a comprehensive learning experience with live webinars, assignments, and access to course leaders for personalized guidance. You will also have the opportunity to network with fellow students from around the world, creating valuable connections in the data science community.

💵 Course Fees and Application

The course fee for Practical Data Science is $1,600. UC Berkeley Extension offers flexible payment options to accommodate your needs. The course is scheduled to start on June 26th, so make sure to submit your application by June 25th to secure your spot. If you are interested in acquiring practical data science skills and advancing your career, this course promises to be a worthwhile investment.

❓ Q&A with Participants

During the webinar, participants had the opportunity to ask questions and obtain clarification on various aspects of the course. Here are some of the frequently asked questions:

Q: Is this course suitable for someone without a coding background or a beginner-level coder? A: Yes, this course caters to beginners in coding or those with a basic coding background. It provides a solid foundation in data science and ensures that learners with varying levels of experience can benefit from the course.

Q: How does this course help in joining the data science or machine learning industry? A: This course equips you with essential skills required in data science, such as data manipulation, visualization, statistics, and machine learning. By completing this course and adding the projects to your portfolio, you can increase your chances of securing positions as a data scientist or analyst.

Q: Can I get a job with knowledge of R only, without Python or SQL? A: While Python and SQL are valuable skills in the data science industry, having proficiency in R is also highly sought after. Many job postings mention either Python or R, so having expertise in either language can open doors for job opportunities.

These are just a few examples of the questions addressed during the webinar. The instructors provided detailed responses, ensuring that participants gained a clear understanding of the course and its relevance in the data science industry.

🎯 Conclusion

Practical Data Science offered by UC Berkeley Extension, in collaboration with Emeritus, is a comprehensive course designed to equip learners with practical skills in data science. Whether you are new to the field or looking to enhance your existing knowledge, this course offers a structured learning experience with real-world datasets and expert instruction. By completing the course, you will receive a certificate of completion, enhancing your credibility in the job market. Join this course and embark on your journey towards becoming a proficient data scientist. Invest in your future today!

📚 Resources

1. UC Berkeley Extension
2. Emeritus
3. Canvas Learning Platform
4. RStudio
5. GitHub

Highlights

• Practical Data Science course designed for beginners in coding or those with a basic coding background.
• Instructors from UC Berkeley Extension bring extensive industry experience to the course.
• Course covers the data science process, R programming, data visualization, statistics, machine learning, and data applications with Shiny.
• Flexible learning experience with live webinars, assignments, and discussions.
• Participants receive a certificate of completion from UC Berkeley Extension, enhancing their credentials in the job market.

FAQ

Q: Who is this course suitable for? A: This course is suitable for beginners in coding or individuals with a basic coding background who want to acquire practical data science skills.

Q: Can I join this course without knowledge of R? A: Yes, this course caters to beginners and provides an introduction to R programming along with the necessary basics.

Q: Will this course help me change careers to data science? A: Completing this course and adding the projects to your portfolio will increase your chances of securing positions in data science or analytics. However, market demand and other factors may also impact career transitions.

Q: Can I get a job with knowledge of R only, without Python or SQL? A: While Python and SQL are valuable skills, expertise in R alone can still open doors to job opportunities. Many job postings mention either Python or R, so proficiency in either language is valuable.

Q: Can I network with other students in this course? A: Yes, opportunities for networking with other students are provided through discussion panels and class interactions on platforms like Canvas.

Q: What kind of timeframe should I expect for completing assignments? A: The course requires a time commitment of 4 to 6 hours per week, including completing assignments, participating in discussions, and studying the course material. However, individual timeframes may vary based on personal learning pace.

Q: Are there additional resources or support available during the course? A: Yes, there are course leaders available during office hours to assist with questions and provide guidance. The Emeritus learning platform, Canvas, provides access to course materials, lectures, and discussions.

Q: Can I gain practical experience with real-world datasets in this course? A: Yes, the course utilizes real-world datasets throughout the program, allowing learners to apply their skills and knowledge to practical data analysis scenarios.

Q: Will I have access to the course material after completing the program? A: While access to the course material may be limited after completing the program, the knowledge and skills acquired during the course will provide a strong foundation for future data science endeavors.

Q: Is there a certificate provided upon course completion? A: Yes, participants receive a certificate of completion from UC Berkeley Extension, in collaboration with Emeritus.

Q: How can I apply for this course? A: To apply for this course, please visit the UC Berkeley Extension website and follow the application instructions provided.

Note: The FAQ section includes some of the questions discussed during the webinar, and the answers are based on the responses provided by the instructors.