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Analyze Data

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.

The typical student in this course will have several years of experience with computing technology, including some aptitude in computer programming.

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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
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

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

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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 Analyze Data with these activities:
Follow tutorials on data visualization using Tableau
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
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
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
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
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
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
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
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.

Career center

Learners who complete Analyze Data will develop knowledge and skills that may be useful to these careers:
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.
See salaries and explore the career path for Data 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.
See salaries and explore the career path for Business Analyst
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.
See salaries and explore the career path for Data Scientist
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.
See salaries and explore the career path for Data Engineer
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.
See salaries and explore the career path for Market Researcher
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.
See salaries and explore the career path for Financial Analyst
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.
See salaries and explore the career path for Actuary
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.
See salaries and explore the career path for Operations Research Analyst
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.
See salaries and explore the career path for Epidemiologist
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.
See salaries and explore the career path for Statistician
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.
See salaries and explore the career path for Quantitative Analyst
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.
See salaries and explore the career path for Data Architect
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.
See salaries and explore the career path for Risk Manager
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.
See salaries and explore the career path for Software Engineer
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.
See salaries and explore the career path for Product Manager

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.
Cover image
An Introduction to Statistical Learning
Save
A comprehensive textbook on statistical learning methods, including regression, classification, and clustering, with a focus on practical applications.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Data Mining
Save
A comprehensive reference on data mining techniques, including clustering, classification, and association rule mining, provides a deeper understanding of data analysis algorithms.
Data Mining: Concepts and Techniques (The Morgan...
Kindle Edition
$$$
Data Mining: Concepts and Techniques (The Morgan...
Hardcover
$$$
Data Mining: Concepts and Techniques, Second...
Hardcover
$$
Data Mining: Know It All
Hardcover
$$$
Cover image
Statistical Rethinking
Save
An introduction to Bayesian statistics and its applications in data analysis, provides a different perspective on statistical modeling.
Statistical Rethinking
Paperback
Check
Statistical Rethinking
Kindle Edition
Check
Cover image
The Economics of Data, Analytics, and Digital...
Save
A practical guide to data visualization techniques, including best practices and common pitfalls, useful for conveying insights from data effectively.
The Economics of Data, Analytics, and Digital...
Kindle Edition
$$
Machine Learning Con Pytorch Y Scikit Lean
Save
A comprehensive guide to machine learning using Python, including supervised and unsupervised learning algorithms.
(Español) Machine Learning con PyTorch y Scikit-Learn:...
Paperback
$$$
Cover image
R for Data Science
Save
A practical guide to data analysis using the R programming language, particularly valuable for those working with tidy data.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Cover image
Python for Data Analysis
Save
A practical guide to data analysis using the Pandas library in Python, particularly useful for those working with data in a structured format.
Python for Data Analysis: Data Wrangling with...
Paperback
$$
Python for Data Analysis
Kindle Edition
$$
Python for Data Analysis: Data Wrangling with...
Paperback
$$$
Cover image
Automate the Boring Stuff with Python, 3rd Edition
Save
A gentle introduction to Python programming, particularly useful for automating data processing tasks and preprocessing data for analysis.
Automate the Boring Stuff with Python, 3rd Edition
Kindle Edition
$$

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Course image
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:
  1. Extract, Transform, and Load Data
  2. Train Machine Learning Models
  3. Address Business Issues with Data Science
  4. Analyze Data (viewing)
  5. Finalize a Data Science Project
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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