Your Introduction to Data Science is tuition-free
Learn data science by doing data science
Start your studies in data science and analytics
Prepare for a future in data science
Across both units in the module, students gain a comprehensive introduction to scientific computing, Python, and the related tools data scientists use to succeed in their work. Students will develop machine learning and statistical analysis skills through hands-on practice with open-ended investigations of real-world data.
All students receive complimentary access to a ready-to-use Python environment for the entire module. This allows students to gain first-hand experience with Python, pandas, and Jupyter Notebooks, and allows for immediate immersion into novel data science problems.
The Applied Data Science module is built by Worldquant University’s partner, The Data Incubator, a fellowship program that trains data scientists. Graduates earn a certificate upon completion of each unit to share and celebrate their professional development.
Students only need to fill out a short profile on their educational history and technical skillset to apply. The application takes less than 20 minutes to complete. Apply Now
The Module
The Applied Data Science module is delivered entirely online so students can participate in continuing education without disrupting their lives.
Students who successfully complete Unit I earn a certificate and an invitation to enroll in Unit II. Those who successfully complete Unit II earn an overall certificate. Students have the opportunity to complete either unit of the module with honors.
Across two units and sixteen weeks, students learn to source data relevant to a business problem or task, to summarize data in aggregate statistics and visualizations, and to model trends to showcase insights and make practical business decisions.
Scientific Computing and Python for Data Science
In Unit I, students gain a comprehensive introduction to scientific computing, Python, and the related tools data scientists use to succeed in their work. Successful completion of Unit I is a required prerequisite for enrollment in Unit II.
- Program flow, data structures
- Data structures, algorithms, classes
- Data formats
- Multi-dimensional arrays and vectorization in NumPy
- DataFrame, Series, data ingestion and transformation with pandas
- Data aggregation in pandas
- SQL and Object-Relational Mapping
- Data munging
Machine Learning and Statistical Analysis
In Unit II, students develop machine learning and statistical analysis skills through hands-on practice with open-ended investigations of real-world data. Students can expect to work with authentic public data sets from organizations like the NHS, or anonymized data on credit card defaults. This unit has a heavy emphasis on creative use of the tools of data science to solve problems from multiple perspectives.
- Introduction to Machine Learning
- Regression and classification
- Metrics and overfitting
- Model selection
- Principal Component Analysis and Dimensionality Reduction, feature engineering
- Statistical methods and nonparametric analysis, probability distributions
- Ensemble methods
- Support Vector Machine and Natural Language Processing
How does the module work?
The Requirements
This is a true introduction to data science and can accomodate beginners with the right amount of foundational knowledge. This professional development opportunity does not require you to have any prior degrees.
To qualify for the course you must have experience with algebraic concepts such as functions and variables, which are frequently used in programming.
Preference is given to applicants with familiarity in programming fundamentals, basic statistics, linear algebra, and calculus.
Students should expect to commit roughly 8 to 10 hours per week between lecture videos, assignments and individual study.
Let’s go!
The module runs four times each calendar year. We strongly encourage all qualified candidates to apply soon, as space in each class is limited and initial interest is high.
2020 Start Dates
| Start Date | Appl. Deadline |
|---|---|
| January 13Jan 13 | December 23Dec 23 |
| April 13Apr 13 | March 23Mar 23 |
| July 6Jul 6 | June 15Jun 15 |
| September 28Sep 28 | September 7Sep 7 |