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Introduction to Data Analytics

IBM via Coursera


This course equips you with a practical understanding and a framework to guide the execution of basic analytics tasks such as pulling, cleaning, manipulating and analyzing data by introducing you to the OSEMN cycle for analytics projects. You’ll learn to perform data analytics tasks using spreadsheet and SQL queries. You will also be introduced to using the Python programming language to manipulate datasets as an alternative to spreadsheets. You will learn foundational programming concepts and how they apply to marketing. You will also learn how to use Tableau to create data visualizations and dashboards.

By the end of this course, you will be able to:
• State business goals, KPIs and associated metrics
• Apply a Data Analysis Process: OSEMN
• Identify and define the relevant data to be collected for marketing
• Compare and contrast the different formats and use cases of different kinds of data
• Identify gaps in data collected and describe the strengths and weaknesses
• Demonstrate proficiency in Python with variables, control flow, loops, and basic data structures
• Sort, query and structure data in spreadsheets and with Python libraries
• Write basic SQL statements to select, group and filter data
• Visualize data patterns and trends with spreadsheets
• Utilize Tableau to visualize data patterns and trends

This course is designed for people who want to learn the basics of data analytics including using spreadsheets and Python to sort and structure data and using Tableau to visualize data patterns.

Learners don't need marketing or data analysis experience, but should have basic internet navigation skills and be eager to participate. Learners also need access to a computer with strong internet connection. Ideally learners have already completed course 1 (Marketing Analytics Foundation) in this program.


  • Working with Data
    • This week you’ll get an overview of the Introduction to Data Analytics Course and then you’ll be introduced to setting Goals, Objectives and Key Performance Indicators for marketing campaigns. The 5 steps of a Data Science Project will be explained with the introduction of the OSEMN cycle framework. You’ll finish out the week seeing a real-life application of each step of the OSEMN cycle.
  • Python for Data Analysis
    • This week you will be introduced to programming in Python. You will learn foundational programming concepts such as variables, data types, and functions.
  • Data Cleaning and Processing
    • In week three, you’ll dig into how to clean and process data you’ve gathered using spreadsheets, SQL, and the Python Data Analytics Stack (Pandas).
  • Introduction to Data Visualization
    • This week you’ll be introduced to the Tableau platform which you will use to create data visualizations and dashboards. You’ll learn different types of visualization and their use cases.
  • Structuring Real-World Analytics Projects
    • This week you will combine all the information you have learned throughout the course and apply it in your first data analytics project.

Taught by

Rav Ahuja


4.5 rating, based on 2 Class Central reviews

3.4 rating at Coursera based on 104 ratings

Start your review of Introduction to Data Analytics

  • 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.
  • 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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