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 course 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.
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
Prior knowledge of Python Data Analysis is needed.
Who is this course intended for?
This course 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!