Introduction To Data Science

Offered byUDEMY

Introduction To Data Science
at
UDEMY
Overview

Use the R Programming Language to execute data science projects and become a data scientist

Duration	6 hours
Total fee	₹1,299
Mode of learning	Online
Credential	Certificate

Introduction To Data Science
at
UDEMY
Highlights

Earn a certificate of completion from Udemy
Get full lifetime access of the course material
Comes with 30 days money back guarantee

Introduction To Data Science
at
UDEMY
Course details

Skills you will learn

Data Modeling R Statistics Data Science Predictive modeling

Who should do this course?

For analytically minded students who are looking for an introduction to applied predictive modeling methods

What are the course deliverables?

Start and execute the steps of a data science project, from project definition to model evaluation
Use machine learning techniques to build effective predictive models
Learn how to find and correct common problems found in real world data

More about this course

The R language provides a way to tackle day-to-day data science tasks, and this course will teach you how to apply the R programming language and useful statistical techniques to everyday business situations
With this course, you'll be able to use the visualizations, statistical models, and data manipulation tools that modern data scientists rely upon daily to recognize trends and suggest courses of action
This course is designed for those who are analytically minded and are familiar with basic statistics and programming or scripting
You'll learn applied predictive modeling methods, as well as how to explore and visualize data, how to use and understand common machine learning algorithms in R, and how to relate machine learning methods to business problems
This course begins with a walk-through of a template data science project before diving into the R statistical programming language
By the end of this course, you'll be a better data analyst because you'll have an understanding of applied predictive modeling methods, and you'll know how to use existing machine learning methods in R

Introduction To Data Science
at
UDEMY
Curriculum

Course Overview

Course Introduction

Walk-through of a data science project

Starting with R and data

Modeling and Machine Learning

Mapping Business to Machine Learning Tasks

Validating Models

Your Feedback is Valuable

Naive Bayes: background

Naive Bayes: practice

Linear Regression: background

Linear Regression: practice

Logistic Regression: background

Logistic Regression: practice

Decision Trees and Random Forest: background

Random Forest: practice

Generalized Additive Models

Support Vector Machines

Gradient Boosting

Regularization for Linear and Logistic Regression

Evaluating Models

Data

Loading Data in R

Visualizing Data

Missing Values

The Shape of Data

Dealing with Categorical Variables

Useful Data Transformations

Moving On

Recommended Books

Further Topics

Next Steps

Introduction To Data Science
at
UDEMY
Faculty details

John Mount

He is co-author of the popular book Practical Data Science with R, and he blog often on mathematics, programming, machine learning, and optimization on the Win-Vector blog.

Nina Zumel

Nina Zumel, PhD, has over 10 years of experience in research, machine learning, and data science. She is a co-author of the popular book Practical Data Science with R, co-author of the EMC data scientist certification program, and blogs often on statistics, data science, and data visualization.