Practical Python Data Science Techniques
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Practical Python Data Science Techniques

Learn practical solutions to Data Science problems with Python
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
0 students enrolled
Created by Packt Publishing
Last updated 9/2017
English [Auto-generated]
Current price: $10 Original price: $125 Discount: 92% off
5 hours left at this price!
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  • 2.5 hours on-demand video
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • 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
View Curriculum
  • 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.

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.

About The Author

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.

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.

Who is the target audience?
  • If you are a Python programmer and looking at learning the different Data Science Techniques then this course is all you need. Basic understanding of Python concepts is all you need to get started with this video.
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Curriculum For This Course
15 Lectures
Exploring Your Data
4 Lectures 39:21

This video provides an overview of the entire course.

Preview 08:35

This video discusses how to access data from local files in different formats. The aim of the video is to understand the most common file formats used to exchange data, and how Python makes it easy to access these formats.

Loading Data into Python

This video introduces the notion of exploratory analysis and outlines some of the common steps that an analyst needs to take when dealing with a new data set.

A New Data Set – Exploratory Analysis

This video discusses the most common steps that are required to get the data in the right shape, including preprocessing and cleaning.

Getting Data in the Right Shape – Preprocessing and Cleaning
Dealing with Text
4 Lectures 40:06

This video discusses the process of breaking a string down into individual tokens or phrases, including text data from different domains (For example, social media versus general English).

Preview 11:25

This video discusses the process of removing stop-words (unimportant words) and punctuation from a list of tokens.

Stop-Words and Punctuation Removal

This video introduces the most common steps for text normalization that is the process of transforming a token into its canonical form.

Text Normalization

This video discusses how to calculate word frequencies within documents and across a whole collection, and how to read.

Calculating Word Frequencies
Machine Learning Problems
5 Lectures 42:35

This video introduces scikit-learn as the main library for machine learning.

Preview 06:22

This video introduces regression analysis as the problem of predicting a quantity, or a continuous variable, using scikit-learn.

Regression Analysis – Predicting a Quantity

This video introduces binary classification as the problem of assigning a label to an item, out of two possible labels.

Binary Classification – Predicting a Label (Out of Two)

This video extends the concepts from the previous video introducing multi-class classification as the problem of assigning a label to an item, out of many possible labels.

Multi-Class Classification - Predicting a Label (Out of Many)

This video introduces clustering as the problem of grouping together similar items to find hidden structure in our data.

Cluster Analysis – Grouping Similar Items
Time Series and Recommender Systems
2 Lectures 30:37

This video discusses how to analyze time series data using Pandas, observing seasonality and understanding the general trend of a series.

Preview 11:27

This video discusses recommender systems and how to implement a movie recommendation engine using collaborative filtering.

Building a Movie Recommendation System
About the Instructor
Packt Publishing
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8,175 Reviews
58,819 Students
687 Courses
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Packt has been committed to developer learning since 2004. A lot has changed in software since then - but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.

With an extensive library of content - more than 4000 books and video courses -Packt's mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages, to cutting edge data analytics, and DevOps, Packt takes software professionals in every field to what's important to them now.

From skills that will help you to develop and future proof your career to immediate solutions to every day tech challenges, Packt is a go-to resource to make you a better, smarter developer.

Packt Udemy courses continue this tradition, bringing you comprehensive yet concise video courses straight from the experts.