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Cecia Ki CHAN
Foundations of Data Analytics: This course will provide fundamental techniques for data analytics, including data collection, data extraction, data integration, data cleansing, and basic machine learning techniques. The learners will learn how to manage and optimize the analytics value chain, including collecting and extracting the suitable values, selecting the right data processing, integrating the data from various resources, with programming tools such as Python. This course will also introduce data security and privacy.
Register for this course and see more details by visiting:
OpenCourser.com/course/j8h83m/foundations
What's inside
Learning objectives
- Basic statistical analysis
- The essentials of data preprocessing
- Basic machine learning techniques
- Understanding data integration
- An introduction to data security and privacy
- Python programming
- Python libraries for data preprocessing and analysis
- Basic statistical analysis
- The essentials of data preprocessing
- Basic machine learning techniques
- Understanding data integration
- An introduction to data security and privacy
- Python programming
- Python libraries for data preprocessing and analysis
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Introduces data security and privacy, which is standard in industry today
Explores basic statistical analysis, which is foundation for many data-driven fields
Develops Python programming, which is a core skill for data analytics engineers
Teaches data integration, which helps learners connect data from various sources
Advises students to take other courses first, which may be a barrier to enrollment
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Reviews summary
Foundational data analytics with practical python
According to learners, "Foundations of Data Analytics" serves as an excellent foundation for aspiring data professionals, especially those with little to no prior programming experience. Students consistently highlight the hands-on Python modules, including practical exercises and labs, as particularly effective for solidifying understanding of data collection, cleansing, and preprocessing. The instructor's clear and engaging teaching style is widely praised. While the course excels in introducing core concepts, some learners found the machine learning section superficial and desired more advanced content or real-world datasets, indicating it's best suited for absolute beginners rather than those seeking in-depth advanced topics.
Instructor's teaching style is clear and engaging.
"The instructor was clear and engaging, making complex topics easy to grasp."
"The instructor explained concepts clearly."
"The lectures were easy to follow."
Emphasizes hands-on Python for data tasks.
"The Python modules were incredibly hands-on and practical."
"Absolutely loved the practical exercises and labs. They solidified my understanding of data collection and preprocessing using Python libraries."
"Python was taught from scratch, and the practical assignments truly built confidence."
Best suited for those with no prior experience.
"Highly recommend for beginners!"
"Good introduction to Python for data. I'd recommend it only if you have absolutely zero background."
"As someone without a strong programming background, this course was a godsend."
Provides an excellent starting point for beginners.
"This course provided an excellent foundation in data analytics."
"Overall, a very solid course for beginners."
"Fantastic for a complete beginner like me! It truly built a strong foundation."
Could benefit from more practical data examples.
"The content is relevant, but the examples sometimes felt contrived. I expected more real-world datasets."
Some learners found the pace uneven.
"I found the pace inconsistent. Some modules were very basic, while others... jumped too quickly."
"I wish there were more advanced topics or challenges, as it felt a bit too basic towards the end."
ML section lacks depth, feeling rushed.
"Some parts felt rushed, especially the machine learning section. I felt it only scratched the surface."
"My main gripe is that the machine learning bit was super superficial."
"Others, like the ML intro, jumped too quickly without enough background."
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 Foundations of Data Analytics with these
activities:
Review Python basics
Show steps
Starting from scratch with Python will help you acclimate quicker to the materials.
Browse courses on
Programming Essentials
Show steps
-
Read through the Python Tutorial
-
Do the Python exercises from a textbook or online resource
Practice data analysis problems
Show steps
This type of practice will help you develop your data analysis skills and improve your understanding of the concepts taught in this course.
Browse courses on
Data Analysis Techniques
Show steps
-
Find a set of data analysis practice problems
-
Solve the data analysis problems using the techniques you've learned in the course
Attend data analysis workshops
Show steps
These workshops will provide you with hands-on experience with data analysis tools and techniques and develop new skills and knowledge.
Browse courses on
Data Science Techniques
Show steps
-
Find a workshop on a topic you're interested in
-
Register for the workshop and attend all sessions
Two other activities
Expand to see all activities and additional details
Show all five activities
Build a data analysis project
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This project will allow you to apply the skills you learn in this course to a real-world problem.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset and a problem to solve
-
Clean and prepare your data
-
Analyze your data and build a model
-
Visualize your results and communicate your findings
Mentor a junior data scientist
Show steps
This will help you reinforce your understanding of data analysis concepts and develop your leadership skills.
Browse courses on
Knowledge Sharing
Show steps
-
Find a junior data scientist who you can mentor
-
Meet with your mentee regularly to provide guidance and support
Review Python basics
Show steps
Starting from scratch with Python will help you acclimate quicker to the materials.
Browse courses on
Programming Essentials
Show steps
Show steps
Show steps
- Read through the Python Tutorial
- Do the Python exercises from a textbook or online resource
Practice data analysis problems
Show steps
This type of practice will help you develop your data analysis skills and improve your understanding of the concepts taught in this course.
Browse courses on
Data Analysis Techniques
Show steps
Show steps
Show steps
- Find a set of data analysis practice problems
- Solve the data analysis problems using the techniques you've learned in the course
Attend data analysis workshops
Show steps
These workshops will provide you with hands-on experience with data analysis tools and techniques and develop new skills and knowledge.
Browse courses on
Data Science Techniques
Show steps
Show steps
Show steps
- Find a workshop on a topic you're interested in
- Register for the workshop and attend all sessions
Two other activities
Expand to see all activities and additional details
Show all five activities
Build a data analysis project
Show steps
This project will allow you to apply the skills you learn in this course to a real-world problem.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset and a problem to solve
- Clean and prepare your data
- Analyze your data and build a model
- Visualize your results and communicate your findings
Mentor a junior data scientist
Show steps
This will help you reinforce your understanding of data analysis concepts and develop your leadership skills.
Browse courses on
Knowledge Sharing
Show steps
Show steps
Show steps
- Find a junior data scientist who you can mentor
- Meet with your mentee regularly to provide guidance and support
Career center
Learners who complete Foundations of Data Analytics will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make better decisions. The Foundations of Data Analytics course provides a strong foundation in the skills and techniques needed to be successful in this role. These skills include data collection, data extraction, data integration, data cleansing, and basic machine learning techniques. The course also introduces data security and privacy, which are important considerations for Data Analysts.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. The Foundations of Data Analytics course provides a strong foundation in the skills and techniques needed to be successful in this role. These skills include data collection, data extraction, data integration, data cleansing, and basic machine learning techniques. The course also introduces data security and privacy, which are important considerations for Machine Learning Engineers.
Data Scientist
Data Scientist
Data Scientists are responsible for using data to solve business problems. The Foundations of Data Analytics course provides a strong foundation in the skills and techniques needed to be successful in this role. These skills include data collection, data extraction, data integration, data cleansing, and basic machine learning techniques. The course also introduces data security and privacy, which are important considerations for Data Scientists.
Business Analyst
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. The Foundations of Data Analytics course provides a strong foundation in the skills and techniques needed to be successful in this role. These skills include data collection, data extraction, data integration, data cleansing, and basic machine learning techniques. The course also introduces data security and privacy, which are important considerations for Business Analysts.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. The Foundations of Data Analytics course provides a strong foundation in the skills and techniques needed to be successful in this role. These skills include data collection, data extraction, data integration, data cleansing, and basic machine learning techniques. The course also introduces data security and privacy, which are important considerations for Statisticians.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data pipelines. The Foundations of Data Analytics course provides a strong foundation in the skills and techniques needed to be successful in this role. These skills include data collection, data extraction, data integration, data cleansing, and basic machine learning techniques. The course also introduces data security and privacy, which are important considerations for Data Engineers.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. The Foundations of Data Analytics course may be useful for Software Engineers who want to develop data-driven applications. The course provides a foundation in data collection, data extraction, data integration, data cleansing, and basic machine learning techniques.
Database Administrator
Database Administrator
Database Administrators are responsible for managing and maintaining databases. The Foundations of Data Analytics course may be useful for Database Administrators who want to learn how to use data analytics techniques to improve the performance of their databases. The course provides a foundation in data collection, data extraction, data integration, data cleansing, and basic machine learning techniques.
Project Manager
Project Manager
Project Managers are responsible for planning, executing, and closing projects. The Foundations of Data Analytics course may be useful for Project Managers who want to learn how to use data analytics techniques to improve the efficiency and effectiveness of their projects. The course provides a foundation in data collection, data extraction, data integration, data cleansing, and basic machine learning techniques.
Product Manager
Product Manager
Product Managers are responsible for managing the development and launch of new products. The Foundations of Data Analytics course may be useful for Product Managers who want to learn how to use data analytics techniques to improve the quality and success of their products. The course provides a foundation in data collection, data extraction, data integration, data cleansing, and basic machine learning techniques.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. The Foundations of Data Analytics course may be useful for Marketing Managers who want to learn how to use data analytics techniques to improve the effectiveness of their campaigns. The course provides a foundation in data collection, data extraction, data integration, data cleansing, and basic machine learning techniques.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and managing sales teams. The Foundations of Data Analytics course may be useful for Sales Managers who want to learn how to use data analytics techniques to improve the performance of their teams. The course provides a foundation in data collection, data extraction, data integration, data cleansing, and basic machine learning techniques.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making investment recommendations. The Foundations of Data Analytics course may be useful for Financial Analysts who want to learn how to use data analytics techniques to improve the accuracy of their analyses and recommendations. The course provides a foundation in data collection, data extraction, data integration, data cleansing, and basic machine learning techniques.
Consultant
Consultant
Consultants are responsible for providing advice and guidance to businesses on a variety of topics. The Foundations of Data Analytics course may be useful for Consultants who want to learn how to use data analytics techniques to improve the quality of their advice and guidance. The course provides a foundation in data collection, data extraction, data integration, data cleansing, and basic machine learning techniques.
Entrepreneur
Entrepreneur
Entrepreneurs are responsible for starting and running their own businesses. The Foundations of Data Analytics course may be useful for Entrepreneurs who want to learn how to use data analytics techniques to improve the success of their businesses. The course provides a foundation in data collection, data extraction, data integration, data cleansing, and basic machine learning techniques.
For more career information including salaries, visit:
OpenCourser.com/course/j8h83m/foundations
Reading list
We've selected 11 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
Foundations of Data Analytics.
Provides a comprehensive introduction to Python programming for data analysis. It covers topics such as data structures, data manipulation, and data visualization.
Provides a comprehensive introduction to machine learning techniques, which are used for data analysis and prediction. It valuable resource for learners who want to develop their skills in machine learning.
Provides a comprehensive overview of using Python for data analysis, covering data manipulation, data visualization, and machine learning. It valuable resource for those looking to learn or enhance their Python skills for data analytics.
Save
Provides an overview of big data analytics. It covers topics such as big data, data analytics, and the impact of big data on society.
Save
This textbook provides a comprehensive overview of data mining concepts and techniques, including data preprocessing, clustering, classification, and association rule mining. It valuable resource for students and professionals seeking a deeper understanding of data mining.
Provides a practical introduction to data science, using Python. It covers topics such as data collection, data preprocessing, data analysis, and data visualization.
Provides a comprehensive introduction to machine learning with Python. It covers topics such as supervised learning, unsupervised learning, and deep learning.
This textbook provides a comprehensive overview of probability theory, covering the basic concepts, theorems, and applications. It valuable resource for those seeking a strong foundation in probability.
Save
Provides a practical introduction to data visualization, covering the principles and techniques for creating effective and informative data visualizations. It valuable resource for those looking to improve their data visualization skills.
Provides a beginner-friendly introduction to data analytics. It covers topics such as data collection, data preprocessing, and data visualization.
Provides a business-oriented introduction to data science. It covers topics such as data collection, data analysis, and data visualization.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/j8h83m/foundations
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Rating
4.4
Effort
6 to 10 hours per week for 8 weeks
Level
Intermediate
Via
edX
Institution
The Hong Kong University of Science and Technology
Instructor
Cecia Ki CHAN
Language
English
Transcript
English
This course belongs to a
collection
called
Big Data Technology. It's comprised of five courses:
- Big Data Technology Capstone Project
- Big Data Computing with Spark
- Foundations of Data Analytics (viewing)
- Data Mining and Knowledge Discovery
- Mathematical Methods for Data Analysis
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