Introduction to Data Science and Analytics using R
What you'll learn
- Basics of statistical modelling
- Basics of data science using R and Python
- Forecasting and prediction using Data
- Data Visualisation
- No programming experience needed
Are you interested in the field of Data Science and Machine Learning but haven't had experience in it? Then this course is for you!
This course has been designed by a professional Data Scientist so that I can share my knowledge and industry experience and help you learn the basics of data science algorithms and coding libraries.
This course includes a step-by-step approach to Data Science and Machine Learning. With each lecture, you will develop the mathematical understanding as well as the understanding of necessary libraries to help you ace Data Science interviews and enter into this field.
The course is structured in a very crisp and comprehensive manner to help you understand industry-relevant algorithms. It is structured the following way:
Part 1.) Getting started with R
Setting up R
Getting Started with R Studios IDE
Part 2.) Introduction to Statistical Measures
Measures of Central Tendencies
Introduction to Data Science using R
Part 3.) Data Processing and Data Visualisation in R
Measures of Dispersions and Outlier Treatment
Missing Value Treatment using R
Data Visualization using R ( boxplots, bubble plots, heat plots, automated-EDA in R)
Part 4.) Building Regression Models in R
Linear Regression Theory
Linear Regression using R
Multivariate Linear Regression Theory
Multivariate Linear Regression using R (Multiple Linear Regression, R-square, Adjusted R-square, p-value, backward selection)
Part 5.) Building Classification Models in R
Classification using Logistic Regression
Logistic Regression and Generalized Linear Models in R & Measures of Accuracy for a Classification Models (AIC, AUC, Confusion Matrix, Precision, and Recall)
Part 6.) Random Forest Models in R
Introduction to decision tree classifier (trees package, Gini index, and tree pruning )
Creating decision tree and Random Forest in R (Random forest package in R, hyper-parameters tuning, visualizing a tree in R)
Building Random Forest Regressors
The course takes you through practical exercises that are based on real-life datasets to help you build models hands-on.
And as additional material, this course includes R code templates which you can download and re-use on your own projects.
Who this course is for:
- Engineering students
- Beginner python and R data analysts
- Data science enthusiasts
- Business graduates
My name is Diganto and I am a post-grad data scientist! ( Data Scientist at Accenture Strategy, Ex-Mu Sigma, IIT Kharagpur, CAT 2018 - 98.36 percentile, GATE AIR-184).
I am a Kaggle expert Tier data scientist with a rank of 1134 and I am within the top 0.7 percentile of data scientists in Kaggle. I have close to 3 years of experience in Data Science and Analytics and I have developed various commodity price prediction models and have extensively worked with time series algorithms, Linear, Logistic Regression Modelling, Classification, and Regression Trees (CART) as well as with unsupervised learning algorithms such as Clustering Algorithms and purchase propensity models using Python, R, and R Shiny.
I have trained more than 300 students in data science using R and Python.
I look forward to interacting with you all during the course.