The best available books for Data Analysis Data Science and getting the most profound insights into data
Data analysis is one of the fundamental know hows of anyone interested in Machine Learning, both as a hobby or as a career.
Some books have the goal of getting you comfortable with Python while at the same teaching you the basics of Data Science and Machine Learning. It is very important to be familiar with the most used Python Libraries for these fields: Pandas, Numpy, Matplotlib, Skelarn, Keras….
These Data analysis books will teach you just how to do that, while also explaining some of the basics of Machine Learning, Probability and Statistics. Check them out!
Python Data Science Handbook is a reference manual and learning resource that teaches its readers statistical and analysis methods crucial to data science. You will learn how to do Exploratory Data Analysis (one of the most important steps of a Machine Learning project, where we get insights from the data), using the three most well known Python Libraries for this: Pandas, Numpy, and Matplotlib. Read the full review.
Python for Data Analysis is a book that has the goal of getting its readers super-comfortable playing around with structured data in Python. It explains how to manipulate, process, clean, and efficiently crunch data in Python using the most well known libraries for this: Numpy and Pandas. Read the full review.
Data Science and Machine Learning are belief to be magic by many, some sort of whimsical field for mathematicians and geniuses. However, this is far from being true. Data Science can be easy, if you know how to do it right, and almost anyone can do it.
The goal of Data Smart is to show that anybody that is willing to learn a few concepts can do Data Science, even with no programming or complex software knowledge. How? you might ask. The answer is simple and controversial at the same time: using the worldwide known and familiar environment of a spreadsheet. Read the full review.
Data Science from scratch is one of the top books out there for getting started with Data Science. It’s second edition has recently been published, upgrading and improving the content of the first one. Lets see what this awesome book offers.
In our opinion, it is one of the best Data analysis books for those that have a little programming knowledge, feel confortable with statistics, and want to get introduced in a swift and painless manner to the Data Science world. Read the full review.
Practical Statistics for Data Scientist is a book that is very well defined by its name: it is a very hands-on book to learn the most important statistical concepts and tools used in the data science world.
For people that are already confident with statistics most of the topics will be a bit familiar, however, the book still brings some fresh perspectives and insights–especially on helping gain a solid (step-by-step) grasp of common algorithms and models in the data science toolkit. For programmers with little statistical knowledge, this book will teach them what statistics are all about, and how they can be used to leverage the true power of their data.
You will learn to perform exploratory data analysis like a pro, how to sample properly, how to answer questions from an statistical perspective, how to use regression to predict outcomes and detect outliers, and some statistical supervised and non supervised Machine Learning methods. Find the full review here.
Bayesian Statistics the fun way offers a delightful and fun read for those looking to make better probabilistic decisions using unusual and highly illustrative examples. It will give you a complete understanding of Bayesian Statistics through simple explanations and amusing examples that go from calculating how likely Han Solo is to survive a flight through an asteroid field to calculating the probability of seeing an UFO. Find the full review here!
Probability for the Enthusiastic Beginner, along with the book just on top, is one of the best introductory probability books out there. With a very little mathematical background, almost everybody can get started into the wonderful world of probability theory with this pedagogical and easy to read book. Compared to Bayesian Statistics: The Fun way, it contains more challenging solved exercises. Check out the full review!
Statistical Rethinking is one of the best statistics books to start with if what you are looking for is going deeper than just the theory, and actually learning the scripting and programming that is actually needed to implement these model-based statistics.
The author will take you by the hand and guide you step by step in this process using the R programming language, combining both great theoretical explanations and neatly explained code examples, while throwing strokes of humour from time to time to make this book a delightful read. Find the full review here!
Think stats will teach you how perform statistical analysis computationally and apply descriptive statistics in Python, with very little mathematical baggage.
It will take you through the entire process of exploratory data analysis and empirical probability in Python: from collecting data and generating different descriptive statistics in Python to identifying patterns and testing hypothesis. You’ll explore distributions, rules of probability, visualisation, and many other tools and concepts. Find the full review here!
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