Python features numerous numerical and mathematical toolkits such as: Numpy, Scipy, Scikit learn and SciKit, all used for data analysis and machine learning. With the aid of all of these, Python has become the language of choice for data scientists for data analysis, visualization, and machine learning.
This video aims to teach Python developers how to perform data analysis with the language by taking advantage of the core data science libraries in the Python ecosystem. The learning objective for viewers is to understand how to locate, manipulate, and analyse data with Python, with the ability to analyse large and small sets of data using libraries such as Numpy, pandas, IPython and SciPy.
This is a two part series. The first series is focused on getting and manipulation sizeable amounts of data using modern techniques. The second series is focused on advanced analysis of the data to include modern machine learning techniques.
What You Will Learn
- Advanced and recommend software engineering development practices
- How to scrape the twitter stream to collect real time data
- Smart storing of data using advanced abstractions and Object-Oriented programming
- Easy and practical data manipulation techniques for dealing with large volumes of data
- Natural Language Processing tools, special designed for working with sentences and other forms of textual data
- Predictive methods that can forecast and predict future trends based on current data
- Data analytics techniques to tease out unseen data relationships
- Dashboard application development to help share and monitor your progress/analysis
This video appeals to Python developers who want to be capable of performing core data analysis tasks with Python's libraries and tools, including data retrieval, cleaning, manipulation, visualization and storage. Those who want to handle large sets of structured and unstructured data, and discovering and delivering insight with various forms of analysis will find this course spot-on!
About The Author
Benjamin Hoff: Benjamin Hoff is a Mechanical Engineer by education, he has spent the first 3 years of his career doing graphics processing, desktop application development, and facility simulation using a mixture of C++ and python under the tutelage of a professional programmer. After rotating back into a mechanical engineering job, Benjamin has continued to develop software utilizing the skills he developed during his time as a professional programmer.
Table of contents
- Chapter 1 : Getting Started with Python
- Chapter 2 : Numerical Computing with Pandas
- Chapter 3 : Scientific Computing with NumPy/SciPy
- Chapter 4 : Presenting stories via simple visualizations
- Chapter 5 : Using the NLTK Package
- Chapter 6 : Getting insights from tweets
- Title: Learning Python Data Analysis
- Release date: March 2017
- Publisher(s): Packt Publishing
- ISBN: 9781785880711
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