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Josh Bernhard ,
Mike Yi,
Judit Lantos,
David Drummond,
Andrew Paster,
Juno Lee ,
and
Luis Serrano
Enroll in Udacity's Introduction to Data Science course and learn the fundamentals of data science including data manipulation, data analysis and more.
Prerequisite details
To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:
- Predictive analytics
- scikit-learn
- NumPy
- Basic statistical modeling
- Pandas
You will also need to be able to communicate fluently and professionally in written and spoken English.
Register for this course and see more details by visiting:
OpenCourser.com/course/x7y6wh/introduction
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What's inside
Syllabus
This lesson will give you an overview of the course, discuss pre-requisites and stakeholders.
In this lesson, you will learn about CRISP-DM and how you can apply it to many data science problems.
Read more
Syllabus
This lesson will give you an overview of the course, discuss pre-requisites and stakeholders.
In this lesson, you will learn about CRISP-DM and how you can apply it to many data science problems.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Provides a solid foundation for individuals interested in pursuing a career in data science
Taught by industry experts with extensive experience in data science
Emphasizes practical skills and hands-on experience through projects and exercises
Covers a comprehensive range of data science topics, including data manipulation, analysis, and visualization
Requires a strong foundation in programming, statistics, and mathematics
May require additional resources or support for individuals without prior knowledge in data science
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Save this course to your list so you can find it easily later.
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Reviews summary
Practical data science for prepared learners
According to students, this course offers a solid foundation in data science, emphasizing practical application and portfolio building. Many highlight the value of the CRISP-DM framework and the hands-on projects that lead to a tangible portfolio piece. However, a significant number of learners point out that despite its title, "Introduction to Data Science," the course has high prerequisites and is not suitable for absolute beginners. Those with existing knowledge in Python, Pandas, NumPy, and basic statistics find the pace appropriate and the content beneficial for career transition or skill enhancement.
Effectively teaches and applies the industry-standard CRISP-DM.
"This course provided a solid foundation in data science concepts, especially the CRISP-DM framework."
"The CRISP-DM methodology was very well explained and applied in the projects."
"I found the course to be a good bridge between theoretical understanding and practical application."
Best for those with a programming background looking to pivot.
"Excellent course for someone with a programming background looking to pivot into data science."
"As someone with some programming experience but new to data science, this course was perfect."
"If you know Python and basic stats, it's a decent refresher, but not a true intro."
Hands-on projects and portfolio building are a major strength.
"The projects were challenging but very practical and helped build my portfolio."
"The practical exercises and the focus on communication (Medium blog post) were invaluable."
"The final project helped me build a portfolio piece. The videos were concise and to the point."
Pace is suitable for prepared learners but rushed for others.
"Some parts felt a bit rushed."
"Content is generally good... but the pace assumes you're already quite familiar with the tools."
"Occasionally, I felt the need for more in-depth explanations on certain advanced topics."
Course title misleads as it requires significant prior knowledge.
"I signed up for an 'Introduction' but quickly realized it assumes a lot of prior knowledge."
"Misleading title. This is NOT an introduction. I struggled immensely because I didn't have the background in scikit-learn or predictive analytics they expected."
"It's definitely not for complete beginners; you need to be comfortable with Python, Pandas, and Scikit-learn already."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Introduction to Data Science with these
activities:
Review Pandas
Show steps
Review the basics of Pandas to strengthen your foundation in data manipulation.
Browse courses on
Pandas
Show steps
-
Go through the Pandas documentation
-
Complete a few tutorials on Pandas
Join a study group with other data science students
Show steps
Collaborate with peers, discuss concepts, and improve your understanding through peer learning.
Show steps
-
Find a study group or create one with classmates
-
Meet regularly to discuss course material
Practice data cleaning drills
Show steps
Practice cleaning data to improve your data wrangling skills.
Browse courses on
Data Cleaning
Show steps
-
Find a dataset with errors
-
Clean the dataset
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow a tutorial on creating data visualizations
Show steps
Learn how to create effective data visualizations to communicate insights.
Browse courses on
Data Visualization
Show steps
-
Choose a data visualization tool
-
Follow a tutorial on creating visualizations
Volunteer at a data science organization
Show steps
Gain practical experience and contribute to the community by volunteering at a data science organization.
Show steps
-
Find a data science organization to volunteer with
-
Commit to a few hours of volunteering each week
Write a blog post on a data science topic
Show steps
Share your knowledge and improve your writing skills by creating a blog post on a data science topic.
Show steps
-
Choose a data science topic
-
Research and write the blog post
Review Pandas
Show steps
Review the basics of Pandas to strengthen your foundation in data manipulation.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Go through the Pandas documentation
- Complete a few tutorials on Pandas
Join a study group with other data science students
Show steps
Collaborate with peers, discuss concepts, and improve your understanding through peer learning.
Show steps
Show steps
Show steps
- Find a study group or create one with classmates
- Meet regularly to discuss course material
Practice data cleaning drills
Show steps
Practice cleaning data to improve your data wrangling skills.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Find a dataset with errors
- Clean the dataset
Three other activities
Expand to see all activities and additional details
Show all six activities
Follow a tutorial on creating data visualizations
Show steps
Learn how to create effective data visualizations to communicate insights.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a data visualization tool
- Follow a tutorial on creating visualizations
Volunteer at a data science organization
Show steps
Gain practical experience and contribute to the community by volunteering at a data science organization.
Show steps
Show steps
Show steps
- Find a data science organization to volunteer with
- Commit to a few hours of volunteering each week
Write a blog post on a data science topic
Show steps
Share your knowledge and improve your writing skills by creating a blog post on a data science topic.
Show steps
Show steps
Show steps
- Choose a data science topic
- Research and write the blog post
Career center
Learners who complete Introduction to Data Science will develop knowledge and skills
that may be useful to these careers:
Data Engineer
Data Engineer
Data Engineers design and build data pipelines to ensure that data is available, reliable, and secure. This course provides a foundation in data manipulation and analysis, which are essential skills for Data Engineers.
Data Scientist
Data Scientist
Data Scientists apply statistical modeling and other techniques to extract insights from data. This course provides a foundation in data science, including data manipulation, analysis, and modeling.
Data Analyst
Data Analyst
Data Analysts bridge the gap between Data Scientists and business users. They typically perform basic data analysis, create dashboards, and communicate insights to stakeholders. This course may be helpful for those aspiring to become Data Analysts as it provides a foundation in data manipulation and analysis techniques.
Business Analyst
Business Analyst
Business Analysts use data to identify problems and opportunities for businesses. This course provides a foundation in data analysis and communication, which are essential skills for Business Analysts.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand market trends and customer behavior. This course provides a foundation in data analysis and communication, which are essential skills for Market Researchers.
Financial Analyst
Financial Analyst
Financial Analysts use data to evaluate investments and make financial recommendations. This course provides a foundation in data analysis and modeling, which are essential skills for Financial Analysts.
Risk Analyst
Risk Analyst
Risk Analysts use data to identify and assess risks. This course provides a foundation in data analysis and modeling, which are essential skills for Risk Analysts.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency and effectiveness of organizations. This course provides a foundation in data analysis and modeling, which are essential skills for Operations Research Analysts.
Actuary
Actuary
Actuaries use data to assess and manage risk. This course provides a foundation in data analysis and modeling, which are essential skills for Actuaries.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This course provides a foundation in basic statistical modeling, which is a core skill for Statisticians.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models to solve real-world problems. This course provides a foundation in predictive analytics and scikit-learn, which are essential skills for Machine Learning Engineers.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. This course provides a foundation in data analysis and modeling, which can be helpful for Software Engineers who work on data-intensive applications.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course provides a foundation in data analysis and communication, which can be helpful for Product Managers who need to understand customer needs and make data-informed decisions.
Management Consultant
Management Consultant
Management Consultants help organizations improve their performance. This course provides a foundation in data analysis and communication, which can be helpful for Management Consultants who need to understand business problems and make data-informed recommendations.
Auditor
Auditor
Auditors examine financial records to ensure accuracy and compliance. This course provides a foundation in data analysis and modeling, which can be helpful for Auditors who need to identify and assess risks.
For more career information including salaries, visit:
OpenCourser.com/course/x7y6wh/introduction
Reading list
We've selected eight books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Introduction to Data Science.
Save
Provides a comprehensive introduction to deep learning. It covers the key concepts and techniques used in deep learning, and how they can be applied to real-world problems.
Provides a practical guide to data science design. It covers the key principles and best practices for designing data science projects.
Provides a hands-on introduction to data science. It covers the entire data science pipeline, from data cleaning and preparation to modeling and visualization.
Provides a comprehensive introduction to deep learning using Python. It covers the key concepts and techniques used in deep learning, and how they can be applied to real-world problems.
Provides a practical introduction to data science for business professionals. It covers the key concepts and techniques used in data science, and how they can be applied to real-world business problems.
Save
Provides a practical guide to storytelling with data. It covers the key principles and techniques for creating effective data visualizations and communicating insights from data.
Save
Provides a comprehensive introduction to predictive analytics. It covers the key concepts and techniques used in predictive analytics, and how they can be applied to real-world problems.
Provides a gentle introduction to machine learning for those who have no prior experience with the field. It covers the basic concepts and techniques, and how they can be applied to real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/x7y6wh/introduction
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Similar courses
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Effort
3 weeks
Via
Udacity
Instructors
Josh Bernhard
Mike Yi
Judit Lantos
David Drummond
Andrew Paster
Juno Lee
Luis Serrano
Language
English
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