EdX

Data Science and Agile Systems for Product Management (edX)

Data Science and Agile Systems for Product Management (edX)

Deliver faster, higher quality, and fault-tolerant products regardless of industry using the latest in Agile, DevOps, and Data Science. Modern systems today must be designed for agility in order to outpace the competition. Concepts like Agile, DevOps, and Data Science were once considered only for the technology-based companies. Today that means every company. Because there is no greater currency than timely information for optimizing operations and meeting the needs of customers.

Class Deals by MOOC List - Click here and see EdX's Active Discounts, Deals, and Promo Codes.

Modern product management requires that every development and operations value stream is identified and continuously improved. This means using Lean and DevOps principles to streamline handoffs and information flows across teams. It means reorienting towards self-service and automation wherever possible. And to avoid incrementalism, it means a robust Agile development process to keep innovations important and aggressive enough to make noticeable improvements in value delivery.
Agile systems in a DevOps environment requires that products are built completely differently from a traditional designs. Modularity, open set architectures, and flexible data management paradigms are a starting point. The evolutionary nature of the product with so much change enables functionality, design, and technology to drive and influence each other simultaneously. And beneath it all is a data collection and feedback loop essential for anticipating and reacting to business needs both for operations and marketing.
Data science and analytics are the lifeblood of any product organization, and enable product managers to tackle risks early. Luckily, new technologies allow us to collect and integrate data without extreme upfront constraints and onerous controls. This means all data is fair game, and when tagged and stored properly, can be made available at nearly any scale for preparation, visualization, analysis, and modeling.
We’ll teach you the paradigms, processes, and introduce some key technologies that make the data-driven product organization the optimal competitor in the market.
This course is part of the Product Management Professional Certificate.

What you'll learn

  • Designing and modeling for fast feedback and idea sharing
  • System optimization with open architectures
  • Validating functions and verifying performance
  • Leveraging and enabling the system designs, platforms, and ecosystems
  • Lean Startup and Product Innovation Analytics
  • Developing the data collection and preparation pipeline for products and services
  • Analyzing the performance and testing hypotheses for usability, fast-feedback, and growth
  • Customer experience (CX) validation and enhancement leveraging usability analytics

Syllabus

Module 1: Agile Systems Engineering
Module 2: DevOps Principles for Business Agility
Module 3: Data Science for Product Risk Management
Module 4: Implementing Data-Driven Controls using Technology and Teams

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Introduction to Data Science (edX) EdX
IBM

Introduction to Data Science (edX)

Learn about the world of data science first-hand from real data scientists. The art of uncovering the insights and trends in data has been around for centuries. The ancient Egyptians applied census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year.

Self Paced
Self-Paced
Data Science Tools (edX) EdX
IBM

Data Science Tools (edX)

Learn about the most popular data science tools, including how to use them and what their features are. In this course, you'll learn about Data Science tools like Jupyter Notebooks, RStudio IDE, and Watson Studio. You will learn what each tool is used for, what programming languages they can execute, their features and limitations and how data scientists use these tools today.

Self Paced
Self-Paced
Data Science: Capstone (edX) EdX
HarvardX,Harvard University

Data Science: Capstone (edX)

Show what you’ve learned from the Professional Certificate Program in Data Science. To become an expert data scientist you need practice and experience. By completing this capstone project you will get an opportunity to apply the knowledge and skills in R data analysis that you have gained throughout the series. This final project will test your skills in data visualization, probability, inference and modeling, data wrangling, data organization, regression, and machine learning.

Self Paced
Self-Paced
Python for Data Science (edX) EdX
University of California, San Diego,UC San DiegoX

Python for Data Science (edX)

Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets. In the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?

Self Paced
Self-Paced
Computational Thinking and Big Data (edX) EdX
University of Adelaide,AdelaideX

Computational Thinking and Big Data (edX)

Learn the core concepts of computational thinking and how to collect, clean and consolidate large-scale datasets. Computational thinking is an invaluable skill that can be used across every industry, as it allows you to formulate a problem and express a solution in such a way that a computer can effectively carry it out.

Self Paced
Self-Paced
Probability and Statistics in Data Science using Python (edX) EdX
University of California, San Diego,UC San DiegoX

Probability and Statistics in Data Science using Python (edX)

Using Python, learn statistical and probabilistic approaches to understand and gain insights from data. The job of a data scientist is to glean knowledge from complex and noisy datasets. Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

Self Paced
Self-Paced
Modern Product Leadership (edX) EdX
University of Maryland, College Park,University System of Maryland - USM,USMx,UMD

Modern Product Leadership (edX)

Lead product teams and organizations to deliver winning solutions for customers while driving engagement and innovation across the enterprise. Product Managers are masters at internal sales, team building, delegation, and empowerment. They also know that great product management careers are not built alone. From envisioning a product strategy to correctly portraying the product roadmap, the process must be inclusive, interactive, and motivating with modern leadership techniques. As your products grow, you’ll also need to skills to manage teams of teams. This course will enable you to align product teams, product designs, and product development for speed and innovation at scale.

Self Paced
Self-Paced
Agile Process, Project, and Program Controls (edX) EdX
University of Maryland, College Park,University System of Maryland - USM,USMx,UMD

Agile Process, Project, and Program Controls (edX)

Learn Agile controls that get work done with confidence by using true transparency (actuals not estimates) and continuous improvement to ensure your people, process, and products deliver valuable, working solutions. Agile provides greater opportunities for control and risk management and offers unique benefits that traditional methods miss. As a project manager or program manager the emphasis should always be on delivering value and benefits. With complex projects these demand increase and knowing you've delivered value can be difficult for even those with years of project management experience.

Self Paced
Self-Paced
Data Science: Probability (edX) EdX
HarvardX,Harvard University

Data Science: Probability (edX)

Learn probability theory — essential for a data scientist — using a case study on the financial crisis of 2007–2008. In this course, you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007–2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability.

Self Paced
Self-Paced