Master Data Science through a
Real - Work based Curriculum
Let's take you on an exciting journey through our Data Science curriculum, divided into
four terms, each carefully crafted to help you grow both academically and professionally
Real Work based projects
Solve real data science problems and case studies across multiple industries .
Learn how Data Product Managers at Netflix leverage user data to personalize recommendations, improving user engagement and retention
Learn how Ola optimize pricing strategies and improve customer experiences by analyzing real-time data on traffic patterns and demand.
Learn how Airbnb make use of data in order to improve search engines and make precise property recommendations
Learn how Google enhances search algorithms and ad targeting, delivering more relevant results to users and advertisers through data-driven insights.
Learn Amazon uses data analytics to identify customer preferences and behavior, enabling targeted marketing campaigns and personalized product suggestions.
Term 1 (2.5 Months)
Super Scholar's Data Science curriculum commences with a strong focus
on building a rock-solid foundation in fundamental Data Science concepts.
MODULE 01 - Data Science 101
- Introduction to data scienc
- A to Z Terminologies in Data Science
- Data Lifecycle and Application
- Basic DS problems for beginners
Term 2 (3.5 Months)
In Term 2, we take a deep dive into the heart of Data Science, guiding you
through hands-on projects and challenging real-world scenarios that will
elevate your practical skills.
MODULE 06: Intro. to Python
- Basics of Programming
- Variables, operators & functions
- Data Structures & Libraries in Python
- Basic coding problems in Python
Term 3 (3.3 Months)
Term 3 is your gateway to advanced Data Science concepts, where we
not only delve into the intricate aspects of the field but also equip you
with interview preparation skills and the art of personal branding.
MODULE 11: DV using Python
- Introduction to Matlplotlib & Seaborn
- Creating histograms and plots
- Dashboards with Plotly & and Dash
- Categorical data in Python
Term 4 (4.3 Months)
In the fourth term, students will be working on a research problem for 3 months
and publishing a white paper while preparing for job interviews simultaneously.
Project Selection Criteria
- Defining project selection criteria (relevance, feasibility, impact)
- Balancing personal interests and career objectives
- Evaluating project complexity and resource requirements
- Considering ethical considerations and societal implications
Get your hands dirty with real data and solve real problems working at
funded, product-based tech companies.