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.
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.
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.
This video discusses the most common steps that are required to get the data in the right shape, including preprocessing and cleaning.
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).
This video discusses the process of removing stop-words (unimportant words) and punctuation from a list of tokens.
This video introduces the most common steps for text normalization that is the process of transforming a token into its canonical form.
This video discusses how to calculate word frequencies within documents and across a whole collection, and how to read.
This video introduces scikit-learn as the main library for machine learning.
This video introduces regression analysis as the problem of predicting a quantity, or a continuous variable, using scikit-learn.
This video introduces binary classification as the problem of assigning a label to an item, out of two possible labels.
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.
This video introduces clustering as the problem of grouping together similar items to find hidden structure in our data.
This video discusses how to analyze time series data using Pandas, observing seasonality and understanding the general trend of a series.
This video discusses recommender systems and how to implement a movie recommendation engine using collaborative filtering.
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.