LibGuides: Statistics and Data Science: Programming

UTL Software Resources

UTL provides access to different software at its computer labs across campus. View the Computer Labs and Workspaces Page to find a lab with the software you need. You can search by location, software, or equipment.

Programming Languages

Online Resources:

R for Data Science - "This is the website for the 2nd edition of “R for Data Science” (you can access the book through UTL as well!) This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it and visualize."
The R Project for Statistical Computing - Great R programming resource. Provides news updates, reference documentation, and access to an R blog with current updates from the community.

Videos:

(Access provided through UTL).

Part 1, An introduction to programming with R - Installation process and the R layout and environment; The fundamental elements of R programming; Types of data.

Part 2, An introduction to programming with R - Recording, filtering, and summarizing data; Exploring data; Built-in datasets in R; The tidyverse packages; Selecting and creating variables, changing their order and their names.

Books:

R 4 Data Science Quick Reference by Thomas Mailund
ISBN: 9781484287798
Publication Date: 2022-10-29
In this handy, quick reference book you'll be introduced to several R data science packages, with examples of how to use each of them. All concepts will be covered concisely, with many illustrative examples using the following APIs: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more.
Graphing Data with R by John Jay Hilfiger
ISBN: 9781491922569
Publication Date: 2015-10-19
This introductory guide shows you how to use the R language to create a variety of useful graphs for visualizing and analyzing complex data for science, business, media, and many other fields. You'll learn methods for highlighting important relationships and trends, reducing data to simpler forms, and emphasizing key numbers at a glance.
Introduction to Probability, Statistics and R by Sujit K. Sahu
ISBN: 9783031378645
Publication Date: 2024-04-02
This book serves as a gateway to learning statistical methods from scratch, assuming a solid background in high school mathematics. Readers gradually progress from basic concepts to advanced statistical modelling, with examples from actuarial, biological, ecological, engineering, environmental, medicine, and social sciences highlighting the real-world relevance of the subject. An accompanying R package enables seamless practice and immediate application, making it ideal for beginners.
Univariate, Bivariate, and Multivariate Statistics Using R by Daniel J. Denis
ISBN: 9781119549932
Publication Date: 2020-04-14
A practical source for performing essential statistical analyses and data management tasks in R Univariate, Bivariate, and Multivariate Statistics Using R offers a practical and very user-friendly introduction to the use of R software that covers a range of statistical methods featured in data analysis and data science.

Books:

Applied Statistics with Python by Leon Kaganovskiy
ISBN: 9781040309971
Publication Date: 2025-03-06
Applied Statistics with Python: Volume I: Introductory Statistics and Regression concentrates on applied and computational aspects of statistics, focusing on conceptual understanding and Python-based calculations. Based on years of experience teaching introductory and intermediate Statistics courses at Touro University and Brooklyn College, this book compiles multiple aspects of applied statistics, teaching the reader useful skills in statistics and computational science with a focus on conceptual understanding.
Modern Statistics by Shelemyahu Zacks; Peter Gedeck; Ron S. Kenett
ISBN: 9783031075650
Publication Date: 2022-09-21
This innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications.
A Python Data Analyst's Toolkit by Gayathri Rajagopalan
ISBN: 9781484263983
Publication Date: 2020-12-23
Explore the fundamentals of data analysis, and statistics with case studies using Python. This book will show you how to confidently write code in Python, and use various Python libraries and functions for analyzing any dataset.
Python for Probability, Statistics, and Machine Learning by José Unpingco
ISBN: 9783031046476
Publication Date: 2022-11-05
Using a novel integration of mathematics and Python codes, this book illustrates the fundamental concepts that link probability, statistics, and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses. To clearly connect theoretical concepts to practical implementations, the author provides many worked-out examples along with "Programming Tips" that encourage the reader to write quality Python code.
Statistics and Data Visualisation with Python by Jesus Rogel-Salazar
ISBN: 9780367749361
Publication Date: 2022-12-01
Statistics and Data Visualisation with Python builds statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics, and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis.

Videos:

Statistics essentials with Python - Dr. Chirag Shah, PhD, reviews two Python packages used for statistical processing with Python: NumPy library, used for data processing, and matplotlib.pyplot for data visualization.

Data analysis with Python for Excel users - A common task for scientists and engineers is to analyze data from an external source. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information.

Books:

Getting Started with SQL and Databases by Mark Simon
ISBN: 9781484294925
Publication Date: 2023-06-08
Learn the basics of writing SQL scripts. Using Standard SQL as the starting point, this book teaches writing SQL in various popular dialects, including PostgreSQL, MySQL/MariaDB, Microsoft SQL Server, Oracle, and SQLite.
Data Science with SQL Server Quick Start Guide by Dejan Sarka
ISBN: 9781789537130
Publication Date: 2018-09-14
Use the features of SQL Server 2017 to implement the data science project life cycle Leverage the power of R and Python to design and develop efficient data models find unique insights from your data with powerful techniques for data preprocessing and analysis.
SQL Server Analytical Toolkit by Angelo Bobak
ISBN: 9781484286661
Publication Date: 2023-09-24
Learn window function foundational concepts through a cookbook-style approach, beginning with an introduction to the OVER() clause, its various configurations in terms of how partitions and window frames are created, and how data is sorted in the partition so that the window function can operate on the partition data sets. You will build a toolkit based not only on the window functions but also on the performance tuning tools, use of Microsoft Excel to graph results, and future tools you can learn such as PowerBI, SSIS, and SSAS to enhance your data architecture skills.

Videos:

Master SQL for data analysis / [presented by] Idan Gabrieli.
Learn sequential query language terminology, build databases, and write SQL syntax step by step. Create/load data into databases, schema, and tables, and analyze data with filter and search patterns. Learn about datasets and e-commerce datasets, and handle SQL queries and subqueries efficiently.

Articles:

Let’s Try – Developers: Simplifying Statistics with SQL for Data Analysis
This article explores ways to find the measures of central tendency and the measures of variability using MySQL.

Software/Environments

Google Colab Notebook
"Colab is a hosted Jupyter Notebook service that requires no setup to use and provides free access to computing resources, including GPUs and TPUs. Colab is especially well suited to machine learning, data science, and education."
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https://colab.google/
Jupyter Notebooks
"JupyterLab is the latest web-based interactive development environment for notebooks, code, and data. Its flexible interface allows users to configure and arrange workflows in data science, scientific computing, computational journalism, and machine learning. A modular design invites extensions to expand and enrich functionality."