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Fundamentals of Data Analysis

Erik Herman

This course is the first of a series that aims to prepare you for a role working in data analytics. In this course, you’ll be introduced to many of the primary types of data analytics and core concepts. You’ll learn about the tools and skills required to conduct data analysis. We’ll go through some of the foundational math and statistics used in data analysis and workflows for conducting efficient and effective data analytics. This course covers a wide variety of topics that are critical for working in data analytics and are designed to give you an introduction and overview as you begin to build relevant knowledge and skills.

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What's inside

Syllabus

Types of Data Analysis
In the first module of the course, we'll learn about the primary types of data analysis including, descriptive, predictive, diagnostic, and exploratory. We will also learn about some advanced data analytic types including mechanistic, causal, and inferential. By the end of this module, you will know how to identify the different types of data analysis and their use cases. So let's get started!
Read more
The Phases of Data Analysis
In the second module of this course, we'll learn about the phases of the data analysis process including identifying data, defining scope, and level of detail. We'll learn about the data collection process, from gathering targeted information to evaluating outcomes. We'll discover the importance of data cleaning and how removing, modifying, and formatting data is a priority, as well as the benefits of visualizing data.
Data Analytics Tools and Skills
In the third module of this course, we'll learn about the tools and skills essential for data analysis. We'll learn about using spreadsheets and databases for analyzing and managing the data. We'll discover the power of query languages and multidimensional expressions. We’ll also describe the fundamental programming languages used in data analytics.
Foundational Data Analytics Math and Stats
In the fourth module of this course, we'll learn about the fundamental math and stats used for data analysis. We’ll also describe some advanced data analytic algorithms and their use cases, including linear regression and clustering.
Data Analytics Methodologies and Workflows
In the fifth week of this course, we'll learn about defining data analytics methodologies and workflows.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Examines various types of data analysis, a core understanding in the industry
Covers the data analysis process from start to finish to develop essential skills
Highlights the essential tools and skills for effective data analysis
Reinforces foundational math and statistics concepts in a data analysis context
Develops methodologies and workflows to structure the data analysis process
May require prior knowledge and experience in data analysis

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Reviews summary

Introductory data analysis course

Learners say that Fundamentals of Data Analysis is an engaging introductory data analysis course geared towards beginners. While the course content is generally comprehensive, there could be more depth. Some students also experienced difficulty with the course quizzes, which often require exact spelling and have obscure answers. Despite these issues, many students found the teaching format to be easy to understand.
Course is beginner-friendly.
"The fact that he repeatedly mentions he does not have a math or computer science background is extremely reassuring for those of us who are changing careers and coming from a non-data-specific background !"
"The unfortunate and frustrating parts were the non-working links and the fill in the blank questions that did not allow for variation other than a very specifically worded response that was hard to determine. For example ~ "Factor Analysis" was correct for one but only "Factor" was acceptable for a similar question with the same answer. "Six" was not accepted but "6" was, etc. The content itself is great, tho!"
"I debated between giving this course two stars or three stars and decided to give it the benefit of the doubt and give it three, since there is some useful information contained with it, despite its many flaws."
Course content is generally positive.
"VERRY GOOD"
"B​asic Theory only"
"The content and teaching format are excellent. The information is easy to understand and the fact that he repeatedly mentions he does not have a math or computer science background is extremely reassuringg for those of us who are changing careers and coming from a non-data-specific background ! "
"Some quizes contain very obscure answers, other than that, its a good course on data analysis for beginners."
Course quizzes can be difficult.
"The unfortunate and frustrating parts were the non-working links and the fill in the blank questions that did not allow for variation other than a very specifically worded response that was hard to determine."
"For example ~ "Factor Analysis" was correct for one but only "Factor" was acceptable for a similar question with the same answer. "Six" was not accepted but "6" was, etc."
"The content itself is great, tho!"

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 Fundamentals of Data Analysis with these activities:
Refine SQL Knowledge
Review foundational SQL concepts to solidify your understanding of data retrieval and manipulation.
Browse courses on SQL
Show steps
  • Review SQL syntax and commands
  • Practice writing queries using a database
Follow tutorials on data cleaning
Following tutorials on data cleaning will help you understand the importance of data cleaning and how to do it effectively.
Browse courses on Data Cleaning
Show steps
  • Find tutorials on data cleaning.
  • Follow the tutorials and practice data cleaning techniques.
Review the linear regression model
Reviewing the linear regression model will help you understand the foundational concepts of data analysis.
Browse courses on Linear Regression
Show steps
  • Read the textbook chapters on linear regression.
  • Go through the online tutorials on linear regression.
  • Solve practice problems on linear regression.
Seven other activities
Expand to see all activities and additional details
Show all ten activities
Practice data analysis problems
Practice solving data analysis problems to improve your understanding of the concepts and techniques covered in the course.
Show steps
  • Find a set of data analysis problems to practice.
  • Work through the problems, step-by-step.
  • Check your solutions against the provided answer key.
  • Review your mistakes and identify areas where you need more practice.
Explore Advanced Visualization Techniques
Expand your data visualization skills by exploring advanced techniques and tools, enhancing your ability to present data in a compelling and insightful manner.
Browse courses on Data Visualization
Show steps
  • Identify advanced visualization techniques
  • Follow tutorials on using these techniques
Practice SQL queries
Practicing SQL queries will help you become proficient in extracting data from databases.
Browse courses on SQL
Show steps
  • Find a dataset to practice on.
  • Write SQL queries to extract data from the dataset.
  • Review your results and identify any errors.
Mini Data Analytics Project
Enhance your understanding by applying data analytics techniques to a small-scale project, testing your skills in data gathering, analysis, and interpretation.
Browse courses on Data Analysis
Show steps
  • Identify a relevant dataset or problem
  • Clean and analyze the data
  • Develop insights and draw conclusions
Collaborative Data Analysis
Foster teamwork and enhance your data analysis skills by collaborating with peers on a data analysis project.
Browse courses on Data Analysis
Show steps
  • Form a study group or connect with classmates
  • Assign roles and responsibilities
  • Work together to analyze a dataset and synthesize findings
Create a data visualization dashboard
Creating a data visualization dashboard will help you apply the data analysis techniques you learn in the course.
Browse courses on Data Visualization
Show steps
  • Gather the data you want to visualize.
  • Choose the appropriate data visualization tools.
  • Design and create the data visualization dashboard.
Data Analysis Blog Post
Solidify your understanding and share your insights by writing a blog post that explores a data analysis topic or technique.
Browse courses on Data Analysis
Show steps
  • Choose a specific data analysis topic
  • Research and gather data
  • Analyze the data and formulate insights
  • Write and publish your blog post

Career center

Learners who complete Fundamentals of Data Analysis will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts use their expertise in machine learning, statistics, programming, and data visualization to extract insights from large volumes of structured and unstructured data. This course provides a solid foundation for aspiring Data Analysts by introducing them to the core concepts, tools, and techniques used in data analysis. The course covers various types of data analysis, including descriptive, predictive, diagnostic, and exploratory analysis. It also introduces learners to essential data analytics tools and skills, including spreadsheets, databases, query languages, and programming languages. By completing this course, learners will gain a comprehensive understanding of the data analysis process and develop the skills necessary to excel as Data Analysts.
See salaries and explore the career path for Data Analyst
Business Analyst
Business Analysts use their understanding of business processes and data analysis techniques to help organizations improve their performance. This course is a valuable resource for aspiring Business Analysts as it provides a solid foundation in data analysis, which is essential for identifying and solving business problems. The course covers various types of data analysis, including descriptive, predictive, and diagnostic analysis, as well as tools and techniques for data collection, cleaning, and visualization. By completing this course, learners will gain the skills and knowledge necessary to succeed as Business Analysts.
See salaries and explore the career path for Business Analyst
Data Scientist
Data Scientists use their expertise in machine learning, statistics, and data analysis to extract insights from large volumes of data and develop predictive models. This course is a valuable resource for aspiring Data Scientists as it provides a solid foundation in data analysis, which is a core skill for this role. The course covers various types of data analysis, including descriptive, predictive, diagnostic, and exploratory analysis, as well as introduces learners to fundamental data analytics math and statistics. By completing this course, learners will gain the skills and knowledge necessary to succeed as Data Scientists.
See salaries and explore the career path for Data Scientist
Market Researcher
Market Researchers use their understanding of data analysis techniques to gather and interpret data about consumer behavior and market trends. This course is a valuable resource for aspiring Market Researchers as it provides a solid foundation in data analysis, which is essential for conducting effective market research. The course covers various types of data analysis, including descriptive, predictive, and diagnostic analysis, as well as tools and techniques for data collection, cleaning, and visualization. By completing this course, learners will gain the skills and knowledge necessary to succeed as Market Researchers.
See salaries and explore the career path for Market Researcher
Operations Research Analyst
Operations Research Analysts use their expertise in data analysis and optimization techniques to improve the efficiency and effectiveness of business processes. This course is a valuable resource for aspiring Operations Research Analysts as it provides a solid foundation in data analysis, which is essential for this role. The course covers various types of data analysis, including descriptive, predictive, and diagnostic analysis, as well as introduces learners to fundamental data analytics math and statistics. By completing this course, learners will gain the skills and knowledge necessary to succeed as Operations Research Analysts.
See salaries and explore the career path for Operations Research Analyst
Financial Analyst
Financial Analysts use their expertise in data analysis and financial modeling to make investment recommendations and evaluate the performance of financial instruments. This course is a valuable resource for aspiring Financial Analysts as it provides a solid foundation in data analysis, which is essential for this role. The course covers various types of data analysis, including descriptive, predictive, and diagnostic analysis, as well as introduces learners to fundamental data analytics math and statistics. By completing this course, learners will gain the skills and knowledge necessary to succeed as Financial Analysts.
See salaries and explore the career path for Financial Analyst
Actuary
Actuaries use their expertise in data analysis and statistics to assess and manage financial risks. This course is a valuable resource for aspiring Actuaries as it provides a solid foundation in data analysis, which is essential for this role. The course covers various types of data analysis, including descriptive, predictive, and diagnostic analysis, as well as introduces learners to fundamental data analytics math and statistics. By completing this course, learners will gain the skills and knowledge necessary to succeed as Actuaries.
See salaries and explore the career path for Actuary
Statistician
Statisticians use their expertise in data analysis and probability theory to collect, analyze, and interpret data. This course is a valuable resource for aspiring Statisticians as it provides a solid foundation in data analysis, which is essential for this role. The course covers various types of data analysis, including descriptive, predictive, and diagnostic analysis, as well as introduces learners to fundamental data analytics math and statistics. By completing this course, learners will gain the skills and knowledge necessary to succeed as Statisticians.
See salaries and explore the career path for Statistician
Data Engineer
Data Engineers use their expertise in data management and data analysis to design and build data pipelines and data warehouses. This course is a valuable resource for aspiring Data Engineers as it provides a solid foundation in data analysis, which is essential for this role. The course covers various types of data analysis, including descriptive, predictive, and diagnostic analysis, as well as introduces learners to fundamental data analytics math and statistics. By completing this course, learners will gain the skills and knowledge necessary to succeed as Data Engineers.
See salaries and explore the career path for Data Engineer
Quantitative Analyst
Quantitative Analysts use their expertise in data analysis and financial modeling to develop trading strategies and manage investment portfolios. This course is a valuable resource for aspiring Quantitative Analysts as it provides a solid foundation in data analysis, which is essential for this role. The course covers various types of data analysis, including descriptive, predictive, and diagnostic analysis, as well as introduces learners to fundamental data analytics math and statistics. By completing this course, learners will gain the skills and knowledge necessary to succeed as Quantitative Analysts.
See salaries and explore the career path for Quantitative Analyst
Machine Learning Engineer
Machine Learning Engineers use their expertise in data analysis and machine learning to develop and deploy machine learning models. This course is a valuable resource for aspiring Machine Learning Engineers as it provides a solid foundation in data analysis, which is essential for this role. The course covers various types of data analysis, including descriptive, predictive, and diagnostic analysis, as well as introduces learners to fundamental data analytics math and statistics. By completing this course, learners will gain the skills and knowledge necessary to succeed as Machine Learning Engineers.
See salaries and explore the career path for Machine Learning Engineer
Data Architect
Data Architects use their expertise in data management and data analysis to design and build data architectures. This course is a valuable resource for aspiring Data Architects as it provides a solid foundation in data analysis, which is essential for this role. The course covers various types of data analysis, including descriptive, predictive, and diagnostic analysis, as well as introduces learners to fundamental data analytics math and statistics. By completing this course, learners will gain the skills and knowledge necessary to succeed as Data Architects.
See salaries and explore the career path for Data Architect
Software Engineer
Software Engineers use their expertise in computer science and software development to design, build, and maintain software systems. This course may be useful for aspiring Software Engineers as it provides a solid foundation in data analysis, which is increasingly used in software development. The course covers various types of data analysis, including descriptive, predictive, and diagnostic analysis, as well as introduces learners to fundamental data analytics math and statistics. By completing this course, learners will gain the skills and knowledge necessary to succeed as Software Engineers.
See salaries and explore the career path for Software Engineer
Product Manager
Product Managers use their expertise in product development and data analysis to define, build, and launch new products. This course may be useful for aspiring Product Managers as it provides a solid foundation in data analysis, which is increasingly used in product development. The course covers various types of data analysis, including descriptive, predictive, and diagnostic analysis, as well as introduces learners to fundamental data analytics math and statistics. By completing this course, learners will gain the skills and knowledge necessary to succeed as Product Managers.
See salaries and explore the career path for Product Manager
Business Intelligence Analyst
Business Intelligence Analysts use their expertise in data analysis and business intelligence tools to provide insights and recommendations to business leaders. This course may be useful for aspiring Business Intelligence Analysts as it provides a solid foundation in data analysis, which is essential for this role. The course covers various types of data analysis, including descriptive, predictive, and diagnostic analysis, as well as introduces learners to fundamental data analytics math and statistics. By completing this course, learners will gain the skills and knowledge necessary to succeed as Business Intelligence Analysts.
See salaries and explore the career path for Business Intelligence Analyst

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 Fundamentals of Data Analysis.
Cover image
Big Data: Second Edition
Save
Provides a comprehensive overview of data analytics, covering the key concepts, tools, and techniques used in the field. It valuable resource for beginners who want to learn more about data analytics, as well as for experienced professionals who want to refresh their knowledge.
Big Data: Second Edition
Paperback
Check
Big Data: Second Edition
Kindle Edition
Check
Cover image
Data Science for Business
Save
Provides a practical guide to data science for business professionals. It covers the key concepts, tools, and techniques used to analyze data and make informed decisions. It valuable resource for business professionals who want to learn more about data science.
Data Science for Business: What You Need to Know...
Paperback
$$
Data Science for Business: What You Need to Know...
Kindle Edition
$$
Data Mining
Save
Provides a comprehensive overview of data mining. It covers the key concepts, tools, and techniques used to extract knowledge from data. It valuable resource for data miners who want to learn more about the field.
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
Active Learning
Save
Provides a comprehensive overview of machine learning for data analytics. It covers the key concepts, tools, and techniques used to build and deploy machine learning models. It valuable resource for data analysts who want to learn more about machine learning.
Active Learning (Synthesis Lectures on Artificial...
Paperback
$$
Cover image
Deep Learning
Save
Provides a comprehensive overview of deep learning for data analytics. It covers the key concepts, tools, and techniques used to build and deploy deep learning models. It valuable resource for data analysts who want to learn more about deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Data Visualization
Save
Provides a practical introduction to data visualization. It covers the key concepts, tools, and techniques used to create effective data visualizations. It valuable resource for data analysts who want to learn more about data visualization.
Data Visualization: A Successful Design Process: A...
Paperback
$
Data Visualization: Representing Information on...
Kindle Edition
$$$
Data Visualization Design and Utilization (Korean...
Paperback
$$$
Linear Regression Analysis
Save
Provides a comprehensive overview of statistical methods for data analysis. It covers the key concepts, tools, and techniques used to analyze data. It valuable resource for data analysts who want to learn more about statistics.
Linear Regression Analysis
Paperback
Check
Linear Regression Analysis
Kindle Edition
Check
Cover image
The R Book
Save
Provides a comprehensive guide to data analysis using R. It covers the key concepts, tools, and techniques used to analyze data with R. It valuable resource for data analysts who want to learn more about R.
The R Book
Hardcover
$$$
The R Book
Kindle Edition
$$$
Cover image
R for Data Science
Save
Provides a comprehensive guide to using R for data science. It covers the key concepts, tools, and techniques used to analyze data with R. It valuable resource for data scientists who want to learn more about R.
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
Provides a comprehensive guide to using Python for data analysis. It covers the key concepts, tools, and techniques used to analyze data with Python. It valuable resource for data analysts who want to learn more about Python.
Python for Data Analysis
Paperback
Check
Python for Data Analysis
Kindle Edition
Check

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Course image
Effort
This course requires 4 weeks at about 3-5 hours/week to complete.
Level
Introductory
Via
Coursera
Institution
LearnQuest
Instructor
Erik Herman
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 Key Technologies in Data Analytics. It's comprised of four courses:
  1. Fundamentals of Cloud Computing
  2. Fundamentals of Data Analysis (viewing)
  3. Fundamentals of Big Data
  4. Fundamentals of Data Warehousing
Good to know
Examines various types of data analysis, a core understanding in the industry
Covers the data analysis process from start to finish to develop essential skills
Highlights the essential tools and skills for effective data analysis
Reinforces foundational math and statistics concepts in a data analysis context
Develops methodologies and workflows to structure the data analysis process
May require prior knowledge and experience in data analysis
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