--- title: Data Science description: This multidisciplinary field combines statistics, scientific computing, and algorithms to extract knowledge and insights from structured and unstructured data. Learners will understand how to perform data cleaning, exploratory analysis, predictive modeling, and data visualization. category: programming-tech subcategory: artificial-intelligence difficulty: beginner, intermediate, advanced url: /subject/data-science --- # Data Science This multidisciplinary field combines statistics, scientific computing, and algorithms to extract knowledge and insights from structured and unstructured data. Learners will understand how to perform data cleaning, exploratory analysis, predictive modeling, and data visualization. ## Available Resources 2 Videos • 4 Books • 8 Courses ## Videos ### 1. Vectors | Chapter 1, Essence of Linear Algebra **Author:** Grant Sanderson Opening chapter of Grant Sanderson's animated linear algebra series, contrasting the physics, computer science and mathematics views of vectors. Shows vectors as arrows and coordinate lists, and how addition and scalar multiplication work geometrically, laying intuition for linear combinations and transformations. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.youtube.com/watch?v=fNk_zzaMoSs **Tags:** linear-algebra, vectors, vector-addition, visual-intuition ### 2. StatQuest with Josh Starmer - Neural Networks videos Josh Starmer's StatQuest provides clear, concise, and often humorous explanations of statistical and machine learning concepts. His videos on neural networks break down complex ideas into easily digestible 'quests.' **Difficulty:** Beginner | **Language:** English | **Price:** Free **Link:** https://www.youtube.com/playlist?list=PLXe8IwAUB2B-Ews9ADFJBHE7nVAZAfV05 **Tags:** neural-networks, backpropagation, machine-learning, statistics, video-series ## Courses ### 1. Data Analysis with Python Learn data analysis with Python! This free course covers NumPy, Pandas, data cleaning, and visualization. Start your data science journey today! **Difficulty:** Beginner | **Price:** Free **Link:** https://www.freecodecamp.org/learn/data-analysis-with-python/ **Tags:** python, pandas, numpy, data-analysis, data-visualization ### 2. Introduction to Data Science in Python This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.coursera.org/learn/python-data-analysis **Tags:** python, pandas, data-analysis, dataframes, statistics ### 3. Practical Deep Learning for Coders fast.ai's free course teaching deep learning top-down: you train working image, text, and tabular models in the first lessons, then work back to the underlying mechanics. Assumes about a year of coding experience, uses PyTorch and the fastai library. **Difficulty:** Beginner | **Price:** Free **Link:** https://course.fast.ai/ **Tags:** deep-learning, pytorch, fastai, computer-vision, top-down-teaching ### 4. Machine Learning In the first course of the Machine Learning Specialization, you will: • Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.coursera.org/learn/machine-learning **Tags:** supervised-learning, linear-regression, logistic-regression, python, scikit-learn ### 5. SQL for Data Science As data collection has increased exponentially, so has the need for people skilled at using and interacting with data; to be able to think critically, and provide insights to make better decisions and optimize their businesses. This is a data scientist, “part mathematician, part computer scientist, and part trend spotter” (SAS Institute, Inc.). According to Glassdoor, being a data scientist is the best job in America; with a median base salary of $110,000 and thousands of job openings at a time. The skills necessary to be a good data scientist include being able to retrieve and work with data, and to do that you need to be well versed in SQL, the standard language for communicating with database systems. This course is designed to give you a primer in the fundamentals of SQL and working with data so that you can begin analyzing it for data science purposes. You will begin to ask the right questions and come up with good answers to deliver valuable insights for your organization. This course starts with the basics and assumes you do not have any knowledge or skills in SQL. It will build on that foundation and gradually have you write both simple and complex queries to help you select data from tables. You'll start to work with different types of data like strings and numbers and discuss methods to filter and pare down your results. You will create new tables and be able to move data into them. You will learn common operators and how to combine the data. You will use case statements and concepts like data governance and profiling. You will discuss topics on data, and practice using real-world programming assignments. You will interpret the structure, meaning, and relationships in source data and use SQL as a professional to shape your data for targeted analysis purposes. Although we do not have any specific prerequisites or software requirements to take this course, a simple text editor is recommended for the final project. So what are you waiting for? This is your first step in landing a job in the best occupation in the US and soon the world! **Difficulty:** Beginner | **Price:** Free **Link:** https://www.coursera.org/learn/sql-for-data-science **Tags:** sql, data-analysis, joins, case-statements, coursera ### 6. Databases and SQL for Data Science with Python **Author:** Rav Ahuja, Hima Vasudevan IBM course on Coursera teaching SQL for data work, from SELECT, INSERT, UPDATE and DELETE through filtering, grouping, subqueries, joins, views, stored procedures and transactions. Labs query cloud databases from Jupyter notebooks with Python, ending in a project analysing real-world datasets. **Difficulty:** Beginner | **Price:** Free **Link:** https://www.coursera.org/learn/sql-data-science **Tags:** sql, python, data-science, jupyter, relational-databases ### 7. Kaggle Learn Free interactive micro-courses from Kaggle, each a few hours of short lessons with in-browser coding exercises. Topics include Python, pandas, data visualization, SQL, feature engineering and introductory machine learning, giving learners working code skills for basic data analysis and modelling. **Difficulty:** Beginner | **Language:** English | **Price:** Free **Link:** https://www.kaggle.com/learn **Tags:** python, pandas, machine-learning, data-visualization, sql ### 8. Harvard University's CS109 Data Science **Author:** Pavlos Protopapas, Kevin Rader, Natesh Pillai, Mark Glickman Harvard's two-semester data science course with public lectures, labs and homework. CS109A covers data collection, cleaning, visualization and regression and classification models; CS109B adds deep learning and probabilistic methods. Students learn to run a full analysis in Python. **Difficulty:** Intermediate | **Language:** English | **Price:** Free **Link:** https://cs109.org/ **Tags:** data-science, python, machine-learning, data-visualization, statistical-modeling ## Podcasts ### 1. Data Skeptic **Author:** Kyle Polich The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches. **Difficulty:** Beginner | **Language:** en | **Price:** Free **Link:** https://dataskeptic.com **Tags:** data-science, machine-learning, statistics, critical-thinking, interviews ### 2. Not So Standard Deviations **Author:** Roger Peng and Hilary Parker Roger Peng and Hilary Parker talk about the latest in data science and data analysis in academia and industry. **Difficulty:** Beginner | **Language:** en | **Price:** Free **Link:** https://nssdeviations.com **Tags:** data-science, data-analysis, statistics, r ### 3. The Data Science Podcast **Author:** Berkeley Data Science Interview podcast from UC Berkeley's Data Science Undergraduate Studies program in which educators and practitioners discuss how data science is taught, curriculum design and career paths, giving listeners a view of how the field and its education are evolving. **Difficulty:** Beginner | **Language:** en | **Price:** Free **Link:** https://datascienceeducation.substack.com/podcast **Tags:** data-science-education, curriculum-design, data-science-careers, interviews ## Books ### 1. Practical Statistics for Data Scientists **Author:** Peter Bruce, Andrew Bruce A concise statistics book written for programmers that covers sampling, experimental design, A/B testing, regression, classification, and resampling, with R code throughout. Readers learn which statistical ideas matter for data science and how sampling and study design shape the conclusions data can support. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/1491952962?tag=edmonddante07-20 **Tags:** applied-statistics, data-science, ab-testing, regression, resampling, r-programming ### 2. Python for Data Analysis **Author:** Wes McKinney Written by the creator of pandas, this book teaches practical data wrangling in Python: loading, cleaning, reshaping and aggregating tabular data with pandas, NumPy and IPython. Readers finish able to handle real datasets end to end. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/1449319793?tag=edmonddante07-20 **Tags:** books, technology-computer-science, ai-ml ### 3. R for Data Science **Author:** Hadley Wickham, Garrett Grolemund Wickham and Grolemund's introduction to the tidyverse workflow in R: importing, tidying, transforming, visualising and modelling data. After working through it you can carry a dataset from raw file to reproducible report using dplyr, ggplot2 and RMarkdown. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/1491910399?tag=edmonddante07-20 **Tags:** books, technology-computer-science, data-science--ai ### 4. The Data Science Handbook **Author:** Field Cady Field Cady's survey of the working data scientist's toolkit, spanning Python and R programming, statistics, machine learning algorithms, big-data systems and the softer craft of framing problems and communicating results to non-technical stakeholders. **Difficulty:** Intermediate | **Language:** English | **Price:** Paid **Link:** https://www.amazon.com/dp/1119092949?tag=edmonddante07-20 **Tags:** books, technology-computer-science, ai-ml --- *This content is part of Dantes.io - Your Treasure Map to Knowledge* *Curated by humans at Dantes.io. Personal study use welcome; republishing this curation requires permission (team@dantes.io).* View this page online: https://dantes.io/subject/data-science