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This course features interactive videos to help you understand both the analytical concepts and the software. This course contains a capstone project, in which learners will apply the concepts taught using a separate data source; this realistic project gives learners the practice and confidence necessary to connect, explore, and analyze data sources into the future.This course is also part of a certificate:
Read more
This course features interactive videos to help you understand both the analytical concepts and the software. This course contains a capstone project, in which learners will apply the concepts taught using a separate data source; this realistic project gives learners the practice and confidence necessary to connect, explore, and analyze data sources into the future.This course is also part of a certificate:
Read more
This course features interactive videos to help you understand both the analytical concepts and the software. This course contains a capstone project, in which learners will apply the concepts taught using a separate data source; this realistic project gives learners the practice and confidence necessary to connect, explore, and analyze data sources into the future.This course is also part of a certificate:
Data Analytics and Visualization Certificate
Register for this course and see more details by visiting:
OpenCourser.com/course/mjlt7j/data
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What's inside
Learning objective
The types of algorithms covered are used for uncovering more complex business insights than possible using descriptive statistics (of the type covered in courses 1 and 2). by learning how to build a predictive model in python, you will gain the capability to better describe the relationships between multiple variables in a dataset. while typically this content depends on an advanced understanding of statistics, this course teaches how to build and interpret models without needing a statistics prerequisite by focusing on the most essential aspects of predictive modeling.
The types of algorithms covered are used for uncovering more complex business insights than possible using descriptive statistics (of the type covered in courses 1 and 2). by learning how to build a predictive model in python, you will gain the capability to better describe the relationships between multiple variables in a dataset. while typically this content depends on an advanced understanding of statistics, this course teaches how to build and interpret models without needing a statistics prerequisite by focusing on the most essential aspects of predictive modeling.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides a capstone project to help learners practice connecting, exploring, and analyzing data sources
Involves using Python to build a predictive model, enabling learners to describe relationships between variables in a dataset
Emphasizes the essential aspects of predictive modeling, making it accessible to learners without a statistics prerequisite
Forms part of a certificate in Data Analytics and Visualization, indicating a broader educational journey
Utilizes interactive videos to enhance understanding of analytical concepts and software
Save this course
Save Data Analytics Methods to your list so you can find it easily later:
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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 Data Analytics Methods with these
activities:
Review core concepts of statistics and probability
Show steps
Review the foundational statistical concepts that underpin predictive modeling techniques, including descriptive statistics, probability distributions, and hypothesis testing.
Browse courses on
Mathematical Statistics
Show steps
-
Revisit your notes or textbook from previous statistics courses.
-
Take practice quizzes or online assessments to test your understanding.
-
Work through example problems and exercises to reinforce your knowledge.
Explore Python libraries for data analysis and visualization
Show steps
Familiarize yourself with the essential Python libraries and tools used for data manipulation, analysis, and visualization. This will provide a solid foundation for the hands-on assignments and projects in the course.
Browse courses on
Pandas
Show steps
-
Follow online tutorials or documentation to learn the basics of these libraries.
-
Experiment with different functions and methods using Jupyter Notebooks or interactive coding environments.
-
Load and explore sample datasets to practice data manipulation and visualization techniques.
Mentor or tutor other students in the course
Show steps
Mentoring or tutoring other students will reinforce your understanding of the course concepts while developing your leadership and communication skills.
Browse courses on
Mentoring
Show steps
-
Identify students who may benefit from your support.
-
Offer your assistance and schedule regular sessions.
-
Review course materials, answer questions, and provide guidance.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve coding challenges and practice exercises
Show steps
Regular practice with coding challenges and exercises will enhance your proficiency in Python programming and sharpen your skills in applying predictive modeling techniques.
Browse courses on
Python Coding
Show steps
-
Participate in online coding challenges or platforms like LeetCode or HackerRank.
-
Work through practice exercises provided in the course materials or textbooks.
-
Create your own small-scale projects to apply the concepts you're learning.
Develop a tutorial or blog post on a specific predictive modeling technique
Show steps
Creating a tutorial or blog post will deepen your understanding of a specific predictive modeling technique and enhance your communication skills by explaining complex concepts in a clear and concise manner.
Browse courses on
Technical Writing
Show steps
-
Choose a specific technique or concept to focus on.
-
Research and gather information from reliable sources.
-
Outline and structure your tutorial or blog post.
-
Write the content, ensuring clarity and logical flow.
-
Proofread and edit your work before sharing it with others.
Build a predictive model using a real-world dataset
Show steps
Putting your knowledge into practice by building a predictive model on a real-world dataset will solidify your understanding of the concepts and provide valuable hands-on experience for your portfolio.
Show steps
-
Identify a suitable dataset and define your project goals.
-
Preprocess and explore the data to gain insights.
-
Select and train a predictive model using Python libraries.
-
Evaluate the performance of your model using appropriate metrics.
-
Document your project and share your findings in a report or presentation.
Review core concepts of statistics and probability
Show steps
Review the foundational statistical concepts that underpin predictive modeling techniques, including descriptive statistics, probability distributions, and hypothesis testing.
Browse courses on
Mathematical Statistics
Show steps
Show steps
Show steps
- Revisit your notes or textbook from previous statistics courses.
- Take practice quizzes or online assessments to test your understanding.
- Work through example problems and exercises to reinforce your knowledge.
Explore Python libraries for data analysis and visualization
Show steps
Familiarize yourself with the essential Python libraries and tools used for data manipulation, analysis, and visualization. This will provide a solid foundation for the hands-on assignments and projects in the course.
Browse courses on
Pandas
Show steps
Show steps
Show steps
- Follow online tutorials or documentation to learn the basics of these libraries.
- Experiment with different functions and methods using Jupyter Notebooks or interactive coding environments.
- Load and explore sample datasets to practice data manipulation and visualization techniques.
Mentor or tutor other students in the course
Show steps
Mentoring or tutoring other students will reinforce your understanding of the course concepts while developing your leadership and communication skills.
Browse courses on
Mentoring
Show steps
Show steps
Show steps
- Identify students who may benefit from your support.
- Offer your assistance and schedule regular sessions.
- Review course materials, answer questions, and provide guidance.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve coding challenges and practice exercises
Show steps
Regular practice with coding challenges and exercises will enhance your proficiency in Python programming and sharpen your skills in applying predictive modeling techniques.
Browse courses on
Python Coding
Show steps
Show steps
Show steps
- Participate in online coding challenges or platforms like LeetCode or HackerRank.
- Work through practice exercises provided in the course materials or textbooks.
- Create your own small-scale projects to apply the concepts you're learning.
Develop a tutorial or blog post on a specific predictive modeling technique
Show steps
Creating a tutorial or blog post will deepen your understanding of a specific predictive modeling technique and enhance your communication skills by explaining complex concepts in a clear and concise manner.
Browse courses on
Technical Writing
Show steps
Show steps
Show steps
- Choose a specific technique or concept to focus on.
- Research and gather information from reliable sources.
- Outline and structure your tutorial or blog post.
- Write the content, ensuring clarity and logical flow.
- Proofread and edit your work before sharing it with others.
Build a predictive model using a real-world dataset
Show steps
Putting your knowledge into practice by building a predictive model on a real-world dataset will solidify your understanding of the concepts and provide valuable hands-on experience for your portfolio.
Show steps
Show steps
Show steps
- Identify a suitable dataset and define your project goals.
- Preprocess and explore the data to gain insights.
- Select and train a predictive model using Python libraries.
- Evaluate the performance of your model using appropriate metrics.
- Document your project and share your findings in a report or presentation.
Career center
Learners who complete Data Analytics Methods 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 identify trends and patterns. This course provides a strong foundation in data analytics methods, including supervised and unsupervised learning, which can help Data Analysts build predictive models and uncover insights from data.
Data Scientist
Data Scientist
Data Scientists use advanced statistical and machine learning techniques to solve complex business problems. This course provides a comprehensive introduction to data science methods, including model building and interpretation, which can help Data Scientists develop the skills needed to succeed in this role.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design and implement machine learning models for a variety of applications. This course provides a solid foundation in machine learning algorithms and techniques, which can help Machine Learning Engineers build and deploy effective machine learning solutions.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. This course provides a strong foundation in data analytics methods, which can help Business Analysts develop the skills needed to analyze data and make informed business decisions.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to provide insights and make predictions. This course provides a comprehensive introduction to statistical methods, including probability, inference, and regression, which can help Statisticians develop the skills needed to succeed in this role.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines to collect, clean, and store data. This course provides a foundation in data engineering concepts and technologies, which can help Data Engineers develop the skills needed to manage and maintain data infrastructure.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a solid foundation in Python programming, which can help Software Engineers build and deploy data analytics solutions.
Product Manager
Product Manager
Product Managers are responsible for developing and managing products. This course provides a foundation in data analytics methods, which can help Product Managers understand user behavior and make informed product decisions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to assess risk and make investment decisions. This course provides a solid foundation in data analytics methods, which can help Quantitative Analysts develop the skills needed to succeed in this role.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and make financial decisions. This course provides a foundation in predictive modeling, which can help Actuaries develop the skills needed to build and interpret models for risk assessment.
Financial Analyst
Financial Analyst
Financial Analysts use data to analyze financial statements and make investment recommendations. This course provides a foundation in data analytics methods, which can help Financial Analysts develop the skills needed to analyze financial data and make informed investment decisions.
Market Researcher
Market Researcher
Market Researchers collect and analyze data to understand consumer behavior and market trends. This course provides a foundation in data analytics methods, which can help Market Researchers develop the skills needed to design and conduct market research studies.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data to analyze and improve business processes. This course provides a foundation in data analytics methods, which can help Operations Research Analysts develop the skills needed to build and implement models for process improvement.
Supply Chain Analyst
Supply Chain Analyst
Supply Chain Analysts use data to analyze and improve supply chain performance. This course provides a foundation in data analytics methods, which can help Supply Chain Analysts develop the skills needed to build and implement models for supply chain optimization.
Risk Manager
Risk Manager
Risk Managers use data to identify, assess, and mitigate risks. This course provides a foundation in predictive modeling, which can help Risk Managers develop the skills needed to build and interpret models for risk assessment.
For more career information including salaries, visit:
OpenCourser.com/course/mjlt7j/data
Reading list
We've selected 12 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
Data Analytics Methods.
Provides a comprehensive overview of machine learning techniques using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow. It valuable resource for learners who want to gain practical experience with machine learning projects.
Save
Provides a comprehensive overview of deep learning concepts and algorithms, covering both the theoretical foundations and practical applications. It valuable resource for learners who want to gain a deeper understanding of deep learning techniques and tools.
Provides a comprehensive overview of machine learning techniques in Python, covering both the theoretical foundations and practical applications. It valuable resource for learners who want to gain a deeper understanding of machine learning techniques and tools.
Provides a comprehensive overview of machine learning techniques in R, covering both the theoretical foundations and practical applications. It valuable resource for learners who want to gain a deeper understanding of machine learning techniques and tools.
Provides a comprehensive overview of data analysis techniques in Python, covering both the theoretical foundations and practical applications. It valuable resource for learners who want to gain a deeper understanding of data analysis techniques and tools.
Provides a practical introduction to data science concepts and algorithms, focusing on building data science projects from scratch. It good choice for learners who want to gain hands-on experience with data science techniques and tools.
Save
Provides a comprehensive overview of data mining techniques in R, covering both the theoretical foundations and practical applications. It valuable resource for learners who want to gain a deeper understanding of data mining techniques and tools.
Save
Provides a gentle introduction to machine learning concepts and algorithms in Python. It good choice for learners who are new to machine learning and want to build a solid foundation.
Provides a practical introduction to machine learning concepts and algorithms in Python. It good choice for learners who are new to machine learning and want to build a solid foundation.
Save
Provides a comprehensive overview of data analysis techniques in JavaScript, covering both the theoretical foundations and practical applications. It valuable resource for learners who want to gain a deeper understanding of data analysis techniques and tools.
Provides a gentle introduction to machine learning concepts and algorithms for non-technical readers. It good choice for learners who are new to machine learning and want to understand the basics.
Provides a gentle introduction to machine learning concepts and algorithms for non-technical readers. It good choice for learners who are new to machine learning and want to understand the basics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mjlt7j/data
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Effort
1 to 11 hours per week for 5 weeks
Level
Introductory
Via
edX
Institution
Arizona State University
Session
February 12, 2024
- December 12, 2024
Language
English
Transcript
English
Good to know
Provides a capstone project to help learners practice connecting, exploring, and analyzing data sources
Involves using Python to build a predictive model, enabling learners to describe relationships between variables in a dataset
Emphasizes the essential aspects of predictive modeling, making it accessible to learners without a statistics prerequisite
Forms part of a certificate in Data Analytics and Visualization, indicating a broader educational journey
Utilizes interactive videos to enhance understanding of analytical concepts and software
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