Data Science

Data Science with R Programming

We introduce R Programming for both beginners and professionals. In this course, you will get the basic and advanced concepts of data analysis and visualization using R Programming.

In this course, you will learn, how you can program in R and how you can use R for effective data analysis. This course covers the installation and configuration of the required software in order to analyze complex and real-time data.

During this course, you will get an opportunity to understand the practical issues in statistical computing that covers programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code.

You will learn all the topics of R such as introduction, features, installation, Rstudio ide, variables, data types, operators, if statement, vector, data handling, graphics, statistical modeling, etc.

Python AI

Data Analysis with Python

In this course, you will learn how to analyze data using Python. During this course, you will learn starting from the basics of Python to exploring different types of data. You will also learn, how to load data from different sources such as CSVs.

You will also learn how to prepare data for analysis, perform statistical data analysis, and create meaningful data visualization. You will learn libraries such as Numpy, Pandas, Matplotlib, and Seaborn to process and visualize data.

We will start with the installation of Python using Anaconda distribution. You will learn how to use Spyder, iPython, and Jupyter notebook.

We will cover the fundamentals of Python that are critical for data analysis and machine learning projects. You will learn NumPy which includes multidimensional arrays with broadcasting capabilities, mathematical functions, linear algebra functionalities, and tools to read and write array data to disk.

You will also learn pandas library that includes series, DataFrame, Indexing, summarizing, and computing descriptive statistics.