Mastering Data Science With R: From Beginner To Master.

R programming for Data Science

What you'll learn

Introduction to data science and R Programming

Intermediate R Programming and working with files

Data Visualization and Comparing means with Statistics

Machine Learning

Time Series Analysis, working with Dashboards and Reporting in R

Requirements

No Programming Experience needed

You need a little understanding of Statistics

Description

Data Science with R is a comprehensive course designed to equip learners with the essential skills and knowledge to perform data analysis, machine learning, and time series analysis using the R programming language. Participants will start with an introduction to R and its data types, followed by learning data preparation techniques using dplyr functions. The course will then delve into programming concepts, data analysis, and machine learning with a strong emphasis on hands-on practice. Throughout the course, participants will explore various statistical methods, such as regression analysis, decision trees, random forest, and clustering, to make data-driven decisions and gain insights from data. Furthermore, learners will be guided through the end-to-end machine learning process, including data preprocessing, model selection, training, evaluation, and tuning. The course will also cover advanced topics, including time series analysis, where participants will learn to work with temporal data, detect trends, and seasonality, and make predictions using time series models. Additionally, learners will discover how to create interactive dashboards.Course Objectives: 1. Master R Programming: Develop a strong foundation in R programming, including data manipulation, control structures, importing and exporting data, and working with various data structures. 2. Data Analysis Techniques: Learn essential data analysis techniques, such as scatter plots, box plots, bar charts, histograms, and correlation, to explore and visualize data for insights. 3. Regression Analysis: Understand the principles of simple and multiple linear regression, stepwise regression, and generalized linear models to make predictions and understand relationships between variables. 4. Decision Trees and Random Forest: Explore classification trees, build decision trees, and implement random forests for predictive modeling and ensemble learning. 5. Unsupervised Machine Learning: Master unsupervised learning algorithms, including k-means clustering and hierarchical clustering, to discover patterns and groups within data. 6. End-to-End Machine Learning Project: Gain hands-on experience in the complete machine learning process, from data preprocessing to model training, evaluation, and tuning, culminating in a comprehensive machine learning project. 7. Time Series Analysis: Acquire skills in handling time series data, decomposing time series, forecasting with exponential smoothing and Holt-Winters method, and modeling stationary time series using autoregressive (AR) and moving average (MA) models. 8. Interactive Dashboards and Reporting: Learn to create interactive dashboards using R Shiny and build dynamic reports with R Markdown for reproducible and visually engaging data presentations.

Overview

Section 1: Introduction

Lecture 1 Introduction to Data Science and R Programming

Lecture 2 Introduction to Data Preparation

Lecture 3 Data and Introduction to Functions

Lecture 4 Functions

Section 2: Intermediate R

Lecture 5 Introduction to R statements

Lecture 6 Data Import

Lecture 7 Data Export and missing Values

Lecture 8 Correlations

Section 3: Data Visualization and Comparing Means

Lecture 9 Introduction to Visualization

Lecture 10 Charts

Lecture 11 Comparing Means

Lecture 12 Introduction to ANOVA

Section 4: Machine Learning

Lecture 13 Regression Analysis

Lecture 14 Decision Trees and Random Forest

Lecture 15 Unsupervised Machine Learning

Lecture 16 End-to-end Machine Learning project in R

Section 5: Time Series, Working with Dashboards and reporting

Lecture 17 Time Series Analysis

Lecture 18 Time Series Analysis Continued

Lecture 19 Dashboards In R

Lecture 20 R Markdown and Notebooks

Beginner R programming and Data Science Enthusiasts