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Dr. Ali Feizollah

This course covers problem-solving approaches in data analysis, including descriptive, predictive, and prescriptive analysis. It also covers software tools selection, testing approaches, and approach selection, with tips and best practices.

Knowing how to analyze data and make informed decisions is crucial in many fields today. Whether in business, healthcare, social science, or any other field, the ability to collect and analyze data is a vital skill for success.

In this course, Certified Analytics Professional: Methodology Selection, you’ll gain the ability to analyze data and make informed decisions.

Read more

This course covers problem-solving approaches in data analysis, including descriptive, predictive, and prescriptive analysis. It also covers software tools selection, testing approaches, and approach selection, with tips and best practices.

Knowing how to analyze data and make informed decisions is crucial in many fields today. Whether in business, healthcare, social science, or any other field, the ability to collect and analyze data is a vital skill for success.

In this course, Certified Analytics Professional: Methodology Selection, you’ll gain the ability to analyze data and make informed decisions.

First, you’ll explore the techniques and types of descriptive analysis, including data visualization and the advantages of descriptive analysis.

Next, you’ll discover the types and advantages of predictive analysis.

Finally, you’ll learn how to choose appropriate types of prescriptive analysis, as well as how to select the best software tools and testing, and approach selections for data analysis.

When you’re finished with this course, you’ll have the skills and knowledge of methodology selection needed to analyze data and make informed decisions in your field.

Enroll now

What's inside

Syllabus

Course Overview
Problem-solving Approaches
Descriptive Analysis
Predictive Analysis
Read more
Prescriptive Analysis
Tips and Best Practices
Software Tools Selection
Testing Approaches
Conclusion
Approach Selection
Course Introduction - Original
Problem-solving Approaches - Original
Best Practices and Tips
Descriptive Analysis - Original
Introduction to Predictive Analytics - Original
Prescriptive Analysis - Original
Tips and Best Practices - Original
Conclusion - Original

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Develops decision-making abilities learners can use in various professional settings
Teaches analysis techniques used by professionals in the fields of business, healthcare, and social science
Presents strategies for collecting and analyzing data, a core skill in many fields
Includes tips and best practices to enhance learners' understanding of data analysis
Taught by Dr. Ali Feizollah, an experienced professional in data analysis

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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 Certified Analytics Professional: Methodology Selection with these activities:
Gather learning materials
Gather all necessary learning materials, including syllabus, course notes, and any additional resources.
Show steps
  • Review syllabus and course outline
  • Organize notes and materials
Read 'Data Analysis Using Regression and Multilevel/Hierarchical Models'
Enhance understanding of statistical principles and regression analysis techniques by reading this foundational text.
Show steps
  • Read chapters 1-5
  • Summarize key concepts
  • Complete end-of-chapter exercises
Complete online tutorials
Supplement course content by completing online tutorials on relevant topics, such as software tools selection or data analysis techniques.
Show steps
  • Identify relevant online tutorials
  • Complete tutorials at a steady pace
  • Take notes or summarize key concepts
Four other activities
Expand to see all activities and additional details
Show all seven activities
Participate in online discussion forums
Engage with peers and course instructors by actively participating in online discussion forums to clarify concepts and share insights.
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  • Post thoughtful questions and comments
  • Respond to peer posts
  • Summarize key takeaways
Create a data analysis portfolio
Demonstrate practical application of data analysis skills by creating a portfolio of projects showcasing different techniques and approaches.
Show steps
  • Identify data sources
  • Develop analysis plan
  • Execute analysis and visualize results
  • Write project report
Build a data visualization dashboard
Apply data visualization principles to create an interactive dashboard that effectively communicates insights from data analysis.
Show steps
  • Gather data and identify key metrics
  • Design and prototype dashboard layout
  • Develop dashboard using appropriate software
Volunteer as a tutor
Enhance understanding of course concepts by volunteering to tutor fellow students or participants in related online communities.
Show steps
  • Connect with learners seeking support
  • Identify areas where you can provide guidance
  • Prepare and organize tutoring sessions

Career center

Learners who complete Certified Analytics Professional: Methodology Selection will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use their knowledge of data analysis to build models that can predict future events. This course is a great way to learn the techniques used by Data Scientists, as it covers topics such as predictive analysis and model selection. The course also provides tips and best practices for testing and validating models, which is essential for Data Scientists.
Data Analyst
Data Analysts use their knowledge of data analysis to solve business problems, develop insights, and make recommendations. This course is an excellent way to develop the skills needed for this profession as it covers all of the key topics, including descriptive, predictive, and prescriptive analysis. The course also provides tips and best practices for selecting software tools and testing approaches, which are both essential for Data Analysts.
Business Analyst
Business Analysts use their knowledge of data analysis to help businesses make better decisions. This course is a great way to learn the skills needed for this profession, as it covers all of the key topics, including data visualization, predictive analysis, and prescriptive analysis. The course also provides tips and best practices for selecting software tools and testing approaches, which are both essential for Business Analysts.
Financial Analyst
Financial Analysts use their knowledge of data analysis to value companies and make investment recommendations. This course is a great way to learn the skills needed for this profession, as it covers all of the key topics, including descriptive, predictive, and prescriptive analysis. The course also provides tips and best practices for selecting software tools and testing approaches, which are both essential for Financial Analysts.
Statistician
Statisticians use their knowledge of data analysis to collect, analyze, and interpret data. This course is a great way to learn the skills needed for this profession, as it covers all of the key topics, including descriptive, predictive, and prescriptive analysis. The course also provides tips and best practices for selecting software tools and testing approaches, which are both essential for Statisticians.
Risk Analyst
Risk Analysts use their knowledge of data analysis to identify and mitigate risks. This course is a great way to learn the skills needed for this profession, as it covers all of the key topics, including descriptive, predictive, and prescriptive analysis. The course also provides tips and best practices for selecting software tools and testing approaches, which are both essential for Risk Analysts.
Quantitative Analyst
Quantitative Analysts use their knowledge of data analysis to develop trading strategies and make investment recommendations. This course is a great way to learn the skills needed for this profession, as it covers all of the key topics, including descriptive, predictive, and prescriptive analysis. The course also provides tips and best practices for selecting software tools and testing approaches, which are both essential for Quantitative Analysts.
Market Researcher
Market Researchers use their knowledge of data analysis to understand consumer behavior and trends. This course is a great way to learn the skills needed for this profession, as it covers all of the key topics, including data visualization, predictive analysis, and prescriptive analysis. The course also provides tips and best practices for selecting software tools and testing approaches, which are both essential for Market Researchers.
Operations Research Analyst
Operations Research Analysts use their knowledge of data analysis to improve the efficiency of business operations. This course is a great way to learn the skills needed for this profession, as it covers all of the key topics, including descriptive, predictive, and prescriptive analysis. The course also provides tips and best practices for selecting software tools and testing approaches, which are both essential for Operations Research Analysts.
Machine Learning Engineer
Machine Learning Engineers use their knowledge of data analysis to build and deploy machine learning models. This course may be helpful for those interested in this profession, as it covers some of the key topics, such as predictive analysis and model selection. However, it does not cover all of the skills needed for this profession, such as machine learning algorithms and deep learning.
Data Engineer
Data Engineers use their knowledge of data analysis to design and build data pipelines. This course may be helpful for those interested in this profession, as it covers some of the key topics, including data visualization, predictive analysis, and prescriptive analysis. However, it does not cover all of the skills needed for this profession, such as data warehousing and data integration.
Database Administrator
Database Administrators use their knowledge of data analysis to design and build databases. This course may be helpful for those interested in this profession, as it covers some of the key topics, such as data visualization and predictive analysis. However, it does not cover all of the skills needed for this profession, such as database design and database administration.
Data Architect
Data Architects use their knowledge of data analysis to design and build data warehouses. This course may be helpful for those interested in this profession, as it covers some of the key topics, such as data visualization and predictive analysis. However, it does not cover all of the skills needed for this profession, such as data warehousing and data integration.
Software Engineer
Software Engineers use their knowledge of data analysis to design and build software applications. This course may be helpful for those interested in this profession, as it covers some of the key topics, such as data visualization and predictive analysis. However, it does not cover all of the skills needed for this profession, such as software development and software testing.
Web Developer
Web Developers use their knowledge of data analysis to design and build websites. This course may be helpful for those interested in this profession, as it covers some of the key topics, such as data visualization and predictive analysis. However, it does not cover all of the skills needed for this profession, such as web design and web development.

Reading list

We've selected 18 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 Certified Analytics Professional: Methodology Selection.
Provides a comprehensive overview of data mining techniques and algorithms, and it valuable resource for students and practitioners who want to learn more about this field.
Provides a comprehensive overview of deep learning techniques and algorithms, and it valuable resource for students and practitioners who want to learn more about this field.
Provides a comprehensive overview of causal inference techniques and methods, and it valuable resource for students and practitioners who want to learn more about this field.
Provides a comprehensive overview of Bayesian data analysis techniques and methods, and it valuable resource for students and practitioners who want to learn more about this field.
Provides a comprehensive guide to data science project design and implementation, offering additional insights into the course's problem-solving approaches and methodology selection.
Provides a comprehensive overview of predictive analytics techniques, and it valuable resource for students and practitioners who want to learn more about this field.
Provides a comprehensive overview of reinforcement learning techniques and algorithms, and it valuable resource for students and practitioners who want to learn more about this field.
Offers a comprehensive overview of business analytics, providing additional context for the course's coverage of problem-solving approaches and data analysis techniques.
Classic introduction to machine learning, and it great resource for students and practitioners who want to learn more about the fundamental concepts of machine learning.
Provides a practical introduction to Bayesian data analysis techniques and methods, and it great resource for students and practitioners who want to learn how to use Bayesian data analysis to solve real-world problems.
Provides a practical introduction to data science techniques, and it great resource for students and practitioners who want to learn how to use data science to solve business problems.
Offers a practical guide to predictive modeling, complementing the course's coverage of predictive analysis techniques.
Serves as a practical guide to data science using Python, complementing the course's coverage of software tools selection.
Provides insights into the ethical implications of data analysis, complementing the course's emphasis on best practices and responsible data usage.
Provides a practical introduction to data visualization techniques, and it great resource for students and practitioners who want to learn how to use data visualization to communicate insights.

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