Topic 1: The lesson explores what artificial intelligence is and then dives into the course with an introduction to what problem statements are and why it is essential to create them.

Topic 2: A workshop-type class where students will use all they have learned to create a problem statement and five hypotheses for their autonomous car company.

Topic 3: Students will spend this lesson presenting their problem statements for their autonomous car company.

Topic 1: This lesson is an introduction to customer validation interviews, what they are, and how to conduct them.

Topic 2: This lesson provides students with an opportunity to test their interviewing skills, problem statements, and hypotheses.

Topic 3: Students will spend this lesson practicing their interviewing skills and testing their problem statements and hypotheses.

Topic 4: This lesson aims to provide the students with more background information on autonomous cars and provide them with experts’ opinions.

Topic 5: The students will spend this lesson turning qualitative information into quantitative information.

Topic 6: A deeper dive into turning the qualitative data from the student’s customer interviews into quantitative data.

Topic 1: This lesson will help students understand the importance of a good market and how to define their market.

Topic 2: This lesson will help students understand the how to calculate a market’s size using two methods (Top-down and Bottom-up)

Topic 3: Workshop on market sizing using the jigsaw method

Topic 1: This lesson introduces what sampling is and the different types of sampling.

Topic 2: Students will learn how to calculate sample size and determine the population that their company is serving and the sample size they require for their customer interviews.

Topic 3: In this lesson, students will complete two portfolio documents that they should submit to you.

Topic 1: In this lesson, students will learn the goals of market research surveys and what makes an effective survey.

Topic 2: In this lesson, students will learn the best practices for building surveys in Google Forms.

Topic 3: In this lesson, students are tasked to design a survey for their target customers.

Topic 1: At the end of the lesson, students will have a greater knowledge of the benefits of cleaning data and an introductory understanding of potential data cleaning methods.

Topic 2: This lesson promotes critical thinking by challenging the students to figure out data cleaning methods in Google Sheets and try to perform them on their own.

Topic 3: The main objective is for students to identify all of the different dirty data types within the shared dirty dataset and then clean the dataset entirely.

Topic 4: The students will be prompted to think about the effects that missing data can have on data analysis.

Topic 5: Students will be able to identify the relevance of the missing data and its type.

Topic 1: This class will introduce students to data manipulation, how to organize data in a spreadsheet and how to use inbuilt functions in Google Sheets.

Topic 2: This class will introduce students to visualization, how to build charts in a spreadsheet, and how to use inbuilt functions in Google Sheets.

Topic 3: This class is a continuation of data visualization.

Topic 4: In teams of two, students will analyze a data set and create a short presentation using the functions and charts that they have learned.

Topic 1: Understand what a customer persona is, why they are important to organizations, and how to create one based on the product of interest.

Topic 2: In this lesson, students apply their own customer personas to identify potential customers for their product

Topic 3: Understand what clustering is, why organizations use clustering, the benefits of it and how to implement clustering.

Topic 4: Become familiar with the concept of K-Means clustering and algorithms.

Topic 5: In this lesson, students will use our platform to design a K-Means clustering project of their own

Topic 1: This lesson introduces the concept of customer lifetime value (LTV) and illustrates a use case.

Topic 2: Students perform their own estimations for 3 and 5 year lifetime value of their target customers using a linear regression model.

Topic 3: This lesson introduces the concept of customer acquisition cost (CAC) and why it is so crucial to marketing success.

Topic 4: Students estimate the customer acquisition cost of their target customers using a linear regression model.

Topic 5: Students calculate the ratio of customer lifetime value and customer acquisition costs to determine the success and sustainability of their marketing.

Topic 1: Students explore what is a brand and the value that brands provide

Topic 2: Students are tasked with defining the key characteristics of their company’s brand, utilising their customer segmentation

Topic 3: Introduces students to brand perception analysis and the use of natural language processing (NLPS)

Topic 4: Discussion on how brands may use NLPS to scrape data from the web for brand sentiment analysis

Workshop I: Using natural language processing to design a sentiment analysis project from a selected dataset

Workshop II: Using natural language processing to design a sentiment analysis project from a selected dataset

Topic 1: Introduction to defining KPIs (key performance indicators)

Topic 2: Methods to effectively track and analyze KPI data

Topic 3: Introduction to digital advertising

Topic 4: Defining an appropriate target audience for digital advertisements

Topic 5: Effective budgeting for digital marketing campaigns

Exercise: Building a Facebook ad campaign

Topic 1: Introduction to design thinking

Topic 2: Using principles of design thinking to test, iterate, and build a user-centric product

Topic 3: Introduction to post-purchase customer behavior and the importance of continuous product iteration

Topic 4: In this lesson, students learn the connection between sales and product re-design

Topic 5: In this lesson, students will explore advanced focus group techniques and product use-cases