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Use market data to boost product development. Learn how to deliver customised product experiences using data science techniques, data engineering processes, and market experimentation tests. Begin by leveraging SQL and Tableau's power to inform product strategy. Then, create data pipelines and warehousing strategies to prepare product data for robust analysis. Finally, learn techniques for analysing data from live products, such as how to design and execute various A/B and multivariate tests to shape a product's next iteration.
Syllabus:
Course 1: Applying Data Science to Product Management
Introduction to Data Product Management
- Explain the concept and history of data product management
- Distinguish the different types of data product managers
- Identify the various internal stakeholders that data product managers work with
- Understand the fundamentals of general product management from talking to customers, analyzing data, designing high-level solutions, prioritizing work, setting a roadmap, facilitating development, launch communications, and product iteration
Granularity, Distribution, and Modeling Data
- Analyze what is being measured in a dataset
- Explain the benefits of aggregates or roll-up tables
- Compare and contrast the differences between fact & dimensional tables
- Calculate and analyze the distribution of a dataset
Trends, Enrichment, & Visualization
- Identify and differentiate different visualizations, and justify when to apply the right visualization for the appropriate analyses (spatial, temporal, distribution, correlation) - box plot, line chart, donut chart, density map, histogram
- Implement enriching datasets, and utilize common online repositories for publicly available datasets for analysis.
Develop a Data-Backed Product Proposal (Part 1)
- Utilize SQL and other data analysis techniques to explore and enrich a dataset to identify customer pain points, trends, and opportunities
Setting Product Objectives & Strategy
- Interpret data and insights to come up with product objectives
- Design KPIs that measure if your products are meeting their objectives
- Utilize best practices and different techniques for setting up explicit feedback mechanisms
- Create experiments that generate meaningful results in a timely, resourceful manner
- Drive instrumentation strategies for proper event data collection
Proposal Synthesis & Design
- Assemble & arrange your narrative based on stakeholders
- Weave data visualizations and insights into presentations in a consumable format • Develop the key points to hit in a product proposal presentation
Project: Develop a Data-Backed Product Proposal
Analyzing market data to propose new product opportunities is a key responsibility of data product managers. In this project, you will use the skills you learned in this course to develop the MVP launch strategy for Flyber, the world's first flying car taxi service, in one of America's most congested cities, New York City. Your team gathered taxi data in order to conduct a comparable preliminary analysis. First, you will examine the dataset for existing use cases and identify temporal, behavioural, and spatial trends of ground-based taxis. Following that, you will delve into user research data to gain a better understanding of the general sentiment, desire, concerns, and use cases of a flying cab service among prospective customers. Finally, you will synthesise your findings to create a data-backed product proposal outlining which features the first flying taxi service should have in order to maximise consumer delight, adoption, and profits.
Course 2: Establishing Data Infrastructure
Introduction to Data Pipelines
- Understand the importance and need of data pipelines
- Understand the various components of data pipelines
- Learn how to organize data pipeline components to automate end-to-end data flow
- Create conceptual data pipelines
- Learn about the influence of Saas and IoT on the data infrastructure world
- Understand classic data problems that can be addressed by data pipelines
Data Consumers
- Learn about primary data consumers and their data needs
- Identify data consumers in an organization and relevant data use cases based on their business goals
- Understand the components in building a relational data model
- Apply relational data models to business scenarios
Data Producers
- Learn how to create event data models and implement them to get business insights
- Understand primary product management KPIs (Active Users,
- Session Length, Bounce Rate, Conversion Rate and Click- through-Rate)
- Use data collected from event models to calculate product KPIs
- Identify primary data producers in an organization
- Distinguish between backend data producers (Saas, ERPs and Data stores)
- Differentiate between types of data (structured vs. semi- structured vs. unstructured)
Data Strategy
- Understand the difference between ETL and ELT processes
- Distinguish between batch processing and stream processing
- Select the appropriate data processing components for the product based on data needs
- Distinguish between a data warehouse and data lake
- Differentiate between SQL and NoSQL databases
- Determine the appropriate data storage components for a particular data infrastructure of a product based on data needs
- Assess capabilities of various data warehousing options (build vs buy, cloud vs on-prem, open source vs proprietary and insource vs outsource) to make strategic decisions for data infrastructure
- Understand data security and compliance (PII, PCI, HIPAA, GDPR and CCPA) components related to product use cases
Project Build a Scalable Data Strategy
Once a product is released to the market, the amount of data collected typically skyrockets, necessitating the necessary infrastructure to support such growth. In this project, you will continue to act as a data product manager for Flyber, a flying-taxi service that has been massively successful in New York City since its initial product launch, and develop a data strategy to not only handle the massive amount of incoming data, but also process it to obtain the business insights required to grow the business. To begin, you will define the data needs of the organization's primary business stakeholders and develop a data model to ensure that the data collected meets those needs. Then, you will extract and transform the data as needed to make it relevant to answering business questions. Finally, you will interpret data visualisations to comprehend the scope of Flyber's data growth and select an appropriate data warehouse to support that expansion.
Course 3: Leveraging Data in Iterative Product Design
Choose & Measure KPIs
- Describe how data collection and usages changes depending on the state of the software (from pre-launch to product with existing customer base)
- Choose common KPIs for different business models (freemium, SAAS, eCommerce)
- Calculate the most popular KPIs for user acquisition, activation, retention, and revenue
- Suggest additional data that should be collected to allow for KPI tracking.
Evaluate User Acquisition & Usage Funnels
- Identify the steps in a typical user acquisition and activation funnel
- Run analyses in Tableau to determine rate of user dropoff during each step of a funnel
- Visualize a funnel analysis in Tableau in bar chart form
Cohort Analysis
- Explain the importance of segmenting user data by cohorts
- Identify behavioral traits in a data set that could be used to analyze cohort behavior
- Apply cohort analysis to segment funnel analysis
- Calculate feature use within a product, both among all users and among selected cohorts using existing event data
Qualitative & Quantitative Data
- Explain the benefits and drawbacks of quantitative data
- Explain the benefits and drawbacks of qualitative data
- Determine when qualitative data is most useful during the iterative design process
- Describe unstructured and structured methods of qualitative research, including interviews/focus groups, surveys, and prototype testing
- Explain the framework of “jobs to be done” as used during qualitative research
- Narrow scope and choose which feature(s) to test first using the RICE framework
A/B Test & Multivariate Test
- Explain the benefits and drawbacks of A/B testing
- Explain the benefits and drawbacks of multivariate testing
- Determine what type of test is appropriate given feature(s) of interest
- Determine what user actions should be tracked during A/B and multivariate tests
- Explain methods to create unbiased control and test groups of users
- Apply the correct statistical methods to explain the difference between the experimental and control group data and make a decision
Project Create an Iterative Design Path
As products enter the market, opportunities for product design improvements emerge. In this project, you will continue in your role as a data product manager for Flyber, a flying-taxi service with an exponentially growing user base, and define customer segments as well as relevant new product feature opportunities. First, you will analyse data from an A/B test to identify key behavioural and descriptive characteristics of users in order to define user personas and map out the significant stages of the user journey within the Flyber app. Then, you'll create an assumption map to explain the testable risks, opportunities, and correlated KPIs for app experience product design improvements, such as the most impactful page and most significant subset of users. Finally, you will use the completed assumption map, as well as the developed user persona and journey, to develop hypotheses for new Flyber app product features and conduct experiments to validate these hypotheses.
