The content is my own and not related to any company, it is entirely the end product of educational projects organized by Udacity and Coursera
Product Managers are responsible for designing and delivering a profitable product or feature into the market. Define product strategy and KPIs based on market analysis, pitch a product vision to get stakeholder buy-in, and design a user-centered prototype that adheres to engineering constraints. Develop an execution timeline that handles competing priorities, communicate a product roadmap that builds consensus amongst internal stakeholders, and create a comprehensive go-to-market plan based on product KPIs.
Program Syllabus
Certificate
PM role by GitLab
- product vision
The most effective products start with a comprehensive market-based, insight-driven strategy. Identify the right problems to solve through market research, target user definition, and market sizing. Create a compelling vision and strategy that will set up the team to solve those problems. - design sprint
Take an idea through concept, design, and user validation phases, and create a spec to handoff to Engineering for development. Use design-thinking methodologies to explore various ideas, and then converge on a single idea. Map out the full concept through creation of a prototype that can be used to validate that you’re solving a problem for real users. - product development
Critical soft skills needed to manage the development and execution phase of the product. Collaborate with cross-functional teams and stakeholders to guide them through planning and execution. Manage stakeholder expectations and handle risks that arise, reprioritizing feature and sprint priorities to tackle competing requests. Product Development Flow - PRD & go-to-market
Create a plan, identify the launch risks, and figure out how to minimize their impact on your launch. Collaborate with a variety of teams including Marketing, Sales, Customer Support, and more to prepare them to interface with customers as the product is launched. Execute the launch and use feedback from your customers to determine the next steps for your product. - Product Process
- Product Manager Interview Preparation
- Product School San Francisco
- 3 Prioritization Techniques All Product Managers Should Know
- Common Product Prioritization Mistakes
- Lean Product Management by Mangalam Nandakumar
- Product Management in Practice by Matt LeMay
- Idea validation workbook
Evaluate the business value of an AI product. Start by building familiarity and fluency with common AI concepts. Learn how to scope and build a data set, train a model, and evaluate its business impact. Ensure a product is successful by focusing on scalability, potential biases, and compliance.
Program Syllabus
Certificate
- build a model
How a neural network produces a decision and how “training” works. Use training data and how to evaluate the results of a model. - UX plan
- Create AI Product proposal
How to measure post-deployment impact, and how to make data-informed improvements on your model. Understand how to avoid unwanted bias, ensure security and compliance, and how to scale your product.
- Artificial Intelligence nd with specialization certification
- Deeplearning specialization
- AI capstone project
Build a deep neural network that functions as part of an end-to-end automatic speech recognition (ASR) pipeline. The model will convert raw audio into feature representations, which will then turn them into transcribed text. - Full stack bootcamp
- Machine Learning Yearning by Andrew NG
- AI as a Service by Peter Elger
Leverage market data to amplify product development. Apply data science techniques, data engineering processes, and market experimentation tests to deliver customized product experiences. Begin by leveraging the power of SQL and Tableau to inform product strategy. Then, develop data pipelines and warehousing strategies that prepare data collected from a product for robust analysis. Finally, evaluating the data from live products, including how to design and execute various A/B and multivariate tests to shape the next iteration of a product.
Program Syllabus
Certificate
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Applying Data Science to Product Management
As products become more digital, the amount of data collected is increasing. Product managers now have the opportunity to utilize this data to not only enhance existing products, but create completely new ones. Understand the role of data product managers within organizations and how they utilize data science, machine learning, and artificial intelligence to solve problems. Visualize your data with Tableau for statistical analysis and identify unique relationships between variables via hypothesis testing and modeling. Evaluate the output captured in statistical analyses and translate them into insights to inform product decisions. -
Establishing Data Infrastructure
Data product managers need to ensure their products have the appropriate supporting data pipelines in place so that data collected from users can be extracted, transformed, and loaded into a data lake or warehouse that can be used for statistical analysis. -
Leveraging Data in Iterative Product Design
The best products adapt to market changes over time and are constantly being refined based on user feedback. With a robust data pipeline, the amount of data collected through product usage is extremely valuable to product managers for enhancing their products. Understand which data is best collected through quantitative versus qualitative methods, and how to interpret it. Learn how to apply chi-square tests to determine if results from data analysis are statistically significant. Utilize user data to create user personas that are actionable for development teams to translate into code and for building out user journey maps that describe the stages a user engages with the product along with the associated risks and opportunities. Extract insights from user journey maps to define KPIs of suggested product enhancements and design the relative hypotheses and experiments that are needed to prove the assumptions of product enhancements.
- Applied Machine Learning in Python
Introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. - Introduction to Data Science in Python
Basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. - Structuring Machine Learning Projects
- End-to-end Data Analytics for Product Development by Rosa Arboretti Giancristofaro, Mattia De Dominicis, Chris Jones, Luigi Salmaso
- Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights by Joanne Rodrigues-Craig
Build acquisition funnels, identify core customers, and optimize growth loop models. Analyze your results and make improvements to your strategies. Activation theories on how to decrease time-to-value and friction for both B2C and B2B product users, as well as retention theories, for creating audiences and increasing engagement. Monetization, from designing pricing plans to determining optimal price points using pricing metrics.
Program Syllabus
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Growth and Acquisition Strategy
Build a growth strategy plan to acquire new customers by defining the target market, identifying personas, and aligning them to most efficient acquisition channels. Expand into new markets by harnessing telemetry, customer feedback, market trends, and competitive landscape data. Apply behavioral psychology to the customer purchase process to improve product design, and run A/B tests to assess success. -
Activation and Retention Strategy
Understand how to guide users to the a-ha moment of your product as soon as possible, via the activation funnel and sign-up flow. Apply best practices for how to best engage customers, and retain them for the long term. Analyze the user lifecycle, including the activation, retention, dormancy, and resurrection phases, and deploy experiments to improve the lifetime value (LTV) and decrease churn.- supplement materials:
What is good retention
Coversion funnel Strategy
- supplement materials:
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Monetization Strategy
Learn how to make your product profitable, including selecting key markets and outreach channels, and build purchase paths that leverage industry case studies. Apply best practices of experience design, and learn how to measure the effectiveness of your monetization strategy. Design and define pricing plans that utilize quantitative and qualitative methods.- supplement materials:
SaaS Metrics – A Guide to Measuring and Improving What Matters
SAAS PRICING MODELS, STRATEGIES & PSYCHOLOGICAL HACKS
How to price your SaaS product
Sales Process Checklist Template
unit of economics case study
amazon drone unit of economics
GitLab Price Strategy
ProfitWell Reports
Pricing Bllog
- supplement materials:
Create a digital user experience that is ready to be handed off for development. Building familiarity and fluency with design research fundamentals to identify the user and the solutions they need. Synthesize your research, and use design sprints to take an idea from concept to low-fidelity prototype. Turn your low-fidelity prototype into a high-fidelity design, and improve its performance based on data.
Program Syllabus
UX Research by GitLab
UX resources
- Machine Learning with TensorFlow on Google Cloud Platform Specialization Write distributed machine learning models that scale in Tensorflow, scale out the training of those models. and offer high-performance predictions. Convert raw data to features in a way that allows ML to learn important characteristics from the data and bring human insight to bear on the problem. Finally, learn how to incorporate the right mix of parameters that yields accurate, generalized models and knowledge of the theory to solve specific types of ML problems. You will experiment with end-to-end ML, starting from building an ML-focused strategy and progressing into model training, optimization, and productionalization with hands-on labs using Google Cloud Platform. Certificate