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: Rs 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

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2 stars: 0 votes

3 stars: 0 votes

4 stars: 6 votes

5 stars: 11 votes

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