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Anke Audenaert
This course provides a practical understanding and framework for basic analytics tasks, including data extraction, cleaning, manipulation, and analysis. It introduces the OSEMN cycle for managing analytics projects and you'll examine real-world examples of how companies use data insights to improve decision-making.
By the end of this course you will be able to:
• Formulate business goals, KPIs and associated metrics
• Apply a data analysis process using the OSEMN framework
• Identify and define the relevant data to be collected for marketing
Read more
This course provides a practical understanding and framework for basic analytics tasks, including data extraction, cleaning, manipulation, and analysis. It introduces the OSEMN cycle for managing analytics projects and you'll examine real-world examples of how companies use data insights to improve decision-making.
By the end of this course you will be able to:
• Formulate business goals, KPIs and associated metrics
• Apply a data analysis process using the OSEMN framework
• Identify and define the relevant data to be collected for marketing
Read more
This course provides a practical understanding and framework for basic analytics tasks, including data extraction, cleaning, manipulation, and analysis. It introduces the OSEMN cycle for managing analytics projects and you'll examine real-world examples of how companies use data insights to improve decision-making.
By the end of this course you will be able to:
• Formulate business goals, KPIs and associated metrics
• Apply a data analysis process using the OSEMN framework
• Identify and define the relevant data to be collected for marketing
• Compare and contrast various data formats and their applications across different scenarios
• Identify data gaps and articulate the strengths and weaknesses of collected data
You don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Ideally you have already completed course 1: Marketing Analytics Foundation in this program.
Register for this course and see more details by visiting:
OpenCourser.com/course/vvo5zt/introduction
What's inside
Syllabus
Working with Data
This week, you will learn what data analytics are and what a data analyst does. You’ll be introduced to the OSEMN framework as well as important business metrics, KPIs and their value to a business.
Read more
Syllabus
Working with Data
This week, you will learn what data analytics are and what a data analyst does. You’ll be introduced to the OSEMN framework as well as important business metrics, KPIs and their value to a business.
Read more
Obtaining and Scrubbing Data
In the second week you will learn how to discover different sources of data and how to evaluate their validity. You will also explore different data formats. You’ll begin to apply the OSEMN framework by learning the steps in the data cleaning process as well as how to handle missing or incorrect data in your datasets.
Exploring and Modeling Data
This week moves onto the Exploring and Modeling phases of OSEMN. You will learn how to inspect and summarize your data as well as evaluate data relationships. You will discover the purpose of data modeling and common types of data models and data visualizations.
Interpreting Data
This week you will learn how to interpret the data you have working with and relate the results of your analysis back to a specific business goal. You will also learn how to create a story for a presentation of your data in order to explain and engage an audience.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Appropriate for learners who have basic internet navigation skills
Covers basic analytics tasks, including data extraction, cleaning, manipulation, and analysis
Introduces the OSEMN cycle for managing analytics projects
Examines real-world examples of how companies use data insights to improve decision-making
Provides a foundation for learners who want to pursue further studies in marketing analytics
Save this course
Save Introduction to Data Analytics to your list so you can find it easily later:
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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 Introduction to Data Analytics with these
activities:
Review Basic Statistics and Probability Concepts
Show steps
Strengthen your understanding of essential statistical and probability concepts to support your learning in data analytics.
Browse courses on
Statistics
Show steps
-
Review notes or textbooks from previous courses or online resources.
-
Practice solving basic statistical problems, such as calculating mean, median, and standard deviation.
-
Review probability distributions and their applications in data analysis.
Attend a Workshop on Data Analytics Essentials
Show steps
Enhance your understanding of data analytics principles and best practices through a structured workshop.
Show steps
-
Research and identify upcoming workshops on data analytics essentials.
-
Register for a workshop that aligns with your learning goals.
-
Attend the workshop and actively participate in discussions and exercises.
Read 'Data Analytics Made Accessible'
Show steps
Review the fundamentals of data analytics and business metrics to enhance your understanding of the course concepts.
View
Big Data: Second Edition
on Amazon
Show steps
-
Read the book's introduction and first chapter to gain an overview of data analytics.
-
Summarize the key concepts of data collection, cleaning, and analysis.
-
Identify the different types of data formats and their applications.
-
Discuss the strengths and weaknesses of collected data.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve Data Cleaning Practice Problems
Show steps
Reinforce your data cleaning skills by solving practice problems to improve your accuracy and efficiency.
Browse courses on
Data Cleaning
Show steps
-
Locate a set of data cleaning practice problems online or in a textbook.
-
Solve the problems, focusing on identifying and correcting data inconsistencies and errors.
-
Review your results and identify areas for improvement.
Participate in a Peer Study Group
Show steps
Collaborate with peers to discuss course concepts, share insights, and work through problems collectively.
Show steps
-
Connect with classmates and form a study group of 3-5 people.
-
Establish a regular meeting schedule and agenda for your study sessions.
-
Take turns leading discussions, sharing perspectives, and providing feedback on assignments.
Build a Data Visualization Dashboard
Show steps
Demonstrate your understanding of data visualization by creating an interactive dashboard that presents insights from a given dataset.
Browse courses on
Data Visualization
Show steps
-
Gather a dataset relevant to your interests or coursework.
-
Clean and prepare the data for analysis.
-
Select appropriate visualization techniques to represent the data effectively.
-
Create an interactive dashboard using a visualization tool or software.
Review Basic Statistics and Probability Concepts
Show steps
Strengthen your understanding of essential statistical and probability concepts to support your learning in data analytics.
Browse courses on
Statistics
Show steps
Show steps
Show steps
- Review notes or textbooks from previous courses or online resources.
- Practice solving basic statistical problems, such as calculating mean, median, and standard deviation.
- Review probability distributions and their applications in data analysis.
Attend a Workshop on Data Analytics Essentials
Show steps
Enhance your understanding of data analytics principles and best practices through a structured workshop.
Show steps
Show steps
Show steps
- Research and identify upcoming workshops on data analytics essentials.
- Register for a workshop that aligns with your learning goals.
- Attend the workshop and actively participate in discussions and exercises.
Read 'Data Analytics Made Accessible'
Show steps
Review the fundamentals of data analytics and business metrics to enhance your understanding of the course concepts.
View
Big Data: Second Edition
on Amazon
Show steps
Show steps
Show steps
- Read the book's introduction and first chapter to gain an overview of data analytics.
- Summarize the key concepts of data collection, cleaning, and analysis.
- Identify the different types of data formats and their applications.
- Discuss the strengths and weaknesses of collected data.
Three other activities
Expand to see all activities and additional details
Show all six activities
Solve Data Cleaning Practice Problems
Show steps
Reinforce your data cleaning skills by solving practice problems to improve your accuracy and efficiency.
Browse courses on
Data Cleaning
Show steps
Show steps
Show steps
- Locate a set of data cleaning practice problems online or in a textbook.
- Solve the problems, focusing on identifying and correcting data inconsistencies and errors.
- Review your results and identify areas for improvement.
Participate in a Peer Study Group
Show steps
Collaborate with peers to discuss course concepts, share insights, and work through problems collectively.
Show steps
Show steps
Show steps
- Connect with classmates and form a study group of 3-5 people.
- Establish a regular meeting schedule and agenda for your study sessions.
- Take turns leading discussions, sharing perspectives, and providing feedback on assignments.
Build a Data Visualization Dashboard
Show steps
Demonstrate your understanding of data visualization by creating an interactive dashboard that presents insights from a given dataset.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather a dataset relevant to your interests or coursework.
- Clean and prepare the data for analysis.
- Select appropriate visualization techniques to represent the data effectively.
- Create an interactive dashboard using a visualization tool or software.
Career center
Learners who complete Introduction to Data Analytics will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts translate raw data into meaningful insights that businesses can use to make informed decisions. This course introduces the OSEMN framework for managing analytics projects, which is an industry-standard methodology used by Data Analysts around the world. You'll learn how to clean, analyze, and interpret data, which are essential skills for Data Analysts.
Marketing Analyst
Marketing Analyst
Marketing Analysts measure the effectiveness of marketing campaigns and provide insights that can be used to improve marketing strategies. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Marketing Analysts use to collect, analyze, and interpret data.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions and provide financial advice to clients. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Financial Analysts use to collect, analyze, and interpret data.
Operations Analyst
Operations Analyst
Operations Analysts use data to improve the efficiency and effectiveness of business operations. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Operations Analysts use to collect, analyze, and interpret data.
Risk Analyst
Risk Analyst
Risk Analysts identify and assess risks that can impact businesses, and develop strategies to mitigate those risks. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Risk Analysts use to collect, analyze, and interpret data.
Business Analyst
Business Analyst
Business Analysts identify and analyze business problems and opportunities, and develop solutions that can improve business performance. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Business Analysts use to collect, analyze, and interpret data.
Data Scientist
Data Scientist
Data Scientists use data to build models and simulations that can be used to solve business problems. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Data Scientists use to collect, analyze, and interpret data.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models that can be used to solve business problems. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Machine Learning Engineers use to collect, analyze, and interpret data.
Database Administrator
Database Administrator
Database Administrators manage and maintain databases that can be used to store and process data for analysis. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Database Administrators use to collect, analyze, and interpret data.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines that can be used to store and process data for analysis. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Data Engineers use to collect, analyze, and interpret data.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior and market trends. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Market Researchers use to collect, analyze, and interpret data.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use data to develop and test mathematical models that can be used to predict financial outcomes. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Quantitative Analysts use to collect, analyze, and interpret data.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts use data to identify and analyze business trends, and provide insights that can be used to improve business decision-making. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Business Intelligence Analysts use to collect, analyze, and interpret data.
Economist
Economist
Economists use data to analyze and interpret economic trends, and develop economic models that can be used to make predictions. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Economists use to collect, analyze, and interpret data.
Statistician
Statistician
Statisticians use data to analyze and interpret data, and develop statistical models that can be used to make predictions. This course introduces the OSEMN framework for managing analytics projects, which is a methodology that Statisticians use to collect, analyze, and interpret data.
For more career information including salaries, visit:
OpenCourser.com/course/vvo5zt/introduction
Reading list
We've selected 11 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
Introduction to Data Analytics.
Save
Provides a comprehensive overview of deep learning, including the latest research and applications. It's a valuable resource for those who want to learn more about the field of deep learning.
Provides a comprehensive overview of data analytics, covering the entire data analytics lifecycle from data collection to data visualization. It useful reference tool for learners who want to gain a deeper understanding of the fundamental concepts and techniques of data analytics.
Provides a comprehensive overview of big data analytics, covering various big data analytics techniques and tools. It useful reference tool for learners who want to gain proficiency in big data analytics.
Provides a comprehensive overview of machine learning for data analytics, covering various machine learning algorithms and techniques. It useful reference tool for learners who want to gain proficiency in machine learning for data analytics.
Provides a comprehensive overview of digital marketing analytics, covering various digital marketing analytics techniques and tools. It useful reference tool for learners who want to gain proficiency in digital marketing analytics.
Save
Provides a comprehensive overview of data analytics for finance, covering various data analytics techniques and tools specific to finance. It useful reference tool for learners who want to gain proficiency in data analytics for finance.
Provides a comprehensive overview of data analytics using R, covering various data analytics techniques and tools. It useful reference tool for learners who want to gain proficiency in data analytics using R.
Provides a gentle introduction to reinforcement learning, including the basic concepts and algorithms. It's a great choice for those who are new to the field of reinforcement learning.
Is great for those looking for an introduction to data analytics. If this course is too advanced, this book offers an introduction to the topic of data analytics. However, as an introductory addition to this course, this book will provide helpful context.
Provides an overview of the data analytics process, including tips and techniques for using data to drive better decisions. It's a great choice for those looking for a quick introduction to the field.
Save
Provides a beginner-friendly introduction to data analytics, covering the basics of data analytics and various data analytics techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vvo5zt/introduction
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Effort
4 weeks of study, 5-6 hours/week
Level
Introductory
Via
Coursera
Institution
Meta
Instructor
Anke Audenaert
Language
English
Transcripts
Español
Français
Português
Deutsch
العربية
Ελληνικά
magyar nyelv
Bahasa Indonesia
ไทย
اُردُو,
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پښتو
آذربایجان دیلی
Good to know
Appropriate for learners who have basic internet navigation skills
Covers basic analytics tasks, including data extraction, cleaning, manipulation, and analysis
Introduces the OSEMN cycle for managing analytics projects
Examines real-world examples of how companies use data insights to improve decision-making
Provides a foundation for learners who want to pursue further studies in marketing analytics
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