Analyze and understand your data with the power and simplicity of Python
About This Video
Learn Data Analysis using modern processing techniques with Numpy, SciPy, and Pandas
Apply Natural Language Algorithms other Machine Learning Algorithms to provide unique and interesting insights
Present Insights quickly and cleanly through the use of a Dashboard Application
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.
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!
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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