Data science is a key part of the world of STEM. You may have heard it described as the “sexiest job of the twenty-first century”. In this collection of tutorials, we’ll take a look at the field of data science, the key skills, and provide an introduction to using Python and R for data science.

Data Science: An Introduction

Back End

Python

This book is a collection of in-depth guides to some some of the tools most used in data science, such Pandas and PySpark, as well as a look at some of the skills you'll need as a data scientist.

Data Science: Tools & Skills

In this tutorial, we’ll use the coronavirus pandemic as a focus for a mashup of data science practices—everything from collection to exploration of the data, and also covering visualization and testing of hypotheses. We’ll learn a lot not only about the process but also about the outbreak itself.

Using Data Science to Chart a Pandemic

Turning data into information, better decisions, and stronger organizations

The Real Work of Data Science

Python for Data Science: an Introduction to Pandas

How to Transition from Software Development to Data Science

Appendix E: Recent Technical Advances in Data Science

Programming Interviews Exposed

Data Science, Random Numbers, and Statistics

Solutions Architect’s Handbook, 2nd Edition

Machine Learning Architecture

Working with data science and ML

Building a Customer Service Chatbot with GPT-3: A Step-by-Step Guide

What’s Coming Ahead in NLP

Wu Dao (meaning “Enlightenment” in Chinese) (“ official site ”, Wikipedia ) is developed by the Beijing Academy of Artificial Intelligence (BAAI), and calling it an “NLP model” would be a gross understatement, given that, as a multimodal model, it can understand and generate not only text but also photorealistic images (see GPT-3 Scared You? Meet Wu Dao 2.0: A Monster of 1.75 Trillion Parameters by Towards Data Science).

Building and Analyzing Recommender Systems with the Surprise Library

Preface

About the Author

Lucero is a programmer and entrepreneur with a feel for Python, data science and DevOps. Raised in Buenos Aires, Argentina, he's a musician who loves languages (those you use to talk to people) and dancing.

C++20

Reader Testimonials

The Python Book

Getting Started

Aside from the Anaconda navigator we have over 250 open‐source data science and machine learning packages are automatically installed. You can also make use of the conda installer to install over 7500 packages easily into Python. A full list of packages that come with Anaconda is available for the relevant operating system from https://repo.anaconda.com/pkgs/ . Details on the using the conda installer is available from https://docs.anaconda.com/anaconda/user-guide/tasks/install-packages/ however this is outside the scope of this book. The last concept we will raise but not cover in detail is that of virtual environments. This concept is where the user develops in an isolated Python environment and adds packages as needed. It is a very popular approach to development however as this book is aimed at beginners we use all packages included in the Anaconda installation.

Job Ready Python

Diving Deeper into Data Structures: Sets

INTERSECTING SETS

In contrast to the difference() method, the intersection() method enables our ERP banking application to find the same values in different lists. This is useful in analysis to determine relevant data points and begin to match on key values to create joins and unions (a type of data connection) in data science. We can use this in Listing 11.22 to determine if the customer has a local bank branch.

The Self-Taught Computer Scientist: The Beginner's Guide to Data Structures & Algorithms

What Is an Algorithm?

Cubic Time

While two nested loops are a sign of quadratic time complexity, having three nested loops running from 0 to n means that the algorithm will follow cubic time. You will most likely encounter cubic time complexity if your work involves data science or statistics.

Logistic Regression Using Real-world Data

Exploratory Data Analysis

While EDA is not the focus of this tutorial, EDA and feature engineering are nonetheless critical first steps in any data science endeavor. Creating the right features can have a marked impact on the quality of our model, so for this next section, we’ll focus on a brief analysis of the dataset and see if we can tease out some relevant features.

Predicting Time Series Data

Prophet

Prophet is an R and Python tool developed by Facebook’s Core Data Science team and released as open-source software .

Becoming a Data Head

Index

G Galton, Francis, 195 Gambler's fallacy, 74–75 GANs (Generative Adversarial Networks), 189 Garber, Alan (economist and physician) "Data Science: What the Educated Citizen Needs to Know," 23 Generative Adversarial Networks (GANs), 189 Google, 168 GPUs (graphical processing units), 179 gradient boosted trees, 143–145 graphical processing units (GPUs), 179 grayscale images, 181 guessing, 66 guiding questions, 53 

Sentiment Analysis with Python

Installing Required Libraries

Pandas : Python data analysis and manipulation library. It’s one of the most popular data science libraries in the world. It’s the library of choice for many data scientists as a result of its versatility in data handling. We’ll be using it to load, manipulate, and view our data. NLTK : the Natural Language Toolkit library. This is a suite of libraries used for natural language processing in Python. We’ll be using it for lemmatization, removing stop words, and tokenization. scikit-learn : this is a popular machine learning library for Python. We’ll use it to vectorize our data and create our models. 

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