Data analysis is crucial to accurately predict the performance of an application. The course begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins. Once you are comfortable with the basics, you will read ahead and learn all about data visualization and graphics. You will learn data management techniques such as pivots, aggregations, and dealing with missing values. With this various case studies and examples, this course gives you the knowledge to confidently start your career in the field of data science.
- Use basic programming concepts of R such as loading packages, arithmetic functions, data structures, and flow control
- Import data to R from various formats, such as CSV, Excel, and SQL
- Clean data by handling missing values and standardizing fields
- Perform univariate and bivariate analysis using ggplot2
- Create statistical summary and advanced plots, such as histograms, scatter plots, box plots, and interaction plots
- Apply data management techniques, such as factors, pivots, aggregation, merging, and dealing with missing values, on the example data sets
For an optimal student experience, we recommend the following hardware configuration:
- Processor: i3
- Memory: 2GB RAM
- Hard disk: 10GB
- An Internet connection
You’ll also need the following software installed in advance:
- Operating System: Windows 8 64–bit
- R and RStudio
- Browsers (Google Chrome and Mozilla Firefox - latest versions)