Data Science, AI, and Machine Learning with R

A warm welcome to the Data Science, Artificial Intelligence, and Machine Learning with R course by Uplatz.

R Programming Language

• Concept: R is a free, open-source programming language and software environment designed for statistical computing and graphics. It is widely used by statisticians, data scientists, and researchers.
• Key Strengths in the Context of Data Science, AI & ML:
  • Vast Ecosystem: R boasts a rich collection of packages (over 18,000+) contributed by the community, covering a broad spectrum of data analysis and machine learning tasks.
  • Data Visualization: R’s powerful visualization libraries (like ggplot2) create publication-quality plots and interactive graphics, aiding in data exploration and communication of insights.
  • Statistical Power: R’s foundation in statistics provides a strong base for data analysis, hypothesis testing, and modeling.
  • Reproducibility: R encourages reproducible research through its literate programming capabilities (R Markdown), making it easier to document and share the entire analysis process.

Data Science

• Concept: Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It involves various techniques, including data mining, statistics, machine learning, and visualization.
• R’s Role in Data Science: R provides a robust environment for data science tasks. Its extensive libraries (like dplyr, tidyr, ggplot2) enable data cleaning, manipulation, exploration, and visualization. R’s statistical capabilities make it ideal for hypothesis testing, modeling, and drawing inferences from data.

1. Data Manipulation and Cleaning: R excels at data manipulation and cleaning, using packages like dplyr, tidyr, and data.table. These tools help in transforming and preparing data for analysis.
2. Exploratory Data Analysis (EDA): R provides extensive tools for EDA, allowing users to summarize datasets, detect outliers, and identify trends. Functions in base R along with packages like ggplot2 are commonly used for this purpose.
3. Statistical Analysis: R was built for statistics, so it offers a wide array of functions for hypothesis testing, regression analysis, ANOVA, and more. Packages like stats, MASS, and lmtest are frequently used for statistical modeling.
4. Data Visualization: R is renowned for its data visualization capabilities. ggplot2 is a powerful package for creating complex, multi-layered graphics. Other packages like lattice and plotly allow for interactive visualizations.

Artificial Intelligence (AI)

• Concept: AI is a broad field of computer science that aims to create intelligent agents capable of mimicking human-like cognitive functions such as learning, reasoning, problem-solving, perception, and language understanding.
• R’s Role in AI: While R isn’t the primary language for core AI development (like Python or C++), it plays a vital role in AI research and applications. R’s statistical and machine learning libraries (like caret, randomForest) facilitate building predictive models, evaluating their performance, and interpreting results.

1. Statistical Learning: R supports various statistical learning methods, which are foundational for AI. Libraries like caret and mlr provide tools for building and evaluating statistical models.
2. Natural Language Processing (NLP): While Python is more popular for NLP, R has packages like tm and quanteda for text mining and processing tasks. These can be used for sentiment analysis, topic modeling, and other NLP tasks.
3. Computer Vision: R can be used for basic computer vision tasks through packages like EBImage. However, for more complex tasks, Python is generally preferred due to its more extensive libraries.
4. Integration with Python: For AI tasks where Python’s libraries are more advanced, R can be integrated with Python through the reticulate package, allowing users to leverage Python’s AI capabilities while staying within the R environment.

Machine Learning (ML)

• Concept: ML is a subset of AI that focuses on developing algorithms that enable systems to learn from data and improve their performance on a specific task without being explicitly programmed.
• R’s Role in Machine Learning: R shines in the machine learning domain. It offers a comprehensive collection of machine learning algorithms (regression, classification, clustering, etc.) and tools for model building, evaluation, and tuning. Packages like caret simplify the process of training and comparing various models.

1. Model Development: R offers several packages for building machine learning models, such as randomForest, xgboost, and caret. These tools help in creating models like decision trees, random forests, and gradient boosting machines.
2. Model Evaluation: R provides robust tools for evaluating model performance, including cross-validation, ROC curves, and other metrics. The caret package is particularly useful for this purpose.
3. Feature Engineering: R’s data manipulation packages, like dplyr and caret, are used for feature engineering, which involves creating new features from raw data to improve model performance.
4. Deep Learning: While Python dominates deep learning, R has packages like keras and tensorflow that provide an interface to TensorFlow, allowing users to build deep learning models within R.
5. Deployment: R can be used to deploy models into production environments. The plumber package, for example, can turn R scripts into RESTful APIs, enabling the integration of R models into applications.

Artificial Intelligence, Data Science, and Machine Learning with R – Course Curriculum

1. Overview of Data Science and R Environment Setup
Essential concepts of data science R language Environment Setup

2. Introduction and Foundation Principles of R Programming
Basic concepts of R programming

3. Data Collection

Effective ways of handling various file types and importing techniques

4. Probability & Statistics
Understanding patterns, summarizing data mastering statistical thinking and probability theory

5. Exploratory Data Analysis & Data Visualization
Making the data ready using charts, graphs, and interactive visualizations to use in statistical models

6. Data Cleaning, Data Manipulation & Preprocessing

Garbage in – Garbage out (Wrangling/Munging):

7. Statistical Modeling & Machine Learning

Set of algorithms that use data to learn, generalize, and predict

8. End to End Capstone Project

1. Overview of Data Science and R Environment Setup

a. Overview of Data Science

• Introduction to Data Science
• Components of Data Science
• Verticals influenced by Data Science
• Data Science Use cases and Business Applications
• Lifecycle of Data Science Project

b. R language Environment Setup

• Introduction to Anaconda Distribution
• Installation of R and R Studio
• Anaconda Navigator and Jupyter Notebook with R
• Markdown Introduction and Scripting
• R Studio Introduction and Features

2. Introduction and Foundation Principles of R Programming

a. Overview of R environment and core R functionality

b. Data types

• Numeric (integer and double)
• complex
• character and factor
• logical
• date and time
• Raw

c. Data structures

• vectors
• matrices
• arrays
• lists
• data frames

d. Operators

• arithmetic
• relational
• logical
• assignment Operators

e. Control Structures & Loops

• for, while
• if else
• repeat, next, break
• switch case

g. Functions

• apply family functions

(i) apply

(ii) lapply

(iii) sapply

(iv) tapply

(v) mapply

• Built-in functions
• User defined functions

3. Data Collection

a. Data Importing techniques, handling inaccurate and inconsistent data

b. Flat-files data

• read.csv
• read.table
• read.csv2
• read.delim
• read.delim2

c. Excel data

• readxl
• xlsx
• readr
• xlconnect
• gdata

d. Databases (MySQL, SQLite…etc)

• RmySQL
• RSQLite

e. Statistical software’s data (SAS, SPSS, stata, etc.)

• foreign
• haven
• hmisc

f. web-based data (HTML, xml, json, etc.)

• rvest package
• rjson package

g. Social media networks (Facebook Twitter Google sheets APIs)

• Rfacebook
• twitteR

4. Probability & Statistics

a. Core concepts of mastering in statistical thinking and probability theory

b. Descriptive Statistics

•     Types of Variables & Scales of Measurement

(i) Qualitative/Categorical

1) Nominal

2) Ordinal

(ii) Quantitative/Numerical

1) Discrete

2) Continuous

3) Interval

4) Ratio

• Measures of Central Tendency(i) Mean, median, mode
• Measures of Variability & Shape(i) Standard deviation, variance and Range, IQR

  (ii) Sleekness & Kurtosis

c. Probability & Distributions

• Introduction to probability
• binomial distribution
• uniform distribution

d. Inferential Statistics

• Sampling & Sampling Distribution
• Central Limit Theorem
• Confidence Interval Estimation
• Hypothesis Testing

5. Exploratory Data Analysis & Data Visualization

a. Understanding patterns, summarizing data and presentation using charts, graphs and interactive visualizations

b. Univariate data analysis

c. Bivariate data analysis

d. Multivariate Data analysis

e. Frequency Tables, Contingency Tables & Cross Tables

f. Plotting Charts and Graphics

• Scatter plots
• Bar Plots / Stacked bar chart
• Pie Charts
• Box plots
• Histograms
• Line Graphs
• ggplot2, lattice packages

6. Data Cleaning, Data Manipulation & Preprocessing

a. Garbage in – garbage out: Data munging or Data wrangling

b. Handling errors and outliers

c. Handling missing values

d. Reshape data (adding, filtering, dropping and merging)

e. Rename columns and data type conversion

f. Duplicate records

g. Feature selection and feature scaling

h. Useful R packages

• data.table
• dplyr
• sqldf
• tidyr
• reshape2
• lubridate
• stringr

7. Statistical Modeling & Machine Learning

a. Set of algorithms that uses data to learn, generalize, and predict

b. Regression

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression

c. Classification

• Logistic Regression
• K-Nearest Neighbors (K-NN)
• Support Vector Machine (SVM)
• Decision Trees and Random Forest
• Naive Bayes Classifier

d. Clustering

• K-Means Clustering
• Hierarchical clustering
• DBSCAN clustering

e. Association Rule Mining

• Apriori
• Market Basket Analysis

f. Dimensionality Reduction

• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)

g. Ensemble Methods

• Bagging
• Boosting

8. End to End Capstone Project

Career Path and Job Titles after learning R

R is primarily used for statistical analysis, data science, and data visualization. It’s particularly popular in academia, research, finance, and industries where data analysis is crucial. Following is a potential career path and the job titles you might target after learning R:

1. Entry-Level Roles

• Data Analyst: Uses R to clean, manipulate, and analyze datasets. This role often involves generating reports, creating visualizations, and conducting basic statistical analysis.
• Statistical Analyst: Focuses on applying statistical methods to analyze data and interpret results. R is commonly used for its rich set of statistical tools.
• Junior Data Scientist: Works under the supervision of senior data scientists to gather, clean, and analyze data, often using R for data exploration and model building.
• Research Assistant: Supports research projects by performing data analysis, literature reviews, and statistical testing, often using R for handling data.

2. Mid-Level Roles

• Data Scientist: Uses R to build predictive models, perform advanced statistical analysis, and extract actionable insights from data. This role may also involve developing and testing machine learning algorithms.
• Quantitative Analyst (Quant): Works in finance or trading, using R to analyze financial data, develop pricing models, and perform risk assessment.
• Biostatistician: Uses R to analyze biological data, often in clinical trials or medical research. This role involves designing experiments, analyzing results, and interpreting the data.
• Econometrician: Applies statistical methods to economic data to analyze trends, make forecasts, and model economic behavior. R is commonly used for econometric modeling.

3. Senior-Level Roles

• Senior Data Scientist: Leads data science projects, mentors junior team members, and designs complex models to solve business problems using R and other tools.
• Data Science Manager: Oversees data science teams, ensuring that projects align with business goals. This role involves both technical work and managerial responsibilities.
• Principal Statistician: Works at a high level within organizations, leading statistical analysis and contributing to the design of studies, experiments, and surveys.
• Chief Data Officer (CDO): An executive role responsible for the data strategy and governance within an organization. This position requires deep expertise in data science, often with a background in using tools like R.