Introduction To Data Science

Introduction To Data Science, available at $24.99, has an average rating of 3.5, with 28 lectures, based on 254 reviews, and has 4581 subscribers.

You will learn about 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. This course is ideal for individuals who are The course is for analytically minded students who are looking for an introduction to applied predictive modeling methods, and who want to learn about what goes into successful data science projects. The course will teach students how to use existing machine learning methods in R, but will not teach them how to implement these algorithms from scratch. Students should be familiar with basic statistics and basic scripting/programming. Some familiarity with R is helpful; otherwise, students should be willing to learn R as they go. We will direct you to ready-to-go implementations and additional references throughout the course. It is particularly useful for The course is for analytically minded students who are looking for an introduction to applied predictive modeling methods, and who want to learn about what goes into successful data science projects. The course will teach students how to use existing machine learning methods in R, but will not teach them how to implement these algorithms from scratch. Students should be familiar with basic statistics and basic scripting/programming. Some familiarity with R is helpful; otherwise, students should be willing to learn R as they go. We will direct you to ready-to-go implementations and additional references throughout the course.

Enroll now: Introduction To Data Science

Summary

Title: Introduction To Data Science

Price: $24.99

Average Rating: 3.5

Number of Lectures: 28

Number of Published Lectures: 28

Number of Curriculum Items: 28

Number of Published Curriculum Objects: 28

Original Price: $24.99

Quality Status: approved

Status: Live

What You Will Learn

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.

Who Should Attend

The course is for analytically minded students who are looking for an introduction to applied predictive modeling methods, and who want to learn about what goes into successful data science projects. The course will teach students how to use existing machine learning methods in R, but will not teach them how to implement these algorithms from scratch. Students should be familiar with basic statistics and basic scripting/programming. Some familiarity with R is helpful; otherwise, students should be willing to learn R as they go. We will direct you to ready-to-go implementations and additional references throughout the course.

Target Audiences

The course is for analytically minded students who are looking for an introduction to applied predictive modeling methods, and who want to learn about what goes into successful data science projects. The course will teach students how to use existing machine learning methods in R, but will not teach them how to implement these algorithms from scratch. Students should be familiar with basic statistics and basic scripting/programming. Some familiarity with R is helpful; otherwise, students should be willing to learn R as they go. We will direct you to ready-to-go implementations and additional references throughout the course.

Use the R Programming Language to execute data science projects and become a data scientist. Implement business solutions, using machine learning and predictive analytics.

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, youll 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.

Understand Data Science to Be a More Effective Data Analyst

●Use R and RStudio

●Master Modeling and Machine Learning

●Load, Visualize, and Interpret Data

Use R to Analyze Data and Come Up with Valuable Business Solutions

This course is designed for those who are analytically minded and are familiar with basic statistics and programming or scripting. Some familiarity with R is strongly recommended; otherwise, you can learn R as you go.

Youll 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.

All of these skills will combine to give you the ability to explore data, ask the right questions, execute predictive models, and communicate your informed recommendations and solutions to company leaders.

Contents and Overview

This course begins with a walk-through of a template data science project before diving into the R statistical programming language.

You will be guided through modeling and machine learning. Youll use machine learning methods to create algorithms for a business, and youll validate and evaluate models.

Youll learn how to load data into R and learn how to interpret and visualize the data while dealing with variables and missing values. You’ll be taught how to come to sound conclusions about your data, despite some real-world challenges.

By the end of this course, youll be a better data analyst because youll have an understanding of applied predictive modeling methods, and youll know how to use existing machine learning methods in R. This will allow you to work with team members in a data science project, find problems, and come up solutions.

You’ll complete this course with the confidence to correctly analyze data from a variety of sources, while sharing conclusions that will make a business more competitive and successful.

The course will teach students how to use existing machine learning methods in R, but will not teach them how to implement these algorithms from scratch. Students should be familiar with basic statistics and basic scripting/programming.

Course Curriculum

Chapter 1: Course Overview

Lecture 1: Course Introduction

Lecture 2: Walk-through of a data science project

Lecture 3: Starting with R and data

Chapter 2: Modeling and Machine Learning

Lecture 1: Mapping Business to Machine Learning Tasks

Lecture 2: Validating Models

Lecture 3: Your Feedback is Valuable

Lecture 4: Naive Bayes: background

Lecture 5: Naive Bayes: practice

Lecture 6: Linear Regression: background

Lecture 7: Linear Regression: practice

Lecture 8: Logistic Regression: background

Lecture 9: Logistic Regression: practice

Lecture 10: Decision Trees and Random Forest: background

Lecture 11: Random Forest: practice

Lecture 12: Generalized Additive Models

Lecture 13: Support Vector Machines

Lecture 14: Gradient Boosting

Lecture 15: Regularization for Linear and Logistic Regression

Lecture 16: Evaluating Models

Chapter 3: Data

Lecture 1: Loading Data in R

Lecture 2: Visualizing Data

Lecture 3: Missing Values

Lecture 4: The Shape of Data

Lecture 5: Dealing with Categorical Variables

Lecture 6: Useful Data Transformations

Chapter 4: Moving On

Lecture 1: Recommended Books

Lecture 2: Further Topics

Lecture 3: Next Steps

Instructors

Nina Zumel
Data Scientist, Win-Vector LLC

John Mount
Data Scientist, Win-Vector LLC

Rating Distribution

1 stars: 8 votes

2 stars: 8 votes

3 stars: 65 votes

4 stars: 91 votes

5 stars: 82 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!