Free Product Management Course for Tackle Challenges in Data Science Product Management
Learn how to tackle obstacles in data science product management with our free course. Gain valuable skills and insights to excel in this field. Enroll now!
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Learning Objectives
1. Embracing Resilient Problem-Solving Strategies
Highlighting the importance of overcoming challenges in data science and product management by combining creativity with analytical thinking.
2. Application of Diverse Data Science Products
Examining how data science companies employ a range of products influenced by industry leaders such as Netflix, OpenAI, and social media giants.
3. Leveraging Generative AI for Innovative Solutions
Exploring the impact of unsupervised models like Chat GPT in predicting Internet responses and shaping vast data sets, signaling a shift in machine learning.
Utilizing noise application to existing data for synthetic data creation to augment datasets and improve deep learning model training and performance.
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Who's it for
Data Scientists
Product Managers
Machine Learning Engineers
Fintech Executives
Risk Management Professionals
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What will I learn
Chapter 1
Introduction to Data Science and Product Management Challenges
This chapter sets the stage for understanding the complexities faced in the fields of data science and product management. It highlights the importance of resilience and problem-solving skills, emphasizing the need to combine creativity with analytical thinking to navigate challenges effectively.
Chapter 2
Influence of Key Industry Players in Data Science Products
Here, the chapter explores how data science companies are influenced by major industry players such as Netflix, OpenAI, and social media giants. It delves into how understanding performance targets for predictive models, shaped by these key players, is crucial for success in the field.
Chapter 3
Evolving Landscape of Machine Learning and AI Models
This chapter discusses the shift in machine learning towards generative AI, especially in unsupervised models like Chat GPT. It also touches upon the prevalence of supervised learning in solving industry challenges and the limitations faced by deep learning models due to insufficient training data.
Chapter 4
Data Augmentation and Model Performance Tracking
The chapter delves into the importance of synthetic data creation and applying noise to existing data to augment datasets for better model performance. It also highlights the significance of tracking model predictions effectively to evaluate performance and establish a robust infrastructure for comparison.
Chapter 5
Balancing Accuracy and Cost in Predictive Modeling
Here, the chapter emphasizes the need to prioritize accuracy in predictions before deployment, stressing the balance between true positives and false positives in risk assessment. It discusses the costs incurred from missing predictions or unnecessary preparations in predicting severe weather.
Chapter 6
Risk Management and Decision-making in Product Management
This chapter focuses on evaluating benefits and costs in decision-making with monetary implications, particularly in assessing self-reinforcing and self-destructive use cases. It stresses the importance of integrating accuracy assumptions into strategies and prioritizing risk management for overall positive impact.
Chapter 7
Improving Outcome Estimates and Mitigating Risks
Addressing the uncertainty of value and ROI, this chapter suggests conducting additional research or analysis to reduce risk and make informed decisions. It highlights the importance of automating feedback collection for model evaluation and balancing benefits with cost analysis beyond monetary gains.
Chapter 8
Implementing Machine Learning for Sales Forecasting in Fintech
The final chapter discusses the implementation of machine learning for sales forecasting in fintech, starting with simple models like extrapolation and conducting proofs of concept to showcase value. It advocates focusing on solving real needs, prioritizing impactful solutions over trends, and staying grounded in practical problem-solving approaches.
Meet your Mentor
Jack Moore
Product Head @ Rohin Healthcare
Jack Moore is the co-founder of AurilAI, a remote-based tech company since May 2023. He also serves as an Advisor at Vocap Partners, specializing in Product Management & AI. With expertise in AI and ML, Jack aids VOCAP in evaluating potential investments in the tech sector.
Frequently Asked Questions
What are the main pillars of Product Management?
The main pillars of Product Management are strategy, execution, communication, and customer focus.
What is Product Management, and why is it important to learn about?
Product Management is the process of developing and managing a product. It's essential for understanding customer needs and market demands.
Is this Product Management course designed for corporate training and workforce upskilling?
Yes, the Product Management course is designed to help corporate teams enhance their skills and stay competitive.
How long can I access the free product management course content?
You can access the free product management course content for a limited time specified by the course provider.
Will I receive a certification upon completion of the free product management course?
Yes, upon successful completion of the free course, you will receive a certification to showcase your new skills.
Overcoming Challenges in Data Science Product Management
3.5
(262 ratings)
Advanced
27 October 2024 at 11:30 pm GMT
19k Learners enrolled
Mentor
Jack Moore
Product Head @ Rohin Healthcare
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