Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models.

The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages.

Jul 26, 2023 · python : 3.13.0 python-bits : 64 OS : Darwin OS-release : 24.0.0 ... Depending on the input data, this can cause the results to be inaccurate, especially for ‘float32‘ (see example below). Specifying a higher-precision accumulator using …

May 15, 2024 · Foundations of Data Science with Python . by John M. Shea. The book provides a comprehensive introduction to data science using Python, focusing on key concepts, tools, and practical applications for data analysis.

Our vehicle will be the most popular language in Data Science. Python is an easy to learn, yet extremely powerful computer language. We will use cloud-based computing negating the need to install any software one your own computer. The course has been created to jump-start your Data Science skills. As such, it is very dense with information.

Data Science with Python-Sasmita.pdf - Free download as PDF File (.pdf), Text File (.txt) or read online for free. The document provides an overview of a 45-day data science training course that teaches machine learning through hands-on projects.

“Rather than presenting Python as Java or C, this textbook focuses on the essential Python programming skills for data scientists and advanced methods for big data analysts.

First, and foremost, you'll learn how to conduct data science by learning how to analyze data. That includes knowing how to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily shareable reports.

At the end of the course, students will have a solid grasp of Python programming basics, and have been exposed to the entire data science workflow, starting from interacting with SQL databases to query and retrieve data, through data wrangling, reshaping, summarizing, analyzing and ultimately reporting results.

A STEP-BY-STEP DATA SCIENCE LEARNING PLAN You’ve probably read numerous articles telling you how to start learning data science. Collectively, they tell you to dozens of things you need to learn. Learn Python. Learn R. Learn Hadoop. They tell you all the skills you need: learn machine learning, visualization, data wrangling. Little technical