Course: Python for Data Science

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$99.00
$119.79 incl. vat

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$99.00
$119.79 incl. vat

$99.00
$119.79 incl. vat

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duration: 8 hours |

Language: English (US) |

access duration: 90 days |

In Onbeperkt Leren

Details

In this course you will get an introduction of Python and learn how to use Python for Data Science.

You will learn the components of IPython, Notebook and the NumPy module. In the second part of this course you will learn about complex data such as pandas, machine learning via SciPY operations and the scikit-learn toolset.

Among subjects that covered are Anaconda, JSON, Scipy stack, to develop a 3D plot, manage processes in data science and far more.

Result

After completing this course you are familiar with the basics and advanced techniques of Python in combination with Data Science.

Prerequisites

You are familiar with the basics of programming.

Target audience

Software Developer

Content

Python for Data Science

8 hours

Python for Data Science – Introduction to Python for Data Science

  • start the course
  • describe elements of data science and datasets with various modeling and prediction relationships
  • recognize the various pipelines in data science and the stages of the data science cycle
  • define and describe the various libraries and packages for data analysis
  • perform the key steps involved in installing Anaconda including all the necessary packages for this course
  • describe the various Python containers for data management
  • create lists, tuples, and dictionaries with Python to drive data
  • use Python list comprehensions to create lists
  • describe the IPython shell and shell commands
  • run the Jupyter Notebook and familiarize with the basics of its user interface
  • capture Python code output in Jupyter Notebook
  • run the Jupyter QT Console and familiarize with the basics of its user interface
  • use IPython to perform debugging and error management on Python code
  • basic access and usage of the NumPy package in a Python development environment
  • describe the various components of NumPy
  • describe ndarray object attributes
  • describe the various NumPy array operations applicable to data science
  • describe different ways of creating NumPy arrays
  • describe how Pandas library may be used to read and write various formats of data
  • use Pandas library to read data from a CSV file and write data out to a CSV file
  • use Python's standard JSON package to read JSON data
  • use the pandas library to generate and parse date values
  • perform data clean up by handling missing and erroneous data
  • download and load a sample dataset into Python from a URL
  • load a large dataset as smaller chunks by obtaining an iterator for the dataset
  • recognize the main concepts in data science using Python

Python for Data Science – Complex Data Engineering in Python

  • start the course
  • use pandas to describe the basic and common functionalities of pandas for Data Science
  • use pandas to describe its primary data structures
  • use pandas to describe hierarchical indexing
  • perform basic data query operations on a pandas DataFrame
  • perform aggregation operations on a pandas DataFrame
  • perform basic merge operations with pandas DataFrames
  • describe the functionality and use of core packages and sub-packages in the SciPy stack
  • use the scikit-learn library to perform basic data standardization
  • use the scikit-learn library to perform basic data normalization
  • use the scikit-learn library to perform simple linear regression analysis
  • perform supervised learning by using the scikit-learn library to perform optical recognition of hand-written digits
  • use the Python matplotlib library to plot and display a simple 2D line plot and set its line properties
  • use the Python matplotlib library to create and customize multiple plots in a single figure
  • use the Python matplotlib library to create and customize a box plot
  • use the Python matplotlib library to create and display a heat map
  • use the Python matplotlib library to place legends and annotations on a 2D line plot
  • use pandas to create a scatter plot matrix
  • use the Python matplotlib library to create a 3D plot
  • create, slice, and resample time series data in Python
  • use pandas to create and manipulate Timedeltas in Python
  • identify key concepts in Python data cleansing
  • perform data preprocessing and text mining in Python
  • use pandas to access a MySQL database
  • use the SciPy package to describe the various forms of distribution
  • manage other concepts and processes in data science

Course options

We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.

Optional practice exam (trial exam)

To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam. 

Optional practice lab

To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediatelyThe lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience. 

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