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

Learn the best programming languages and platforms for website and web app development

Back End

Python

Preface

While data science is not a new field, demand for people with data science skills is soaring, and it’s drawing people in from many other disciplines. Software engineers, in particular, might like to consider transitioning into data science, as many skillsets are shared between the two disciplines. For example, data engineers should be well versed in programming languages like SQL, which allow them to build, maintain and secure big data. They should also be very well versed in logical thinking. Indeed, most data engineers have been software engineers at some time in their careers and have moved on to specializing in data engineering.

How to Transition from Software Development to Data Science

Excel is a spreadsheet—or worksheet—used by many data-driven companies for data analysis and other statistical tasks. A spreadsheet or worksheet program is made up of rows and columns and is mostly used by data scientists to sort, organize and calculate integer and float data types. Just like Pandas , a spreadsheet program like Excel makes it possible to perform data analysis using mathematical formulas.

Pandas for Excel Users

Picture this scenario: you work for a data-driven organization whose main focus is to use data collected from different sources to make insightful decisions. Your data engineers have built a pipeline that retrieves a stream of information from different data sources. They paste that data into a CSV file and transfer it to an external storage service such as Amazon S3 .

Big Data in Python: An Introduction to PySpark

Nowadays, 95% of data processing with Python involves the use of Pandas. Pandas has been around for quite a while now. It has a huge number of functions for manipulating all sorts of data, and it’s been accepted and adopted by the community. It’s the go-to Python library when it comes to handling data.

How to Dramatically Speed Up Pandas

In this guide, we’ll break down the main resources needed in a data scientist toolbox, showcasing some of the best tools on offer as we cover an array of data science use cases.

Top 10 Tools for a Data Scientist

A significant portion of the data that’s generated today is unstructured. Unstructured data includes social media comments, browsing history, and customer feedback. Have you found yourself in a situation with a bunch of textual data to analyze, and no idea how to proceed? Natural language processing in Python can help.

Getting Started with Natural Language Processing in Python

An ensemble is defined as “a group of items viewed as a whole rather than individually”. You might have also heard of an ensemble cast in movies or a musical ensemble, where a group of artists working together produces something more enjoyable than what any of them could produce individually.

Data Science: Tools & Skills

Details

Description

Creators

Mike Aboagye

Cláudio Ribeiro

Lucero Del Alba

Muhammad Jarir Kanji

Content

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Frequently Asked Questions

Data Science: Tools & Skills