Elements
Explore our Elements, representing skills and knowledge in data science and data literacy
Data Science
Elements for Data Scientists and Data Analysts cover a comprehensive skill set including Machine Learning, Statistics, CS and Generative AI
Dsa
Learn MoreData Structures and Algorithms
Computer Science
A data structure is a physical form of data type used to store a set of elements logically represented by abstract data types.
Deep Learning
Machine Learning
The primary success of Deep Learning is end-to-end learning in multiple scenarios including unstructured complex data such as pictures, speech and text.
Ds
Learn MoreDescriptive Statistics
Statistics
Applied Statistics is a critical component of the vast multi-dsiciplinary skills and knowledge set for data science.
Linear Algebra
Mathematics
Linear algebra is the branch of mathematics concerning linear transformations over vector spaces, and it is used in science and engineering to model natural phenomena.
Python
Python and Data Science
Ease of learning, a massive and growing community of users, an eco-system of libraries, the ease of binding with C, C++ make Python APIs the primary means of access to these libraries.
Sql
Learn MoreSQL
Databases and BI
SQL is a declarative programming language for managing and processing data held in relational database management systems (RDBMS).
Data Literacy
Data Literacy starts with the basic qualitative and ChatGPT skills to introductory technical skills, in a spectrum from super basics up to data analysts
Critical Thinking with Data
Data Citizen
Gpt
Learn MoreData Science with ChatGPT
ChatGPT
Ability to prompt ChatGPT properly to assist in answering multi-step data science questions. Using ChatGPT to write and debug Python and SQL codes. Simulates real-life use of ChatGPT by data analysts and scientists.
Dsp
Learn MoreData Security and Privacy
Data Citizen
Data Visualization
Data Citizen
Descriptive Statistics
Data Citizen+
Spreadsheets
Data Citizen