Learning Path: Your Guide to Learn Data Science using Python هذه الدورة تدرس باللغة الإنجليزية

تفاصيل الدورة

Python is a popular programming language, widely used in many scenarios and easy to use to use. Data Science is an interdisciplinary field that employs techniques to extract knowledge from data. As one of the fast growing fields in technology, the interest for Data Science is booming, and the demand for specialized talent is on the rise.

Packt's Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.

To start off with your learning journey, you can learn some of the fundamental tools of the trade and apply them to real data problems. And along the way, it discusses the use of Python stack for data analysis and scientific computing and expands on concepts of data acquisition, data cleaning, data analysis, and machine learning. This course then takes a practical approach to Data Science, presenting solutions for common and not-so-common problems in the form of recipes. You will then explore your data using the different methods like data acquisition, data cleaning, data mining, machine learning, and data visualization, applied to a variety of different data types like structured data or free-form text. It will show how to deal with text using different methods like text normalization and calculating word frequencies. You will then learn how to deal with data with a time dimension and how to build a recommendation system as well as about supervised learning problems (regression and classification) and unsupervised learning problems (clustering). The course will cover tokenization, stop-word removal, stemming and other preprocessing techniques.

In the end, the course will cover the time series and recommender system. By the end of this Learning Path, you will be confident performing Data Analysis using Python.

The highlights of this Learning Path are:

تحديث بتاريخ 18 February, 2018