This course is best suited for individuals who have a technical background in mathematics/statistics/computer science/engineering pursuing a career change to jobs or industries that are data-driven such as finance, retain, tech, healthcare, government and many more. The opportunity is endless. This course is part of the Performance Based Admission courses for the Data Science … [Read more...] about Linear Regression
Data Science
Virtualization, Docker, and Kubernetes for Data Engineering
Throughout this course, you'll explore virtualization, containerization, and Kubernetes, mastering the very tools that power data engineering in the industry. Each week presents a new set of tools and platforms that are indispensable in data engineering. From mastering Docker and Kubernetes to exploring advanced topics such as AI-driven coding with GitHub Copilot, efficient … [Read more...] about Virtualization, Docker, and Kubernetes for Data Engineering
Intro to Null Hypothesis Significance Testing with z-test
This is primarily aimed at first- and second-year undergraduates interested in psychology, data analysis, and quantitative research methods along with high school students and professionals with similar interests. This course delves into the foundational concepts of probability and statistics, emphasizing the importance of random sampling and the normal distribution. Students … [Read more...] about Intro to Null Hypothesis Significance Testing with z-test
Intro to Data Analytics, SQL, and EDA Using Python
The ability to understand and work with data has become increasingly important in today's world, where data is ubiquitous and valuable. This course covers a range of topics, including what data is and its different types, what big data looks like, and how companies are using it. It also explores the fields of data analysis and data science and how the two come together. To … [Read more...] about Intro to Data Analytics, SQL, and EDA Using Python
Interpretable Machine Learning Applications: Part 2
By the end of this project, you will be able to develop intepretable machine learning applications explaining individual predictions rather than explaining the behavior of the prediction model as a whole. This will be done via the well known Local Interpretable Model-agnostic Explanations (LIME) as a machine learning interpretation and explanation model. In particular, in … [Read more...] about Interpretable Machine Learning Applications: Part 2
