Data Science guide for beginners

What is it about

Data Science is an activity related to data analysis and finding the best solutions based on it. Previously, such tasks were dealt with by specialists in mathematics and statisticians. Then artificial intelligence came to the rescue, which made it possible to include optimization and informatics in analysis methods. This new approach has proven to be much more effective.

How is the process built? It all starts with collecting large arrays of structured and unstructured data and converting them into a readable format. Further, visualization, work with statistics and analytical methods are used - machine and deep learning, probabilistic analysis and predictive models, neural networks and their application for solving urgent problems. We are basingourarticle on Data science from scratch.

Five Key Terms to Remember

The Great Data Science Guide for Beginners: Terms, Applications, Education, and Entry into the Profession Artificial intelligence, machine learning, deep learning and data science are the main and most popular terms. They are close but not equivalent to each other. At the start, it is important to figure out how they differ.

Artificial Intelligence is a field dedicated to the creation of intelligent systems that work and act like humans. Its origin is associated with the appearance of Alan Turing machines in 1936. Despite a long history of development, artificial intelligence is not yet capable of completely replacing humans in most areas. And AI competition with humans in chess and data encryption are two sides of the same coin.

Machine learning is the creation of a tool for extracting knowledge from data. ML models are trained on data independently or in stages: training with a teacher on data prepared by a person and without a teacher - working with spontaneous, noisy data.

Deep learning is the creation of multilayer neural networks in areas where more advanced or faster analysis is required and traditional machine learning fails. "Depth" is provided by a number of hidden layers of neurons in the network that perform mathematical calculations.

Big Data - work with a large amount of often unstructured data. The specificity of the sphere is the tools and systems that can withstand high loads.

Data Science - At the heart of the field is the empowerment of data sets, visualization, gathering ideas and making decisions based on that data. Data analysts use several machine learning and Big Data methods: cloud computing, tools for creating a virtual development environment, and much more - check Python isdir for instance.

What to read

Elements of Statistical Learning by Trevor Hasti, Robert Tibshirani and Jerome Friedman - if there are many gaps left after graduation. The classic sections of machine learning are presented in terms of mathematical statistics with rigorous mathematical calculations.

Python workout: 50 ten-minute exercises is another must-read book.

Deep Learning by Ian Goodfellow. The best book on the mathematical principles behind neural networks.

Neural Networks and Deep Learning by Michael Nielsen. To familiarize yourself with the basic principles.

The Complete Guide to Mathematics and Statistics for Data Science. A cool and fun step-by-step guide to help you navigate math and statistics.

An introduction to statistics for Data Science will help you understand the central limit theorem. It covers populations, samples and their distribution, and contains useful videos.

The Complete Beginner's Guide to Linear Algebra for Data Scientists. Everything you need to know about linear algebra.

Linear Algebra for Data Scientists. An interesting article introducing the basics of linear algebra.