Data Science Fundamentals With R, Python, and Open Data
Unlock the world of data science with this comprehensive data science fundamentals course using R, Python, and open data. Whether you’re new to the field or looking to strengthen your skills, this course will teach you the essential tools and techniques to analyze and visualize data, perform statistical analysis, and leverage machine learning to gain insights from real-world datasets.
What You’ll Learn
- Fundamental data science concepts and methodologies
- Data cleaning and preprocessing techniques in R and Python
- Exploratory data analysis (EDA) using both programming languages
- Data visualization using libraries like Matplotlib, Seaborn, and ggplot2
- Introduction to machine learning models and algorithms
- Handling and analyzing open datasets from various sources
- Statistical analysis and hypothesis testing
- Building reproducible data science workflows with R and Python
Requirements
- Basic knowledge of programming concepts
- Familiarity with mathematics and statistics (helpful but not required)
- Interest in data analysis, statistics, and machine learning
Course Description
This data science fundamentals course introduces you to the core principles and tools used in data science. You’ll learn how to manipulate and analyze data using R and Python, two of the most powerful languages in the data science ecosystem. Through hands-on exercises, you’ll gain practical experience with open datasets and develop a strong foundation in data analysis and visualization techniques.
The course will guide you through the process of cleaning and preparing data, performing exploratory data analysis, and visualizing the results. You’ll also learn how to apply machine learning algorithms to real-world data, evaluate model performance, and build reproducible workflows for future projects. By the end of this course, you will be well-equipped to handle diverse data science tasks, from data wrangling to modeling and interpretation.
About the Instructor
This course is taught by experienced data scientists with expertise in R, Python, and statistical analysis. The instructors have years of experience working with diverse datasets and applying machine learning techniques in a variety of domains.
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