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Analysis and Interpretation of Data
Athanasia Lampraki
and
Evangelia Katsikea
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/jccb8w/analysis
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
Week 2
The week commences with a discussion of statistics for investigating relationships with a focus on multiple regression. The week continues with a focus on logistic regression and exploratory factor analysis. The week ends with a discussion of the outcome of factor analysis.
Week 3
The week commences with a discussion of how to conduct an experiment and an observational study. The week ends with an exploration of content analysis and the use of digital analytics in market research.
Week 4
This week explores 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.
Good to know
Good to know
Know what's good ,
what to watch for , 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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Activities
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Analysis and Interpretation of Data.
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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
Good to know
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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