Improving Movie Recommendations for Netflix

Cohort Start Date: 5th February 2025

Background

Netflix, the world’s leading streaming entertainment service, caters to over 238 million paid memberships across more than 190 countries. The platform offers a wide variety of TV series, documentaries, and feature films across multiple genres and languages. One of the core reasons behind Netflix’s global success is its ability to provide personalized content recommendations, using cutting-edge data science techniques to keep users engaged.

Personalized recommendations drive user satisfaction, helping viewers discover new content they would enjoy, leading to longer watch times and higher customer retention. The recommendation system is a critical component of Netflix’s business model, and continuous improvement of this system remains a top priority.

The Challenge

Despite Netflix’s existing recommendation engine being sophisticated, there are challenges in ensuring that the algorithm delivers truly accurate recommendations that are in line with a user’s evolving preferences. Viewers often complain about the platform recommending content that is irrelevant, not in tune with their tastes, or too similar to what they’ve already watched. This challenge can be attributed to a few factors: Data overload, changing user preferences, no previous viewing history etc.

As a team of data science students, your task is to help Netflix improve its recommendation engine by solving these key challenges. You will work with a dataset (potentially hypothetical or simulated) containing user interaction data, movie metadata, and ratings.

Reserve your spot

Week 1

By the end of this week, students will understand what data science is, its role in business, and how it helps solve business & research problems.
They will define the problem that they will work on.
Reflect on real world examples

Discussion Topics:

What kinds of data do you think Netflix collects to make recommendations?
What are some of the challenges in analyzing such large datasets?
How do you think data science impacts your everyday life (e.g., social media, shopping, or school)?
Can you think of ways data science might affect everyday life? For example, how could a company use your social media data?

Assignment:

Research and download a publicly available Netflix dataset (e.g., IMDb ratings, genres, or content popularity). Create a brief report on the dataset you’ve chosen and explain why it’s interesting.

Alternate Assignment:

Research and write a 200-word presentation on a company (other than Netflix) that uses data science to make decisions. How does data improve their business?

Week 2

Understand different types of data, how to work with datasets, and data collection methods.
Learn how to clean and prepare the dataset for analysis, focusing on handling missing data, outliers, and formatting the data properly for analysis.

Discussion Topics:

Why do you think companies like Netflix and Amazon are so focused on data science? How does it benefit them?
Why is data cleaning important, and what are the most common issues when preparing real-world datasets?
How can we make sure the data accurately reflects the trends we want to study?

Assignment:

Take the Netflix dataset and identify any issues (missing values, duplicates). Clean the data. Document the steps you took to clean the dataset and explain why each step was necessary.

Alternate Assignment:

Download a sample Netflix dataset and explore it. Identify the types of data included. Write a short essay on data ethics and privacy, particularly in streaming services like Netflix.

Week 3

How to explore and analyze data, search for patterns or trends to improve Netflix’s recommendation system.
Emphasis on Exploratory Data Analysis (EDA) techniques ( such as Summary Statistics, Data Visualization, Correlation)

Discussion Topics:

How would Netflix use user data to recommend movies?
Why do you think data visualization (like charts and graphs) is important for data analysis?
What insights can be drawn from analyzing trends such as genre popularity or user ratings?
How do seasonal patterns (e.g., more viewership during holidays) affect Netflix’s strategy?

Assignment:

Analyze the cleaned Netflix dataset. Create visualizations showing the most popular genres, ratings, or trends over time.
Write a short summary of any trends or patterns you identified in the dataset.

Alternate Assignment:

Perform a trend analysis on the Netflix dataset. Create visualizations to showcase key insights, such as popular genres over time or trends in user ratings.

Week 4

Learn why is Data Visualization Important, understand common Visualisation Tools, choosing right chart or graph for communicating results etc
Storytelling with data and how to prepare a report that communicates their insights effectively.

Discussion Topics:

Why is it important to visualize data instead of just showing numbers?
How would you present your findings to someone who doesn’t know much about data science?
Why do you think data visualization (like charts and graphs) is important for data analysis?

Assignment:

Create a data dashboard or report summarizing your analysis. Include graphs, charts, and key insights that Netflix could use to improve recommendations.

Alternate Assignment:

Create at least two visualizations (e.g., genre distribution, most-watched content). Include the visualizations in your project work and a brief description of what each reveals.

Week 5

Learn the basics of predictive modeling and how it can be used to forecast future trends.
Key Concepts such as Simple Linear Regression, Classification, Clustering, building & testing a model
Introduction to Machine Learning

Discussion Topics:

What is predictive modeling, and how is it used to anticipate future behavior on platforms like Netflix?
What do you think makes Netflix’s recommendations accurate?
How could machine learning be used in other industries?
How can you measure the accuracy of your model?

Assignment:

Create a predictive model using linear regression or other simple methods to forecast future genre or content popularity. Using the model, explain your results.

Alternate Assignment:

Build a simple predictive model using the Netflix dataset (e.g., linear regression to predict future ratings or trends). Test your model’s accuracy and document your process in a short report.

Week 6

Presenting to Chief Data Officer: Tips on how to deliver a presentation to a business leader and respond to feedback
Learn Industry Best Practices
Students will present their completed projects, showing how they’ve used data science techniques to solve a real-world problem for Netflix.

Discussion Topics:

How do you feel your analysis would help Netflix?
What challenges did you face in the data science process, and how did you overcome them?

Assignment:

Prepare and deliver a final project presentation (10 mins long) that includes:

Problem definition.
Data analysis and visualization.
Final recommendations for improving Netflix’s recommendation system

Skills you develop in this project:

Data Analysis and Interpretation: Data scientists must possess strong analytical skills to extract meaningful insights from complex datasets. This involves gathering, cleaning, and processing raw data, then applying statistical and machine learning techniques to uncover patterns and trends. Students will develop the ability to interpret these findings and use them to inform business decisions or solve real-world problems.

Technical Skills in Data Tools: In this project, students will learn to work with user-friendly tools like Tableau or MATLAB to visualize and analyze data. They will develop the ability to create meaningful charts, dashboards, and reports, making it easier to interpret data trends and communicate insights without needing to write code. These tools help students turn raw data into clear, actionable information for decision-making.

Critical Thinking and Problem Solving: Data scientists need strong problem-solving skills to design data-driven solutions to complex issues. This involves breaking down ambiguous problems into manageable components, selecting the appropriate algorithms or models, and continuously refining solutions based on feedback and performance metrics. Students will also learn to evaluate trade-offs between accuracy, speed, and complexity, ensuring their solutions are practical and scalable.

Benefits of membership (included with project):

Be Part of an Exclusive Community: Connect with like-minded students and build your network with future data leaders, industry mentors, and alumni from top data-driven companies. Start creating connections that will fuel your career early on.
Hands-On Industry Experience: Get a taste of what it’s like to work in top tech and data-driven companies. Learn directly from professionals who have worked at Google, Amazon, Microsoft, and more. Gain the real-world insights that will put you ahead.
Exclusive Discounts: Access discounted rates for top-tier courses in data science, coding, and interview preparation from our trusted partner companies. Get the skills and guidance you need to ace your future internships and job interviews.
Launch Your Future in Data: Whether you’re interested in AI, machine learning, or data analytics, the Lyghthouz Data Pioneers Club offers you the platform to explore, learn, and grow in the world of data science. This is your launchpad to a high-growth career!

Interesting Facts

High Demand: Data scientists are in demand across industries, solving critical problems with data-driven insights and helping businesses make better decisions.
Skill Development: Builds expertise in data analysis, machine learning, programming, and storytelling with data. Data scientists work in industries like finance, healthcare, retail, tech, and more.
Top Companies: Major employers include Google, Facebook, Amazon, Microsoft, and specialized firms like Palantir, Snowflake, and Databricks.
Competitive Pay: Entry-level salaries in data science range from €50,000-€75,000 in Europe and $90,000+ in the U.S. With experience, salaries can surpass six figures.
Global Mobility: Data science roles offer opportunities to work globally, especially with remote work options and projects across borders.
Career Path: A stepping stone to leadership roles in AI, machine learning, and business strategy or entrepreneurship in the tech and analytics sectors.
Data science offers rapid career growth, exciting challenges across industries, and significant earning potential for those passionate about technology and analytics.

Any doubt? Contact us

Lyghthouz Europe

Improving Movie Recommendations for Netflix