DATA ANALYSIS AND DATA SCIENCE: Unlock Insights and Drive Innovation with Advanced Analytical Techniques (2024 Guide)

Winton Clem

Analysis of Data and the Field of Data Science

Data Analysis with R Progamming Language

Copyright © 2024 by Winton Clem

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Contents

1. Introduction

So, what exactly is data analysis?

How Does Data Analysis Work?

Data Mining

Text Analysis

Business Analytics

Data visualization

Data analysis

2. R Programming Language

HOW IS R APPLIED IN DATA SCIENCE?

R Applications in Various Industries

Financial Sector

Banking Sector

Healthcare Industry

Social Media Analysis

E-commerce

Further Applications of R:

Practical Applications of the R Programming Language

3. Analyzing Data and Predicting Outcomes Using R

Analysis and Prediction

Forecasting, Strategic Planning, and Objectives

Objectives

Planning

Choosing What to Predict

Data and Approaches in Forecasting

4. The Time Series Forecasting

The three main types of forecasting models

Explanatory Model

Time Series Model

Panel Data Model

The forecasting process involves several steps:

Problem Definition

Gathering Information

Preliminary/Exploratory Analysis

Choosing and Fitting Models

Using and Evaluating the Forecasting Model

Statistical perspective

Time Series

Time plots

Seasonal Analysis Overview

Scatterplot Matrix

Autocorrelation

Correlogram

The toolkit for forecasters

Mean Method

Naïve Method

Seasonal Naïve Method

Drift Method

Adjustments and Modifications

Mathematical Alterations

Logarithmic Transformation

Power Transformation

Box-Cox Transformation

Residual Diagnostics

Residuals

5. Test

Box-Pierce Test

Ljung-Box Test (a more precise method)

Forecast discrepancies

Mean Absolute Percentage Error (MAPE)

Time series cross-validation

The pipe operator

Prediction intervals

Methods for Evaluation

Intervals from Residual Bootstrapping

Intervals with Data Transformations

Time series regression techniques

Simple Linear Regression

Multiple Linear Regression

The principle of least squares

Estimated values

Model Fit Quality

Regression Standard Error

6. Assessing The Regression Model

Key Characteristics of Residuals

Autocorrelation in Residuals and Its Implications

Breusch-Godfrey Test

Histogram Analysis of Residuals

Residual Analysis Against Predictors

Evaluation of Residuals Against Fitted Values

Outlier and impactful data points

Spurious Regression

Useful Predictors

Trend

Dummy Variables

Choosing predictors for regression models

Dynamic Predictive Model

Scenario-based forecasting

Creating a regression model

Nonlinear regression

Data Transformation:

Utilizing a Nonlinear Function

Forecasting using a nonlinear trend

Exponential trend

Piecewise trends

The cubic spline model

Correlation, causation, and forecasting

7. The Time Series Decomposition

The time series

Trend-cycle component

Seasonal component

Remainder component

Time series decomposition

Moving Averages (MA)

Moving averages of moving averages

8. Classical Decomposition

Additive Decomposition:

Multiplicative Decomposition:

Challenges with Classical Decomposition:

STL decomposition

9. Forecasting Through Decomposition Techniques

Method A

Method B

Exponential Smoothing Introduction

Simple Exponential Smoothing

Optimization

Methods for Trend Analysis

Holt’s linear trend

Damped Trend

Holt-Winter’s Seasonal Approach

Holt-Winters’ Method

1

Introduction

The global landscape is increasingly reliant on data, with a vast reservoir of information at our fingertips. Major corporations such as Google and Microsoft harness this data for decision-making, but they aren’t the sole beneficiaries. Is this data valuable? Undoubtedly so!

Data analysis isn’t limited to large enterprises; it’s utilized by small businesses, retailers, healthcare professionals, and even the sports industry. It serves as a universally understood language, growing in significance with each passing day. While it might seem intricate, at its core, data analysis boils down to implementing a few fundamental concepts.

So, what exactly is data analysis?