Learning Python Data Analysis

Methode
Logo von Simpliv LLC

Tipp: Haben Sie Fragen? Für weitere Details einfach auf "Kostenlose Informationen" klicken.

Beschreibung

Description

Analyze and understand your data with the power and simplicity of Python.

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…

Gesamte Beschreibung lesen

Frequently asked questions

Es wurden noch keine Besucherfragen gestellt. Wenn Sie weitere Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice.

Noch nicht den perfekten Kurs gefunden? Verwandte Themen: Data Science Python, Data Science, Python, Big Data und Data Analytics.

Description

Analyze and understand your data with the power and simplicity of Python.

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.

About the Author

  • Ben spent3 years working as a software engineer and team leader doing graphics processing, desktop application development, and scientific facility simulation using a mixture of C++ and python. Which, sparking a passion for software development and developmental programming has lead him to exploring state of the art projects in Natural Language Processing, Facial Detection/Recognition, and Machine Learning.

Basic knowledge

  • 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!

What will you 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

Werden Sie über neue Bewertungen benachrichtigt

Es wurden noch keine Bewertungen geschrieben.

Schreiben Sie eine Bewertung

Haben Sie Erfahrung mit diesem Training? Schreiben Sie jetzt eine Bewertung und helfen Sie Anderen dabei die richtige Weiterbildung zu wählen. Als Dankeschön spenden wir € 1,00 an Stiftung Edukans.

Es wurden noch keine Besucherfragen gestellt. Wenn Sie weitere Fragen haben oder Unterstützung benötigen, kontaktieren Sie unseren Kundenservice.

Bitte füllen Sie das Formular so vollständig wie möglich aus

Anrede
(optional)
(optional)
(optional)
(optional)
(optional)

Haben Sie noch Fragen?

(optional)
Damit Ihnen per E-Mail oder Telefon weitergeholfen werden kann, speichern wir Ihre Daten.
Mehr Informationen dazu finden Sie in unseren Datenschutzbestimmungen.