second course covered some topics including:
Introduction to Data Analytics Course Syllabus
Course Overview
This course equips you with a practical understanding of basic analytics tasks such as using SQL queries to pull data from a database and cleaning data using spreadsheets by explaining the OSEMN cycle for analytics projects. You will learn foundational programming concepts and how they apply to marketing. You will be introduced to using the Python programming language to manipulate datasets as an alternative to spreadsheets. You will also learn how to use Tableau to create data visualizations and dashboards.
Learning Objectives
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 different marketing related questions
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 in the available data
Demonstrate basic proficiency in Python with variables, control flow, loops, and basic data structures
Sort, query and structure data in spreadsheets and with Python libraries
Visualize data patterns and trends with spreadsheets
Utilize Tableau to visualize data patterns and trends
Explain the basic structure of Google Analytics and the metrics it reports
Interpret information about a website, app and the effect of advertising through the use of Google Analytics
Combine data from multiple sources through the use of Google Data Studios
Evaluate the most appropriate visualization to use given one's narrative
Introduction to Data Analytics Overview
By the end of each week, you will be able to:
Week 1: Working with Data
Define the OSEMN cycle.
Apply the OSEMN cycle to a given marketing situation.
Define "clean" and "dirty"" data.
Vocalize high level insights from data sets.
Write an appropriate analytics question that includes business goals and objectives, as well as KPIs.
Week 2: Python for Data Analytics
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 in the available data
Demonstrate basic proficiency in Python with variables, control flow, loops, basic data structures, and functions
Week 3: Data Cleaning and Processing
Sort, query, and structure data with spreadsheets
Evaluate the limitations of spreadsheets as a means of cleaning data
Sort, query, and structure data with SQL
Sort, query, and structure data using Pandas, a Python library
Using Pandas DataFrames to clean and explore data
Week 4: Introduction to Data Visualization
Understand different visualizations and where they are relevant
Create good storytelling about data using visualizations
Visualize data patterns and trends with spreadsheets and Tableau
Week 5: Structuring Real-World Analytics Projects
State business goals, KPIs and associated metrics as a research question
Apply a Data Analysis Process: OSEMN
Identify and define the relevant data to be collected for different marketing related questions
Interpret information and the effect of advertising
Use data to create visualizations with Tableau
Combine and present findings in a presentation format
Projects and Quizzes
Week 1: Working with Data
Graded Quiz: An Analytics Case Study
This quiz will test your ability to choose appropriate sources of data and to identify what to look for when cleaning data. It will also test your knowledge of various data model classes and their appropriateness for a given situation.
Week 2: Python for Data Analytics
Graded Quiz: Python for Data Analytics
In this quiz you will demonstrate your ability to write functions in Python.
Week 3: Data Cleaning and Processing
Graded Quiz: Pandas and SQL Assessment
This week you’ll be tested on your understanding of using Pandas and SQL when analyzing data.
Week 4: Introduction to Data Visualization
Graded Quiz: Visualization and Storytelling
This week’s quiz you’ll be tested on your understanding of good visualizations and building a narrative with your data and analysis.
Week 5: Structuring Real-World Analytics Projects
Peer Review: Interpreting Data
This week you will complete a capstone project that combines all the skills you’ve developed in this course including the OSEMN process and data visualization. The finished product will be something you can include in a professional portfolio.
