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
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
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.