Statistics for Data Science and Business Analysis
Mastering Statistics Fundamentals using R Programming
Description
Welcome to our comprehensive course on statistical analysis! This course is designed to equip you with the essential skills and knowledge needed to excel in statistical analysis, whether you're a beginner or seeking to enhance your expertise.
Through a series of engaging modules, we'll guide you through the fundamentals of statistics using the powerful R programming language. From understanding the basics of R programming to exploring descriptive and inferential statistics, data types, visualization techniques, and more, this course covers a wide range of topics essential for effective statistical analysis in various fields.
Each module is carefully crafted with practical examples and explanations, ensuring you grasp each concept thoroughly. By the end of the course, you'll have the confidence and skills to apply statistical analysis techniques in real-world scenarios, making better-informed decisions and driving impactful outcomes.
**Key concepts taught in the course are:**
1. R Programming
2. Subjects in the Population
3. Statistical Questions
4. Types of Data
5. Descriptive and Inferential Statistics
6. Distribution of a Variable and Frequency Table
7. Visualizing Distribution with Graphs
8. Shape of Distribution
9. Center of Quantitative Data
10. Measuring Variability of Quantitative Data
11. Empirical Rule
12. Percentiles and Quartiles
13. Relationship Between Variables
Congratulations on taking the first step towards mastering statistical analysis! Dive in and let's embark on this exciting journey together.
What You Will Learn!
- Learn the essentials of R programming, including installation, setup, and exploring datasets for effective data analysis.
- Understand the concept of subjects within a population and their relevance in statistical analysis.
- Explore five types of statistical questions and their applications in summarizing, comparing, and predicting data.
- Differentiate between categorical and quantitative data and understand their significance in statistical analysis.
- Explore variable distribution and frequency tables to gain insights into data patterns.
- Gain insights into both descriptive and inferential statistics and their usage in analyzing sample and population data.
- Learn to visualize categorical and quantitative data distributions using various graphical representations.
- Understand the different shapes of distributions for quantitative variables and their implications.
- Learn methods to describe the center of quantitative data, including mean, median, and mode.
- Explore measures of variability, including range and standard deviation, to understand data spread.
- Gain insights into the empirical rule for understanding data distribution and identifying outliers.
- Understand percentiles and quartiles and their significance in summarizing data variability.
- Explore the relationship between different variables, including categorical and quantitative variables, and understand correlation analysis.
- Learn predictive analysis techniques to make informed predictions based on data patterns and trends.
Who Should Attend!
- This course is suitable for anyone interested in learning statistical analysis techniques using R programming. Whether you're a beginner looking to acquire new skills or someone already familiar with statistical concepts seeking to deepen your knowledge, this course provides valuable insights and practical guidance.