Practical Python Data Science Techniques [Video]
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.
This course takes a practical approach to Data Science, presenting solutions for common and not-so-common problems in the form of recipes. This video will begin from exploring 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. The audience will 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). They will learn how to perform text preprocessing steps that are necessary for every text analysis applications. Specifically, the course will cover tokenization, stop-word removal, stemming and other preprocessing techniques.
The video takes you through with machine learning problems that you may encounter in your everyday use. In the end, the video will cover the time series and recommender system. By the end of the video course, you will become an expert in Data Science Techniques using Python.Style and Approach
A comprehensive course packed with step-by-step instructions, working examples, and helpful advice on Data Science Techniques in Python. This comprehensive course is divided into clear bite size chunks so you can learn at your own pace and focus on the areas that interest you the most.
|Course Length||2 hours 32 minutes|
|Date Of Publication||30 Aug 2017|
|Loading Data into Python|
|A New Data Set – Exploratory Analysis|
|Getting Data in the Right Shape – Preprocessing and Cleaning|
|Regression Analysis – Predicting a Quantity|
|Binary Classification – Predicting a Label (Out of Two)|
|Multi-Class Classification - Predicting a Label (Out of Many)|
|Cluster Analysis – Grouping Similar Items|