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In this course, you will learn:
- Collect, clean, sort, evaluate, and visualise data.
- Use the OSEMN framework to drive the data analysis process, ensuring a thorough and disciplined approach to extracting actionable insights.
- Make data-driven judgments by using statistical analysis, such as hypothesis testing and regression analysis.
- The fundamental concepts of successful data management and the usefulness of data assets in an organizational environment.
Syllabus:
1. Introduction to Data Analytics
- Apply the data analysis process OSEMN to marketing data
- Compare and contrast various data formats and their applications across different scenarios
- Identify data gaps and articulate the strengths and weaknesses of collected data
2. Data Analysis with Spreadsheets and SQL
- This course introduces you to how to use spreadsheets and SQL queries to analyze and extract data. You will learn how to practically apply the OSEMN data analysis framework and spreadsheet functions to clean data, calculate summary statistics, evaluate correlations, and more. You’ll also dive into common data visualization techniques and learn how to use dashboards to tell a story with your data.
3. Python Data Analytics
- Sort, query, and structure data in Pandas, the Python library
- Describe how to model and interpret data using Python
- Create basic data visualizations with Python libraries
4. Statistics for Marketing
- The basic principles of descriptive and inferential statistics
- Use statistical analyses to make data-driven decisions
- How to formulate and test hypotheses and take action based on the outcome
5. Introduction to Data Management
- How to apply the fundamentals of data collection and data quality management
- Different type data storage solutions and architectures, including big data management and how they are used
- The fundamentals of data privacy and compliance, as well as the basics of machine learning
