This specialization provides a comprehensive introduction to data science concepts and techniques, covering a wide range of topics from foundational R programming to advanced machine learning and data product development.
Course Breakdown:
- 1. The Data Scientist's Toolbox: Introduces fundamental R programming concepts, data structures, and basic plotting techniques. (17 hours, 4.6 rating)
- 2. R Programming: Delves deeper into R programming, covering advanced data structures, control flow, and functions. (57 hours, 4.5 rating)
- 3. Getting and Cleaning Data: Focuses on data acquisition, cleaning, and transformation techniques, including handling missing values, merging datasets, and data tidying. (19 hours, 4.5 rating)
- 4. Exploratory Data Analysis: Explores data visualization and summary methods for understanding and summarizing key characteristics of datasets. (54 hours, 4.7 rating)
- 5. Reproducible Research: Emphasizes the importance of reproducible research practices, including version control, documentation, and sharing of data and code. (7 hours, 4.6 rating)
- 6. Statistical Inference: Covers statistical inference concepts, including hypothesis testing, confidence intervals, and statistical modeling. (54 hours, 4.2 rating)
- 7. Regression Models: Focuses on linear regression, multiple regression, and other regression models for predicting outcomes. (53 hours, 4.4 rating)
- 8. Practical Machine Learning: Introduces machine learning algorithms, such as classification, clustering, and decision trees, and their implementation in R. (8 hours, 4.5 rating)
- 9. Developing Data Products: Explores the process of developing and deploying data products, including Shiny applications and interactive dashboards. (10 hours, 4.6 rating)
- 10. Data Science Capstone: A culminating project where you apply the skills and knowledge gained throughout the specialization to a real-world data science challenge.
Repository Structure:
- Each course within the specialization has its own dedicated folder.
- Within each course folder, you'll find folders for the individual programming assignments.
- Assignment folders typically include: R scripts containing the assignment code.
Course - Data Science Specialization