The sexiest profession of the century
Dear reader, if you are reading this article, it is because you are passionate about statistics, mathematics, technology and above all data. In particular, you have realised that data are the ‘oil’ of the century and, if you are good at interpreting them and extrapolating the right information from them, you could help the world of research and companies discover valuable information.
Knowing how to choose the right data and find the right relationships between them, to make classifications or predictions, in any field in which it is required is a very difficult job that could give great satisfaction and makes the Data Scientist one of the coolest professions of the moment.
If you want to go down this road, you have to study hard and you need the right tools.
Below are five books that could help you to be a good data scientist.
1: First you have to know a programming language well, and these days the most used by data scientists is Python
Learning Python: Powerful Object-Oriented Programming
This tutorial, by Mark Lutz, is a true best seller for anyone wanting to get started with Python and will get you started with Python 2.7 and 3.3, the latest versions of the 3.X and 2.X lines, plus all the other versions in use today.
In addition to the basics in Python, such as
numbers, lists and dictionaries you can
Create and process objects with Python statements
Use functions to avoid code redundancy and package code for reuse
Organize statements, functions, and other tools into larger components with modules
Classes: Python’s object-oriented programming tool for structuring code
Learn advanced Python tools, including decorators, descriptors, metaclasses and Unicode processing.
2: You need to learn how to use machine learning algorithms
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
A real bestseller by Raschka and Mirjalili, a comprehensive guide to machine learning and deep learning with Python.
Packed with clear explanations, with this book on machine learning, you will learn the principles behind machine learning, allowing you to build models and applications on your own.
In addition to TensorFlow 2.0 and the Keras API, as well as scikit-learn, state-of-the-art reinforcement learning techniques based on deep learning are explained, as well as an introduction to GANs.
It also mentions natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.
Really clear and useful for machine learning with Python, both for beginners and experienced developers.
-structures, models and techniques that enable machines to ‘learn’ from data
-scikit-learn for machine learning and TensorFlow for deep learning
-Image classification, sentiment analysis, intelligent web applications and more
- Neural networks, GANs and other models
-Predicting continuous results using regression analysis
-NLP and sentiment analysis
3.Deep Learning : Ian Goodfellow, Yoshua Bengio , Aaron Courville
Written by three experts in the field, this is a comprehensive book introducing a wide range of topics in deep learning.
It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularisation, optimisation algorithms, convolutional networks, sequence modelling and practical methodology.
Can you see many applications such as:
NLP, speech recognition, computer vision, recommendation systems, bioinformatics and video games.
Deep Learning has an academic focus and can be used by university students or professionals in the field.
You have to be good at juggling the cloud services world between different platforms, for example: AWS and Google Cloud Platform
4.Machine Learning with AWS: Explore the power of cloud services for your machine
Great book to start with if you are a beginner interested in learning useful artificial intelligence (AI) and machine learning skills using Amazon Web Services (AWS), the most popular and powerful cloud platform.
Machine Learning with AWS: Explore the power of cloud services for your machine learning and…
Scopri Machine Learning with AWS: Explore the power of cloud services for your machine learning and artificial…
Deploy Machine Learning Models to Production: With Flask, Streamlit, Docker, and Kubernetes on Google Cloud Platform
Very good for people who want to move to the next level of machine learning by taking pre-built models and deploying them in production. It also offers guidance to people who want to go beyond Jupyter notebooks to train models at scale on cloud environments.
-Building machine learning models at scale using Kubernetes
-Running any machine learning model on any Docker
-Learning to use Flask and Streamlit frameworks
I hope you have found my reading recommendations useful and that through these books you can become a good data scientist.
If you have other books to recommend, please do so, it will be a pleasure to know new sources to learn and deepen these topics.