Project 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.

Future Leader's 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.

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.

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Domain	Data Science & Analytics (+AI)
Sub Domain	Data Science
Program Type	Industry Cohort Program
Level	Intermediate
Time commitment:	Project Duration: Duration: 6 weeks Expected workload: 3 hours/week > Weekly meetings with your Cohort Leader > Independent research and analysis. > Collaborative tasks, if applicable.
What you receive:	See full benefits: https://lyghthouz.com/team-internship-program/

Detailed Curriculum

Click here to download the detailed curriculum for this cohort program.

Data LYTClub

Data science offers rapid career growth, exciting challenges across industries, and significant earning potential for those passionate about technology and analytics.

Benefits of LYTClub Membership:

1) 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.

2) 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.

3) 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.

4) 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.

Sign up to receive a complimentary handbook about 'Career in Data' compiled by our Data Leaders from Google, Nike & Adidas.

Industry 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.

Lyghthouz Europe

Help Netflix Improve Movie Recommendations

Cohort Project under Data Science & Analytics