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Practical Python Data Science Techniques [Video]

More Information
  • Perform Exploratory data analysis on your Data
  • Clean and process your Data to have the right shape
  • Tokenize your Document to words with Python
  • Calculate the word frequencies using Data Science Techniques of Python
  • Work with scikit-learn to solve every problem in Machine Learning
  • Perform Cluster Analysis using Python Data Science Techniques
  • Build a Time Series Analysis with Panda

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.

  • Easy to follow guide that will take you from being a beginner to a regular data science task.
  • Get solutions to your common and not-so-common data science problems.
  • Highly Practical, real world examples that make data science your comfort zone.
Course Length 2 hours 32 minutes
ISBN 9781788294294
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


Marco Bonzanini

Marco Bonzanini is a data scientist based in London, United Kingdom. He holds a Ph.D. in information retrieval from the Queen Mary University of London. He specializes in text analytics and search applications, and over the years, he has enjoyed working on a variety of information management and data science problems.

He maintains a personal blog at https://marcobonzanini.com, where he discusses different technical topics, mainly around Python, text analytics, and data science.

When not working on Python projects, he likes to engage with the community at PyData conferences and meetups, and he also enjoys brewing homemade beer.