Manisha Arora

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Unlock your potential with our self-assessment tool! Answer a few questions to uncover your strengths, pinpoint areas for improvement, and benchmark yourself against peers. Receive personalized resources to help you up-level yourself as a data scientist.

Assess Your Data Science Skills

Master advanced A/B testing to drive data-informed product decisions and measurable growth.

Data Science Lead, Google

Data Science Lead, Google | Founder, PrepVector | MIT, UT Austin & UCinc

Data Science Manager, Walmart

Banani Mohapatra

Senior Manager, AI/ML & Data Science at Walmart | Generative AI,LLM | IIT Delhi

 Advanced AB Testing for Data Scientists

Senior Business analyst 

Associate Data Scientist

Senior data analyst

Sr Data Scientist

Learn to design A/B tests that drive real product decisions - from metrics and hypotheses to business impact.

AB Testing for Data Scientists & Product Managers

Data Analyst

Product Manager

Data Scientist

Web Analytics and Experimentation Analyst 

This weekend workshop is a hands-on introduction to A/B Testing for data scientists and product managers. To drive real impact, you need to connect experiment results to business decisions, partner effectively with product teams, and communicate insights that shape roadmaps. This workshop helps mid-senior data professionals strengthen those skills, sharpen product thinking, and elevate how they approach experimentation.Across fast-paced case studies, group discussions, and live walkthroughs, you’ll learn to:<ol><li>Frame hypotheses that lead to clear decisions</li><li>Pick metrics that reflect real business trade-offs</li><li>Interpret results beyond statistical significance</li><li>Communicate insights to PMs, leaders, and cross-functional teams</li></ol>By the end, you won’t just run A/B tests — you’ll make better product decisions with them. We’ll close with what separates good data scientists from great ones and how to keep growing your skills.

Learn to Design and Analyze A/B Tests that Influence Product Strategy.

Master high-signal A/B test design

Build confidence in interpreting experimental results.

Apply industry case studies to real product challenges

Communicate experiment outcomes through clear decision briefs

Use a reusable A/B testing framework to evaluate and improve experiments

Level up experimentation judgment to operate like a senior data scientist.

No advanced math or statistics required. Just curiosity, a willingness to learn, and an interest in how product decisions are made.

Basic Comfort with Data and Curiosity

Data scientists who want to build A/B Testing skills to share business outcomes and get interview-ready.

Growth analysts looking to design high-impact experiments and optimize key funnel metrics.

Product Managers who work closely with data teams and want to better frame, interpret and act on experiment results.

This weekend-intensive workshop is built for data scientists who want to go beyond just “running experiments” — and start shaping product strategy through evidence-based decision-making. Over two immersive days, you’ll gain hands-on experience in designing robust experiments, analyzing results with rigor, and translating findings into measurable business impact. Through live lectures, breakout discussions, and case-based exercises, you’ll learn not just how to run experiments, but why each design choice matters — from hypothesis framing to metric selection and trade-offs. This program is fast-paced and practical, tailored for mid-level professionals who want to level up their experimentation craft and drive product outcomes with clarity and confidence.

Learn to Design, Analyze, and Scale A/B Tests that Drive Growth

Design Rigorous Experiments

Detect and Handle Complex Pitfalls

Statistical Methods for Design & Analysis

Advanced Experimental Methods

Translate Experiment Results into Actionable Decisions

You should be familiar with the basics of experiment design. Check out AB Testing 101 course here

Basic Understanding of AB Testing

Data &amp; Applied Scientists who are looking to design stronger experiments and influence product decisions.

Data Analysts who want to level up from dashboards to designing and interpreting high-impact experiments.

CRO Experts who are optimization professionals seeking deeper statistical rigor and better test design.

If you want to succeed as a Data Scientist in tech, proficiency in AI / ML concepts is just the beginning. To truly thrive, you must implement end-to-end projects, integrate business acumen, and effectively collaborate with stakeholders. This advanced course is designed to equip mid-senior career professionals to drive impact while building a portfolio of applied AI &amp; ML projects. This course offers a dynamic blend of technical expertise and real-world business challenges. Through a series of interactive sessions, discussions, and hands-on projects, you will learn how to: 1) Scope machine learning projects effectively2) Lead discussions with stakeholders to align on project objectives and get buy-in3) Navigate the entire data science workflow from data exploration to model deployment4) Communicate project insights and business impact to stakeholders Course Curriculum:Week 1: Scoping ML Projects, Stakeholder Buy-In Strategies, and Data Science Workflow on GitWeek 2: Data Cleaning, Feature Engineering, ML algorithms, Deployment on StreamlitWeek 3: Agentic AI and Frameworks, Ensemble Models, Forecasting Methods, ML Tradeoffs, Actionable Insights, Coding Best PracticesWeek 4: Model Deployment on Cloud, Coding Best Practices, Github Portfolio ShowcaseWeek 5: Project Discussion, Set up Portfolio, Build your Website, Showcase your Work Pre-requisites:1. Familiarity with R / Python programming language2. Knowledge of data manipulation using Pandas3. Understanding of machine learning fundamentals is good-to-have4. Learning curiosity 🙂 Time-commitment:8-10 hours per week Class Format:Each 2-hour session will feature 1.5 hours of content-rich instruction followed by 30 minutes of open discussion and Q&amp;A. Prior to each class, you will be expected to engage in pre-readings, hands-on exercises, and GitHub submissions. During sessions, we'll explore various problem-solving techniques, address nuances and trade-offs, and derive actionable insights to drive business objectives forward. Bonus Features:In addition to course content, you will have the opportunity to showcase your best projects weekly on the PrepVector Newsletter and LinkedIn page. Outstanding projects will also receive special recognition on LinkedIn by me!

Become a Data Science Expert by Implementing End-to-End AI/ML Projects

Course syllabus

We will meet every Sunday to discuss the weekly updates and review codes. You will get to work together with other learners in the course and learn from them.

Live Sessions: Sundays

Optional office hours once a week to answer any questions as you digest the content and work on your project.

[Optional] Office Hours: Thursdays

Take time to work on the project as per the instructions provided for each week. These projects will be discussed during our live sessions.

Weekly projects

Course schedule

Data scientists who want to build a compelling portfolio of industry projects to showcase their skills to potential employers.

Software and data engineers eager to gain expertise in applications of machine learning methodologies to enhance their technical repertoire.

Data and BI analysts seeking to acquire hands-on experience in leveraging data-driven insights to solve industry challenges.

Who is this course for

What you’ll get out of this course

Gain hands-on experience in applying machine learning methodologies to real-world industry scenarios. Learn how to process data effectively, select the right algorithms, and implement models for accuracy and efficiency.

Master Practical Applications of Machine Learning Methodologies

Develop a structured approach to navigating complexities of scoping ML projects, understanding business problems, gathering requirements, implementing projects, and deploying them on Cloud.

Implement End-to-End Data Science Projects & Deploy on Cloud

Construct a portfolio of industry projects using engineering best practices, that showcase your ability to develop projects through organized workflows and coding best practices on GitHub.

Build a Compelling Portfolio of Industry Projects using Organized Workflows on GitHub

Extract meaningful insights from data and translate them into actionable recommendations for decision-makers. Explore techniques for visualizing and communicating data-driven insights, enabling informed decision-making and driving business growth.

Drive Actionable Insights for Informed Decision-Making

Collaborate effectively with diverse stakeholders, including technical and non-technical members. Hone your communication skills to frame narratives, convey complex technical concepts in a compelling and impactful manner, fostering collaboration and alignment.

Enhance Cross-Functional Collaboration and Communication

Gain hands-on experience and build a portfolio of industry AI/ML projects. Scope & execute the workflow from data exploration to deployment.

Data Science Lead, Google | Founder, PrepVector | 
MIT, UT Austin & Univ of Cincinnati

Siddarth Ranganathan

Director of Data Science, Microsoft | Founder, PrepVector | USC Marshall

Cracking A/B Testing Interviews

Siddarth R

Director of Data Science, Microsoft

Senior Manager of Data Science, Walmart

Learn the key concepts that interviewers are evaluating you on during AB Testing Interviews.

How Interviewers Evaluate AB Testing Answers

A framework to confidently design AB Tests and move from business objectives to metrics, design, and decisions.

A clear Framework to Structure Any AB Testing Question

Learn how strong candidates talk about metric tradeoffs, underpowered experiments, and inconclusive results.

Handle Edge Cases & Tradeoffs

Automate Insight Generation Using AI Agents

Principal Data Science Manager, Microsoft

Learn how to assign roles to AI Agents and accomplish insight automated business insight generation

Build Insight-Driven Agents with OpenAI & CrewAI

Offer a concrete and concise explanation of how you will
help students understand and apply this lesson.


Deploy Agentic AI workflows in Streamlit

Learn implementation challenges and how to design an Agentic AI system

Design Considerations for an Agentic AI System

Finetuning LLM for Language Translation

Data Science Lead, Google & Founder, PrepVector

Director of Data Science, Microsoft & Mentor, PrepVector

Learn the step-by-step process of customizing LLM to handle specialized use cases in language translation

Fine-Tuning OpenAI's LLM

Discover how to apply techniques like LoRA and PEFT to reduce training costs & speed up development

Best Practices for Efficient and Cost-Effective Fine-Tuning

Understand what makes a dataset effective for translation fine-tuning and explore the most reliable evaluation metrics

How to Build High-Quality Datasets and Evaluate Translation

From Raw Data to Automated Insights with AI Agents

Learn how to create fast, task-specific AI agents using GroqCloud’s ultra-low-latency LLMs and CrewAI’s orchestration

Build Insight-Driven Agents with GroqCloud & CrewAI

How to build a user-friendly interface with Streamlit that allows data uploads, custom queries, and insight downloads

Deploy Seamless Workflows with Streamlit

Explore how agentic automation turns manual analysis into a repeatable process. Learn how AI can scale EDA and reporting

Automate and Scale Insight Generation

Notebook to Production with GitHub

Data Science Lead at Google

Structure projects with modular code, clean hierarchies, and version control for scalability and reproducibility.

Organize Workflows for Reproducibility and Scalability

Convert messy notebook code into reusable scripts and pipelines for ingestion, training, and deployment.

Transition from Jupyter Notebooks to Production Code

Use GitHub branching, pull requests, and automations to streamline workflows and enhance team collaboration.

Collaborate with your Team via GitHub

Build and Deploy Projects with Streamlit Cloud

Principal Data Science Manager at Microsoft

Learn the essential tips and techniques for seamless and effective cloud deployment.

Best Practices and Setting up

Master the process of turning your models into fully functional, cloud-hosted applications.

Deploy End-to-End Data Science Projects

Understand how to share your deployed projects and build a professional portfolio that stands out.

Learn directly from Manisha Arora

Manisha Arora

About Manisha

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