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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.
Overview
Syllabus
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- 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.
