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Athanasia Lampraki
and
Evangelia Katsikea
This course focuses on the analysis and interpretation of data. The focus will be placed on data preparation and description and quantitative and qualitative data analysis. The course commences with a discussion of data preparation, scale internal consistency, appropriate data analysis and the Pearson correlation. We will look at statistics that can be used to investigate relationships and discuss statistics for investigating relationships with a focus on multiple regression. The course continues with a focus on logistic regression, exploratory factor analysis and the outcome of factor analysis. We are going to explore how to conduct an experiment and an observational study, as well as content analysis and the use of digital analytics in market research. The course ends with a consideration of digital analytics, with an emphasis on digital brand analysis, audience analysis, digital ecosystem analysis, Return on Investment (ROI), and the role of digital analytics in market research.
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What's inside
Syllabus
Week 1
This week commences with a discussion of data preparation and scale internal consistency. We will then look at appropriate data analysis and the Pearson correlation. The week ends with a focus on statistics that can be used to investigate relationships.
Read more
Syllabus
Week 1
This week commences with a discussion of data preparation and scale internal consistency. We will then look at appropriate data analysis and the Pearson correlation. The week ends with a focus on statistics that can be used to investigate relationships.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops both qualitative and quantitative skills, which are core for becoming a marketing analyst
Taught by instructors who are recognized for their work at Cranfield School of Management
Covers concepts that are highly relevant to the marketing industry
Provides a strong foundation for learners with no prior knowledge of data analysis
May require learners to have access to statistical software, which can be costly
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Reviews summary
Foundations in data analysis & digital insights
According to learners, this course offers a solid foundation in data analysis and interpretation. Many appreciate its clear explanations of complex statistical concepts, from Pearson correlation to various regression techniques. The course is particularly praised for its highly relevant modules on digital analytics, covering aspects like ROI, brand analysis, and market research, which are found actionable for professional roles. While the weekly structure is logical and generally accessible for beginners to intermediate learners, some feedback suggests a desire for more hands-on exercises and practical software applications, as certain advanced topics like exploratory factor analysis may feel rushed for those seeking deeper dives. Overall, it's a comprehensive and well-organized overview.
Well-suited for those new to advanced data analysis concepts.
"Highly recommend for beginners to intermediate learners."
"Absolutley brilliant! As someone new to advanced statistics, this course made complex topics like logistic regression accessible."
"Good for true beginners, but not for those with prior experience in data analysis."
Complex statistical topics are broken down and made accessible.
"The explanations were clear, and the examples were highly relevant."
"As someone new to advanced statistics, this course made complex topics like logistic regression accessible."
"The way they broke down complex statistical concepts into digestible modules was impressive."
Provides a strong overview of essential data analysis techniques.
"This course provided a really solid foundation in data analysis techniques, from basic correlations to more complex regressions."
"A very comprehensive course. The coverage of multiple regression and factor analysis was quite good."
"It gives a good overview of many data analysis techniques."
Highly relevant focus on digital analytics for market research.
"I especially appreciated the modules on digital analytics, which are very practical for my marketing role."
"The section on digital analytics, particularly ROI and market research, was very relevant to my career."
"The digital analytics section is a strong point, very timely and relevant."
Could benefit from more practical exercises or software application.
"I felt some parts could have had more hands-on exercises or practical software applications."
"I felt that the explanation of software tools was minimal, which would have helped tremendously for practical application."
"It's more theoretical, which is fine, but I prefer more hands-on."
Covers many topics but may lack depth for advanced learners.
"While topics like Pearson correlation and basic regression are covered adequately, the later sections on exploratory factor analysis felt rushed."
"It's a good overview, but don't expect deep dives into every method. I felt I needed to supplement with external resources."
"As a seasoned analyst, I was hoping for more advanced techniques or case studies. While it provides a broad overview, it lacks the depth I was looking for."
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 Analysis and Interpretation of Data with these
activities:
Read a book on data analysis
Show steps
This activity will help you to learn more about the theory and practice of data analysis.
View
Data Analysis: A Bayesian Tutorial
on Amazon
Show steps
-
Read the book.
Review the basics of statistics
Show steps
This activity will help you refresh your knowledge of the basics of statistics, which will be essential for understanding the material in this course.
Browse courses on
Statistics
Show steps
-
Review your notes from a previous statistics course.
-
Complete a few practice problems on basic statistical concepts.
Complete practice problems on statistical concepts
Show steps
This activity will help you practice applying statistical concepts to real-world data.
Browse courses on
Data Analysis
Show steps
-
Find a set of practice problems on statistical concepts.
-
Work through the problems and check your answers.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group or discussion forum
Show steps
This activity will allow you to discuss the material with other students and get help with any concepts that you are struggling with.
Show steps
-
Find a study group or discussion forum for this course.
-
Participate in the group discussions and ask questions as needed.
Follow a tutorial on a specific statistical technique
Show steps
This activity will help deepen your understanding of specific statistical techniques that will be covered in this course.
Browse courses on
Multiple Regression
Show steps
-
Choose a statistical technique that you want to learn more about.
-
Find a tutorial on that technique.
-
Follow the tutorial and complete the exercises.
Create a data visualization
Show steps
This activity will help you apply the concepts of data visualization to real-world data.
Browse courses on
Data Visualization
Show steps
-
Choose a dataset that you are interested in.
-
Use a data visualization tool to create a visualization of the data.
-
Write a brief report that describes your visualization and what it reveals about the data.
Start a data analysis project
Show steps
This activity will allow you to apply your skills to a real-world problem and demonstrate your understanding of the material covered in this course.
Show steps
-
Choose a data analysis project that you are interested in.
-
Gather the data you need for your project.
-
Clean and prepare the data.
-
Analyze the data.
-
Write a report that describes your project and your findings.
Participate in a data science competition
Show steps
This activity will allow you to put your skills to the test and compete with other data scientists.
Browse courses on
Data Science
Show steps
-
Find a data science competition that you are interested in.
-
Develop a model to solve the problem.
-
Submit your model to the competition.
Read a book on data analysis
Show steps
This activity will help you to learn more about the theory and practice of data analysis.
View
Data Analysis: A Bayesian Tutorial
on Amazon
Show steps
Show steps
Show steps
- Read the book.
Review the basics of statistics
Show steps
This activity will help you refresh your knowledge of the basics of statistics, which will be essential for understanding the material in this course.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review your notes from a previous statistics course.
- Complete a few practice problems on basic statistical concepts.
Complete practice problems on statistical concepts
Show steps
This activity will help you practice applying statistical concepts to real-world data.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find a set of practice problems on statistical concepts.
- Work through the problems and check your answers.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group or discussion forum
Show steps
This activity will allow you to discuss the material with other students and get help with any concepts that you are struggling with.
Show steps
Show steps
Show steps
- Find a study group or discussion forum for this course.
- Participate in the group discussions and ask questions as needed.
Follow a tutorial on a specific statistical technique
Show steps
This activity will help deepen your understanding of specific statistical techniques that will be covered in this course.
Browse courses on
Multiple Regression
Show steps
Show steps
Show steps
- Choose a statistical technique that you want to learn more about.
- Find a tutorial on that technique.
- Follow the tutorial and complete the exercises.
Create a data visualization
Show steps
This activity will help you apply the concepts of data visualization to real-world data.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Choose a dataset that you are interested in.
- Use a data visualization tool to create a visualization of the data.
- Write a brief report that describes your visualization and what it reveals about the data.
Start a data analysis project
Show steps
This activity will allow you to apply your skills to a real-world problem and demonstrate your understanding of the material covered in this course.
Show steps
Show steps
Show steps
- Choose a data analysis project that you are interested in.
- Gather the data you need for your project.
- Clean and prepare the data.
- Analyze the data.
- Write a report that describes your project and your findings.
Participate in a data science competition
Show steps
This activity will allow you to put your skills to the test and compete with other data scientists.
Browse courses on
Data Science
Show steps
Show steps
Show steps
- Find a data science competition that you are interested in.
- Develop a model to solve the problem.
- Submit your model to the competition.
Career center
Learners who complete Analysis and Interpretation of Data will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use data to help businesses make informed decisions. They collect, clean, and analyze data, and then they use their findings to create reports and presentations that help businesses understand their customers, improve their products and services, and make better decisions. This course can help you develop the skills you need to be a successful Data Analyst, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
Market Researcher
Market Researcher
Market Researchers conduct research to understand consumer needs and preferences. They use this information to help businesses develop new products and services, and to improve their marketing campaigns. This course can help you develop the skills you need to be a successful Market Researcher, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
Business Analyst
Business Analyst
Business Analysts use data to help businesses improve their operations. They identify problems and opportunities, and then they develop solutions that can help businesses achieve their goals. This course can help you develop the skills you need to be a successful Business Analyst, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
Data Scientist
Data Scientist
Data Scientists use data to solve complex problems. They use a variety of techniques, including machine learning and artificial intelligence, to extract insights from data. This course can help you develop the skills you need to be a successful Data Scientist, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
Statistician
Statistician
Statisticians use data to make inferences about populations. They use a variety of techniques, including probability and regression analysis, to draw conclusions from data. This course can help you develop the skills you need to be a successful Statistician, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
Survey Researcher
Survey Researcher
Survey Researchers design and conduct surveys to collect data about populations. They use this data to help businesses and organizations understand their customers, improve their products and services, and make better decisions. This course can help you develop the skills you need to be a successful Survey Researcher, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
Data Engineer
Data Engineer
Data Engineers design and build systems for storing and processing data. They also develop tools and techniques for cleaning and analyzing data. This course can help you develop the skills you need to be a successful Data Engineer, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop and deploy machine learning models. They use a variety of techniques, including supervised learning, unsupervised learning, and reinforcement learning, to build models that can learn from data and make predictions. This course can help you develop the skills you need to be a successful Machine Learning Engineer, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
Data Architect
Data Architect
Data Architects design and build data architectures for organizations. They work with data engineers and other IT professionals to ensure that data is stored and processed in a way that meets the needs of the organization. This course can help you develop the skills you need to be a successful Data Architect, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases. They ensure that data is stored and processed in a way that meets the needs of the organization. This course can help you develop the skills you need to be a successful Database Administrator, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
Software Engineer
Software Engineer
Software Engineers design and develop software applications. They work with data engineers and other IT professionals to ensure that software applications are able to access and process data in a way that meets the needs of the organization. This course can help you develop the skills you need to be a successful Software Engineer, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
Web Developer
Web Developer
Web Developers design and develop websites. They work with data engineers and other IT professionals to ensure that websites are able to access and process data in a way that meets the needs of the organization. This course can help you develop the skills you need to be a successful Web Developer, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
Computer Programmer
Computer Programmer
Computer Programmers write and maintain computer programs. They work with data engineers and other IT professionals to ensure that computer programs are able to access and process data in a way that meets the needs of the organization. This course can help you develop the skills you need to be a successful Computer Programmer, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
IT Specialist
IT Specialist
IT Specialists provide technical support to users of computer systems. They work with data engineers and other IT professionals to ensure that computer systems are able to access and process data in a way that meets the needs of the organization. This course can help you develop the skills you need to be a successful IT Specialist, including data preparation and description, quantitative and qualitative data analysis, and statistics for investigating relationships.
Help Desk Technician
Help Desk Technician
Help Desk Technicians provide technical support to users of computer systems. They work with data engineers and other IT professionals to ensure that computer systems are able to access and process data in a way that meets the needs of the organization. This course may help you develop some of the skills you need to be a successful Help Desk Technician, including data preparation and description.
For more career information including salaries, visit:
OpenCourser.com/course/jccb8w/analysis
Reading list
We've selected ten 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
Analysis and Interpretation of Data.
Save
Comprehensive guide to data mining, covering topics such as data preprocessing, clustering, classification, and association rule mining. It great resource for students who want to learn more about the field.
Comprehensive guide to statistical methods for data analysis. It covers a wide range of topics, including descriptive statistics, inferential statistics, and regression analysis.
Save
Great resource for students who want to learn more about web analytics. It covers a wide range of topics, including website traffic analysis, conversion optimization, and social media analytics.
Save
Great resource for students who want to learn more about data visualization. It covers a wide range of topics, including data visualization principles, data visualization techniques, and data visualization tools.
Great resource for students who want to learn more about ggplot2, a popular data visualization library for R. It covers a wide range of topics, including ggplot2 basics, ggplot2 advanced features, and ggplot2 case studies.
Save
Great resource for students who want to learn more about R, a popular programming language for data science. It covers a wide range of topics, including R basics, R data structures, and R data analysis.
Great resource for students who want to learn more about Python, a popular programming language for data science. It covers a wide range of topics, including Python basics, Python data structures, and Python data analysis.
Great resource for students who are new to data analysis. It provides a clear and concise introduction to the field, covering topics such as data collection, data cleaning, and data visualization.
Great resource for students who want to learn more about machine learning with Python. It covers a wide range of topics, including machine learning concepts, machine learning algorithms, and machine learning applications.
Great resource for students who want to learn more about machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jccb8w/analysis
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Effort
20 hours (4 hours per week)
Level
Introductory
Via
Coursera
Institution
Queen Mary University of London
Instructors
Athanasia Lampraki
Evangelia Katsikea
Language
English
Transcripts
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This course belongs to a
collection
called
Market Research. It's comprised of four courses:
- Research Methodologies
- Research Instruments and Research Hypotheses
- Analysis and Interpretation of Data (viewing)
- Introduction to Market Research
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