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.
Overview
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.
- 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.
- [Optional] GenAI in Data Analytics
- In this optional module, you learn what generative AI is and how it functions. You also discover how GenAI can be applied in different business scenarios as well as navigating the concerns around its usage. Then you explore how to incorporate GenAI into your data analytics efforts to streamline processes and improve data quality.
Taught by
Rav Ahuja
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Reviews
4.4 rating, based on 5 Class Central reviews
4.8 rating at Coursera based on 559 ratings
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One of the best courses on data analysis in python. It was a deep dive into the basics. More than the course videos, i enjoyed the assignments more.
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The specificity of this course (it opens a specialization consisting of 9 courses) is that it contains a lot of generalities and should rather be reviewed as a relatively good introduction to the specialization as a whole.
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As expected, this course, from a leader in the data analytics/science field, provides an excellent overview of the state of the art in data analytics. Unlike other courses that I tried in the topic, this offering hit the ground running by emphasizing high-yield points from both the official material and the expert viewpoints. I am looking forward to finish the Professional Certificate where this course belongs.
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The OSEMN data analysis framework was useful as a refresher for data analysis projects.
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CJ HorEasy to understand and systematic. I enjoy going through every topic. This is a stepping stone in starting a new career in data analyst.
