What you will learn in Data Science Specialization Course
Gain proficiency in R programming for data analysis and visualization.
Understand the entire data science process, from data acquisition to modeling and interpretation.
Develop skills in statistical inference and machine learning techniques.
Learn to manage and manipulate data using databases and tools like GitHub.
Create reproducible research reports and presentations.
Program Overview
The Data Scientist’s Toolbox
⏱️ 4 weeks
Introduction to data science and the roles of data scientists.
Overview of tools such as version control, markdown, Git, GitHub, R, and RStudio.
R Programming
⏱️ 4 weeks
- Fundamentals of R syntax and programming concepts.
- Data types, control structures, functions, and debugging in R.
Getting and Cleaning Data
⏱️4 weeks
- Techniques for obtaining data from various sources.
- Data cleaning and preprocessing methods to ensure data quality.
Exploratory Data Analysis
⏱️ 4 weeks
- Visualization techniques to summarize and understand data.
- Application of statistical methods to explore data patterns.
Reproducible Research
⏱️ 4 weeks
Principles and practices for creating reproducible research.
Use of R Markdown and knitr for documentation.
Statistical Inference
⏱️ 4 weeks
Concepts of statistical inference and hypothesis testing.
Application of resampling methods and confidence intervals.
Regression Models
⏱️ 4 weeks
Linear regression techniques and model building.
Interpretation of regression coefficients and diagnostics.
Practical Machine Learning
⏱️ 4 weeks
- Introduction to machine learning algorithms and their applications.
- Model training, validation, and performance evaluation.
Developing Data Products
⏱️ 4 weeks
- Creation of interactive data products using Shiny, R packages, and APIs.
- Deployment of data products for end-user interaction.
Data Science Capstone
⏱️ 6 weeks
Application of acquired skills to a real-world project.
Development and presentation of a data product addressing a specific problem.
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Job Outlook
- Growing demand for data science professionals across various industries.
- Skills acquired are applicable to roles such as Data Analyst, Data Scientist, and Business Analyst.
- Proficiency in R and data analysis techniques enhances employability.
- Experience with real-world projects through the capstone increases job readiness.
Specification: Data Science Specialization
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