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Sarah Haq,
Stacey McBrine,
and
Megan Smith Branch
This course is designed for business professionals that want to learn how to analyze data to gain insight, use statistical analysis methods to explore the underlying distribution of data, use visualizations such as histograms, scatter plots, and maps to analyze data and preprocess data to produce a dataset ready for training.
Read more
This course is designed for business professionals that want to learn how to analyze data to gain insight, use statistical analysis methods to explore the underlying distribution of data, use visualizations such as histograms, scatter plots, and maps to analyze data and preprocess data to produce a dataset ready for training.
Read more
This course is designed for business professionals that want to learn how to analyze data to gain insight, use statistical analysis methods to explore the underlying distribution of data, use visualizations such as histograms, scatter plots, and maps to analyze data and preprocess data to produce a dataset ready for training.
The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming.
Register for this course and see more details by visiting:
OpenCourser.com/course/se1szd/analyze
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What's inside
Syllabus
Examine Data
In the previous course in this specialization, you conducted extract, transform, and load (ETL) to ensure your data was ready for the next phase of the data science process: analysis. In some cases, an analysis of the data may be the actual final goal of the project, or it may be an important intermediary step on the road to machine learning. In either case, analyzing your data using various techniques will help you obtain useful insights into that data and what it represents. It'll also give you a better understanding of how the data needs to undergo more processing to prepare it for machine learning. You'll begin your analysis efforts by exploring the nature of your dataset and the relationships it contains.
Read more
Syllabus
Examine Data
In the previous course in this specialization, you conducted extract, transform, and load (ETL) to ensure your data was ready for the next phase of the data science process: analysis. In some cases, an analysis of the data may be the actual final goal of the project, or it may be an important intermediary step on the road to machine learning. In either case, analyzing your data using various techniques will help you obtain useful insights into that data and what it represents. It'll also give you a better understanding of how the data needs to undergo more processing to prepare it for machine learning. You'll begin your analysis efforts by exploring the nature of your dataset and the relationships it contains.
Read more
Explore the Underlying Distribution of Data
One of the key factors in data analysis is determining how values are spread out within each of the different features. This will give you a deeper understanding of how the data is represented and how it might need to change.
Use Visualizations to Analyze Data
In this module, you'll look at your data from a visual perspective in order to reveal insights that raw numbers alone may not provide.
Preprocess Data
Your analysis efforts will most likely prompt you to transform your data further, especially in preparation for machine learning. In this topic, you'll do just that.
Apply What You've Learned
You'll work on a project in which you'll apply your knowledge of the material in this course to a practical scenario.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
This course provides business professionals with an end-to-end workflow for the data analysis process, bridging the gap between data collection and machine learning
Taught by industry experts with years of experience in data science
Covers fundamental concepts in data analysis, including data exploration, statistical analysis, data visualization, and data preprocessing
Applies practical, real-world examples to help learners understand the concepts
Assumes learners have a good understanding of computing technology and experience with computer programming
Students who are new to data analysis may find some concepts challenging without prior knowledge in statistics
Save this course
Save Analyze Data to your list so you can find it easily later:
Save
Save
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 Analyze Data with these
activities:
Follow tutorials on data visualization using Tableau
Show steps
Provides practical experience in using Tableau for data visualization, enabling the creation of compelling and informative data visualizations.
Browse courses on
Data Visualization
Show steps
-
Create a Tableau account.
-
Watch introductory tutorials on Tableau's website.
-
Follow step-by-step tutorials to create basic visualizations.
-
Explore Tableau's online help documentation.
Solve data analysis problems using Python
Show steps
Develops proficiency in using Python for data analysis, allowing for efficient and accurate data manipulation, exploration, and visualization.
Browse courses on
Data Analysis
Show steps
-
Install Python and necessary libraries.
-
Import data from different sources into Python.
-
Clean and preprocess data using Python libraries.
-
Perform exploratory data analysis using Python functions.
-
Visualize data using Python libraries.
Read Data Analysis Using Regression and Multilevel/Hierarchical Models
Show steps
Provides a strong theoretical foundation for understanding the concepts of data analysis, regression analysis, multilevel modeling, and hierarchical modeling.
View
Regression and Other Stories (Analytical...
on Amazon
Show steps
-
Read the first five chapters of the book.
-
Complete the practice exercises at the end of each chapter.
-
Summarize the key concepts covered in each chapter.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group for data analysis
Show steps
Fosters collaboration, knowledge sharing, and peer support, enhancing the learning experience and reinforcing concepts through discussions and problem-solving.
Browse courses on
Data Analysis
Show steps
-
Find a study group or create one with classmates.
-
Set regular meeting times and stick to them.
-
Take turns leading discussions and presenting findings.
Attend a workshop on advanced data analysis techniques
Show steps
Provides exposure to cutting-edge data analysis techniques and industry best practices, allowing for the acquisition of new skills and knowledge in a structured environment.
Browse courses on
Data Analysis
Show steps
-
Identify and register for workshops relevant to your interests.
-
Attend the workshop and actively participate in discussions.
-
Apply the learned techniques in your own data analysis projects.
Develop a data analysis project portfolio
Show steps
Demonstrates the practical application of data analysis skills and provides tangible evidence of proficiency in data analysis.
Browse courses on
Data Analysis
Show steps
-
Identify a real-world problem that can be addressed using data analysis.
-
Collect and preprocess data relevant to the problem.
-
Analyze the data using appropriate statistical methods.
-
Develop visualizations to communicate the results effectively.
-
Document the project and showcase it in a portfolio.
Volunteer for a non-profit organization using data analysis skills
Show steps
Applies data analysis skills to address real-world problems and contribute to social impact while gaining practical experience in a non-profit setting.
Browse courses on
Data Analysis
Show steps
-
Identify non-profit organizations that leverage data analysis.
-
Inquire about volunteering opportunities and offer your skills.
-
Collaborate with the organization to identify data-related needs.
Contribute to open-source data analysis projects
Show steps
Provides hands-on experience in real-world data analysis projects, fostering collaboration and contributing to the broader data analysis community.
Browse courses on
Open Source
Show steps
-
Find open-source data analysis projects on platforms like GitHub.
-
Identify issues or areas where you can contribute.
-
Propose solutions or improvements through pull requests.
Follow tutorials on data visualization using Tableau
Show steps
Provides practical experience in using Tableau for data visualization, enabling the creation of compelling and informative data visualizations.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Create a Tableau account.
- Watch introductory tutorials on Tableau's website.
- Follow step-by-step tutorials to create basic visualizations.
- Explore Tableau's online help documentation.
Solve data analysis problems using Python
Show steps
Develops proficiency in using Python for data analysis, allowing for efficient and accurate data manipulation, exploration, and visualization.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Install Python and necessary libraries.
- Import data from different sources into Python.
- Clean and preprocess data using Python libraries.
- Perform exploratory data analysis using Python functions.
- Visualize data using Python libraries.
Read Data Analysis Using Regression and Multilevel/Hierarchical Models
Show steps
Provides a strong theoretical foundation for understanding the concepts of data analysis, regression analysis, multilevel modeling, and hierarchical modeling.
View
Regression and Other Stories (Analytical...
on Amazon
Show steps
Show steps
Show steps
- Read the first five chapters of the book.
- Complete the practice exercises at the end of each chapter.
- Summarize the key concepts covered in each chapter.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Join a study group for data analysis
Show steps
Fosters collaboration, knowledge sharing, and peer support, enhancing the learning experience and reinforcing concepts through discussions and problem-solving.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Find a study group or create one with classmates.
- Set regular meeting times and stick to them.
- Take turns leading discussions and presenting findings.
Attend a workshop on advanced data analysis techniques
Show steps
Provides exposure to cutting-edge data analysis techniques and industry best practices, allowing for the acquisition of new skills and knowledge in a structured environment.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify and register for workshops relevant to your interests.
- Attend the workshop and actively participate in discussions.
- Apply the learned techniques in your own data analysis projects.
Develop a data analysis project portfolio
Show steps
Demonstrates the practical application of data analysis skills and provides tangible evidence of proficiency in data analysis.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using data analysis.
- Collect and preprocess data relevant to the problem.
- Analyze the data using appropriate statistical methods.
- Develop visualizations to communicate the results effectively.
- Document the project and showcase it in a portfolio.
Volunteer for a non-profit organization using data analysis skills
Show steps
Applies data analysis skills to address real-world problems and contribute to social impact while gaining practical experience in a non-profit setting.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Identify non-profit organizations that leverage data analysis.
- Inquire about volunteering opportunities and offer your skills.
- Collaborate with the organization to identify data-related needs.
Contribute to open-source data analysis projects
Show steps
Provides hands-on experience in real-world data analysis projects, fostering collaboration and contributing to the broader data analysis community.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Find open-source data analysis projects on platforms like GitHub.
- Identify issues or areas where you can contribute.
- Propose solutions or improvements through pull requests.
Career center
Learners who complete Analyze Data will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts play a critical role in analyzing data to gain insights, which is a central theme of the 'Analyze Data' course. By understanding how to explore the underlying distribution of data, use visualizations to analyze data, and preprocess data, individuals can build a foundation for a successful career as a Data Analyst. This course provides a solid understanding of the techniques and methods used in data analysis, making it highly relevant for those seeking to enter or advance in this field.
Business Analyst
Business Analyst
Business Analysts use data analysis to drive business decisions. The 'Analyze Data' course provides a foundation for success in this role by teaching participants how to examine data, identify patterns, and communicate insights. The course's focus on statistical analysis methods and data visualization aligns with the skills required of Business Analysts to effectively analyze and interpret data, enabling them to make data-informed recommendations that support business growth and optimization.
Data Scientist
Data Scientist
Data Scientists leverage data analysis skills to extract insights and make predictions. The 'Analyze Data' course aligns perfectly with the core responsibilities of a Data Scientist. It provides a comprehensive overview of data analysis techniques, including statistical analysis methods, data visualization, and data preprocessing. By mastering these skills, individuals can enhance their ability to build predictive models, design data-driven solutions, and contribute to informed decision-making as Data Scientists.
Data Engineer
Data Engineer
Data Engineers design and build data pipelines and systems that support data analysis. The 'Analyze Data' course complements the technical skills of Data Engineers by providing a deep dive into data analysis techniques. Understanding how to analyze data, explore its distribution, and preprocess it is essential for Data Engineers to ensure the quality and integrity of data used for analysis and modeling.
Market Researcher
Market Researcher
Market Researchers analyze data to understand consumer behavior and market trends. The 'Analyze Data' course provides a strong foundation for success in this role. It equips individuals with the skills to collect, analyze, and interpret data, enabling them to gain insights into customer needs, preferences, and buying patterns. By understanding how to effectively analyze data, Market Researchers can make informed decisions and develop effective marketing strategies.
Financial Analyst
Financial Analyst
Financial Analysts use data analysis to evaluate investments and make financial recommendations. The 'Analyze Data' course is highly relevant to this role, as it provides a solid grounding in statistical analysis methods and data visualization. By understanding how to analyze financial data, identify trends, and interpret results, individuals can develop strong analytical skills essential for success as Financial Analysts.
Actuary
Actuary
Actuaries use data analysis to assess risk and uncertainty. The 'Analyze Data' course provides a foundation for success in this role by introducing participants to statistical analysis methods and data visualization. Understanding how to analyze data, identify patterns, and communicate insights is essential for Actuaries to accurately assess risk and develop appropriate strategies for managing it.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use data analysis to improve efficiency and productivity. The 'Analyze Data' course is highly relevant to this role, as it provides a solid understanding of data analysis techniques, including statistical modeling and optimization. By mastering these skills, individuals can enhance their ability to analyze complex systems, identify bottlenecks, and develop data-driven solutions that improve operational performance.
Epidemiologist
Epidemiologist
Epidemiologists use data analysis to investigate the causes and spread of diseases. The 'Analyze Data' course provides a strong foundation for success in this role by introducing participants to statistical analysis methods and data visualization. Understanding how to analyze data, identify patterns, and communicate insights is essential for Epidemiologists to effectively track and analyze disease outbreaks, identify risk factors, and develop prevention strategies.
Statistician
Statistician
Statisticians use data analysis to conduct research and solve problems. The 'Analyze Data' course aligns with the core responsibilities of a Statistician by providing a comprehensive overview of data analysis techniques. By mastering these skills, individuals can enhance their ability to design and conduct studies, analyze data, and draw valid conclusions. This course is particularly valuable for those seeking to enter or advance in the field of Statistics.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. The 'Analyze Data' course complements the technical skills of Quantitative Analysts by providing a comprehensive overview of data analysis techniques. By mastering these skills, individuals can enhance their ability to build and validate financial models, analyze risk, and make data-driven investment decisions.
Data Architect
Data Architect
Data Architects design and manage data systems. The 'Analyze Data' course complements the technical skills of Data Architects by providing a deep dive into data analysis techniques. Understanding how to analyze data, explore its distribution, and preprocess it is essential for Data Architects to ensure the quality and integrity of data stored in the systems they design.
Risk Manager
Risk Manager
Risk Managers use data analysis to identify and mitigate risks. The 'Analyze Data' course provides a solid foundation for success in this role by introducing participants to statistical analysis methods and data visualization. Understanding how to analyze data, identify patterns, and communicate insights is essential for Risk Managers to effectively assess risks, develop mitigation strategies, and ensure compliance with regulations.
Software Engineer
Software Engineer
Software Engineers build and maintain software systems. The 'Analyze Data' course may be useful for Software Engineers who want to specialize in data analysis or who work on projects that involve data-driven decision-making. The course provides a foundation in statistical analysis methods and data visualization, which can be valuable for developing software that effectively processes and analyzes data.
Product Manager
Product Manager
Product Managers oversee the development and launch of products. The 'Analyze Data' course may be useful for Product Managers who want to gain a better understanding of how to analyze data to make informed decisions. The course provides a foundation in statistical analysis methods and data visualization, which can be valuable for understanding customer needs, evaluating product performance, and making data-driven decisions.
For more career information including salaries, visit:
OpenCourser.com/course/se1szd/analyze
Reading list
We've selected eight 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
Analyze Data.
A comprehensive textbook on statistical learning methods, including regression, classification, and clustering, with a focus on practical applications.
Save
A comprehensive reference on data mining techniques, including clustering, classification, and association rule mining, provides a deeper understanding of data analysis algorithms.
An introduction to Bayesian statistics and its applications in data analysis, provides a different perspective on statistical modeling.
A practical guide to data visualization techniques, including best practices and common pitfalls, useful for conveying insights from data effectively.
A comprehensive guide to machine learning using Python, including supervised and unsupervised learning algorithms.
Save
A practical guide to data analysis using the R programming language, particularly valuable for those working with tidy data.
A practical guide to data analysis using the Pandas library in Python, particularly useful for those working with data in a structured format.
A gentle introduction to Python programming, particularly useful for automating data processing tasks and preprocessing data for analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/se1szd/analyze
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Effort
4 weeks of study, 1–3 hours per week
Level
Intermediate
Via
Coursera
Institution
CertNexus
Instructors
Sarah Haq
Stacey McBrine
Megan Smith Branch
Language
English
Transcripts
Español
Français
Português
Deutsch
Italiano
中文
العربية
한국어
日本語
हिंदी
Nederlands
Svenska
Pусский
Türkçe
Polski
Ελληνικά
українська мова
Bahasa Indonesia
ไทย
қазақша
This course belongs to a
collection
called
CertNexus Certified Data Science Practitioner. It's comprised of five courses:
Good to know
This course provides business professionals with an end-to-end workflow for the data analysis process, bridging the gap between data collection and machine learning
Taught by industry experts with years of experience in data science
Covers fundamental concepts in data analysis, including data exploration, statistical analysis, data visualization, and data preprocessing
Applies practical, real-world examples to help learners understand the concepts
Assumes learners have a good understanding of computing technology and experience with computer programming
Students who are new to data analysis may find some concepts challenging without prior knowledge in statistics
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