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A Crash Course in Data Science

Approx. 7 hours to complete

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Course Summary

This course provides a comprehensive introduction to data science, covering topics such as data wrangling, visualization, and modeling. Students will learn how to use popular tools like Python, R, and SQL to solve real-world problems.

Key Learning Points

Gain practical skills in data analysis and machine learning
Learn to use popular data science tools like Python and R
Apply your skills to real-world problems and projects

Job Positions & Salaries of people who have taken this course might have

- USA: $62,453
- India: ₹428,367
- Spain: €28,345
- USA: $62,453
- India: ₹428,367
- Spain: €28,345
- USA: $96,072
- India: ₹1,071,026
- Spain: €33,196
- USA: $62,453
- India: ₹428,367
- Spain: €28,345
- USA: $96,072
- India: ₹1,071,026
- Spain: €33,196
- USA: $112,117
- India: ₹1,308,254
- Spain: €40,000

Learning Outcomes

Develop practical skills in data analysis and modeling
Understand the fundamentals of data science
Apply your skills to real-world projects and problems

Prerequisites or good to have knowledge before taking this course

Basic knowledge of programming concepts
Familiarity with statistics and linear algebra

Course Difficulty Level

Beginner

Course Format

Online
Self-paced

Similar Courses

Applied Data Science with Python
Machine Learning
Data Science Essentials

Related Education Paths

Related Books

Description

By now you have definitely heard about data science and big data. In this one-week class, we will provide a crash course in what these terms mean and how they play a role in successful organizations. This class is for anyone who wants to learn what all the data science action is about, including those who will eventually need to manage data scientists. The goal is to get you up to speed as quickly as possible on data science without all the fluff. We've designed this course to be as convenient as possible without sacrificing any of the essentials.

This is a focused course designed to rapidly get you up to speed on the field of data science. Our goal was to make this as convenient as possible for you without sacrificing any essential content. We've left the technical information aside so that you can focus on managing your team and moving it forward. After completing this course you will know. 1. How to describe the role data science plays in various contexts 2. How statistics, machine learning, and software engineering play a role in data science 3. How to describe the structure of a data science project 4. Know the key terms and tools used by data scientists 5. How to identify a successful and an unsuccessful data science project 3. The role of a data science manager Course cover image by r2hox. Creative Commons BY-SA: https://flic.kr/p/gdMuhT

Knowledge

Describe Data Science’s role in various contexts
Understand how Statistics and Machine Learning affect Data Science
Use the key terms used by data scientist
Predict whether a Data Science project will be successful

Outline

A Crash Course in Data Science
About Your Instructors
What is Data Science?
Statistics by example activities
Machine learning, the basics
Machine learning further reading
What is Software Engineering for Data Science?
The Structure of a Data Science Project
The outputs of a data science experiment
The four secrets of a successful data science experiment
Data Scientist Toolbox
Separating Hype from Value
Specialization Textbook
Grading
Pre-Course Survey
Statistics by example activities
Machine learning
The outputs of a data science experiment
The four secrets of a successful data science experiment
Post-Course Survey
What is data science?
What is statistics good for?
Machine learning
Quiz: Software Engineering
Structure of a Data Science Project
The outputs of a data science experiment
Defining Success in Data Science
Data scientist toolbox
Separating hype from value

Summary of User Reviews

Learn data science with this comprehensive course on Coursera. Students praise the course for its engaging content and practical approach. With a focus on real-world applications, this course is a great choice for anyone looking to gain skills in data science.

Key Aspect Users Liked About This Course

The practical approach and real-world applications are highly praised by many users.

Pros from User Reviews

Engaging content that keeps students interested throughout the course
Practical approach to learning data science that prepares students for real-world applications
Experienced instructors with a wealth of knowledge and expertise in data science

Cons from User Reviews

Some users feel that the course is too basic and doesn't cover advanced topics
The course can be time-consuming and requires a significant amount of effort to complete
The pace of the course may be too fast for some users who are new to data science

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