Understanding New Data – Exploratory Analysis in R
Understanding New Data – Exploratory Analysis in R, available at $44.99, has an average rating of 4, with 63 lectures, based on 19 reviews, and has 198 subscribers.
You will learn about Identify suitable R libraries for data exploration Create suitable data visualizations Learn the succession of steps in data exploration Use a combination of hypothesis tests, explorations and models How to prepare data for exploration What to do when problems arise in the initial stages Work with the main variable types Use time series data This course is ideal for individuals who are Data scientists or Analysts of all fields or Researchers working and analyzing data or Young professionals wanting to switch to data analysis related work or Students taking data analysis exams or Everyone interested in analyzing data or Data exploration is an initial phase of a data analysis project therefore you will need these skills in most of your projects It is particularly useful for Data scientists or Analysts of all fields or Researchers working and analyzing data or Young professionals wanting to switch to data analysis related work or Students taking data analysis exams or Everyone interested in analyzing data or Data exploration is an initial phase of a data analysis project therefore you will need these skills in most of your projects.
Enroll now: Understanding New Data – Exploratory Analysis in R
Summary
Title: Understanding New Data – Exploratory Analysis in R
Price: $44.99
Average Rating: 4
Number of Lectures: 63
Number of Published Lectures: 63
Number of Curriculum Items: 63
Number of Published Curriculum Objects: 63
Original Price: $39.99
Quality Status: approved
Status: Live
What You Will Learn
- Identify suitable R libraries for data exploration
- Create suitable data visualizations
- Learn the succession of steps in data exploration
- Use a combination of hypothesis tests, explorations and models
- How to prepare data for exploration
- What to do when problems arise in the initial stages
- Work with the main variable types
- Use time series data
Who Should Attend
- Data scientists
- Analysts of all fields
- Researchers working and analyzing data
- Young professionals wanting to switch to data analysis related work
- Students taking data analysis exams
- Everyone interested in analyzing data
- Data exploration is an initial phase of a data analysis project therefore you will need these skills in most of your projects
Target Audiences
- Data scientists
- Analysts of all fields
- Researchers working and analyzing data
- Young professionals wanting to switch to data analysis related work
- Students taking data analysis exams
- Everyone interested in analyzing data
- Data exploration is an initial phase of a data analysis project therefore you will need these skills in most of your projects
-
Are you new to R and data analysis?
-
Do you ever struggle starting an analysis with a new dataset?
-
Do you have problems getting the data into shape and selecting the right tools to work with?
-
Have you ever wondered if a dataset had the information you were interested in and if it was worth the effort?
If some of these questions occurred to you, then this program might be a good start to set you up on your data analysis journey. Actually, these were the question I had in mind when I designed the curriculum of this course. As you can see below, the curriculum is divided into three main sections. Although this course doesn’t have a focus on the basic concepts of statistics, some of the most important concepts are covered in the first section of the course.
The two other sections have their focus on the initial and the exploratory data analysis phases respectively. Initial data analysis (or IDA for short) is where we clean and shape the data into a form suitable for the planned methods. This is also where we make sure the data makes sense from a statistical point of view. In the IDA section I present tools and methods that will help you figure out if the data was collected properly and if it is worthy of being analyzed.
On the other hand, the exploratory data analysis (EDA) section offers techniques to find out if the data can answer your analytical questions, or in other words, if the data has a relevant story to tell. This will spare you from investing time and effort into a project that will not deliver the results you hoped for. In an ideal case the results of EDA may confirm that the planned analysis is worth it and that there are insights to be gained from that dataset and project.
If you are interested in statistical methods and R tools that help you bridge the gap between data collection and the confirmatory data analysis (CDA), then this program is for you. Take a look at the curriculum and give this course a try!
Course Curriculum
Chapter 1: Introduction
Lecture 1: The Landscape: Data Science and Data Analysis
Lecture 2: Data Analysis Stages: IDA, EDA and CDA
Lecture 3: Why Do We Work with Statistical Samples? – Population vs. Sample
Lecture 4: The Normal Probability Distribution
Lecture 5: The Tidyverse
Lecture 6: Datasets and R Libraries
Lecture 7: Summary
Chapter 2: Initial Data Analysis and Data Pre-processing
Lecture 1: Introduction
Lecture 2: The Succession of Data Pre-processing Steps
Lecture 3: Importing Tabular Data
Lecture 4: Reading and Parsing JSON Files
Lecture 5: Reshaping Techniques
Lecture 6: Sampling Approaches: Creating Subsets with R Base
Lecture 7: Sampling Approaches: Stratified Sampling
Lecture 8: Classifying Variables and Objects
Lecture 9: Data Class Conversion
Lecture 10: Managing Duplicates
Lecture 11: Relative Group Sizes: Calculating Marginal Sums
Lecture 12: Understanding Missing Values
Lecture 13: R's Toolbox for Missing Data Handling
Lecture 14: Detecting Missing Data with Visual Tools: Pattern Identification
Lecture 15: Simple NA Handling Methods
Lecture 16: Investigating the Structure of Missing Values
Lecture 17: Deciding for a Suitable NA Handling Method
Lecture 18: Multiple Imputation with Random Forest
Lecture 19: Validating Numeric Variables
Lecture 20: Understanding Outliers and the Reasons Behind Them
Lecture 21: Exploring Outliers in the Data
Lecture 22: Outlier Detection with Visual Methods: The Boxplot Method
Lecture 23: Outlier Detection with the Six Sigma Method
Lecture 24: Detecting Outliers with Hypothesis Tests
Lecture 25: Multivariate Outlier Detection
Lecture 26: Robust Principal Component Algorithm for Outlier Detection
Lecture 27: Outlier Detection with the Mahalanobis Distance
Lecture 28: Testing for Outliers in Transformed Data
Lecture 29: Plausibility Checks for Non-numeric Data
Lecture 30: Writing a Report: What to Include in an IDA Progress Documentation
Lecture 31: Summary: Initial Data Analysis
Chapter 3: Exploratory Data Analysis
Lecture 1: Introduction
Lecture 2: What Is EDA and What Is the Succession of Steps?
Lecture 3: The Benefits of Using Data Visualizations in EDA
Lecture 4: Basic Plot Types for EDA
Lecture 5: Dataset Overview and Quality Check: Diamonds from Ggplot2
Lecture 6: Non-parametric Methods to Explore the Distribution in Numeric Variables
Lecture 7: Parametric Methods to Explore the Distribution in Numeric Variables
Lecture 8: Exploring Categorical Variables
Lecture 9: The Distribution in Relation to Grouping Variables
Lecture 10: Density Plot
Lecture 11: Relationships Between Numeric Variables
Lecture 12: Dataset Overview: Flights
Lecture 13: Dataset Summary and Variable Classification
Lecture 14: Summaries for Grouping Variables
Lecture 15: Assembling Summary Tables of Custom Aggregations
Lecture 16: Numeric Distributions
Lecture 17: Time Series Based Summaries
Lecture 18: Visual Exploration of the Time Component
Lecture 19: Analysing What Is Missing: Cancelled Flights
Lecture 20: Linear Relationships Between Numeric Variables
Lecture 21: Measuring the Strenght of Association Between Events
Lecture 22: Statistical Models in Exploratory Analysis
Lecture 23: Identifying Covariates with Logistic Regression
Lecture 24: Conclusions about the Flights Dataset
Lecture 25: Farewell
Instructors
-
R-Tutorials Training
Data Science Education
Rating Distribution
- 1 stars: 2 votes
- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 6 votes
- 5 stars: 11 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!
You may also like
- Top 10 Video Editing Courses to Learn in November 2024
- Top 10 Music Production Courses to Learn in November 2024
- Top 10 Animation Courses to Learn in November 2024
- Top 10 Digital Illustration Courses to Learn in November 2024
- Top 10 Renewable Energy Courses to Learn in November 2024
- Top 10 Sustainable Living Courses to Learn in November 2024
- Top 10 Ethical AI Courses to Learn in November 2024
- Top 10 Cybersecurity Fundamentals Courses to Learn in November 2024
- Top 10 Smart Home Technology Courses to Learn in November 2024
- Top 10 Holistic Health Courses to Learn in November 2024
- Top 10 Nutrition And Diet Planning Courses to Learn in November 2024
- Top 10 Yoga Instruction Courses to Learn in November 2024
- Top 10 Stress Management Courses to Learn in November 2024
- Top 10 Mindfulness Meditation Courses to Learn in November 2024
- Top 10 Life Coaching Courses to Learn in November 2024
- Top 10 Career Development Courses to Learn in November 2024
- Top 10 Relationship Building Courses to Learn in November 2024
- Top 10 Parenting Skills Courses to Learn in November 2024
- Top 10 Home Improvement Courses to Learn in November 2024
- Top 10 Gardening Courses to Learn in November 2024