Title: Data Analysis with Python
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Data Analysis with Python

University of Helsinki via Independent

Description

About this courseIn this course an overview is given of different phases of the data analysis pipeline using Python and its data analysis ecosystem. What is typically done in data analysis? We assume that data is already available, so we only need to download it. After downloading the data it needs to be cleaned to enable further analysis. In the cleaning phase the data is converted to some uniform and consistent format. After which the data can, for instance, be

  • combined or divided into smaller chunks
  • grouped or sorted,
  • condensed into small number of summary statistics
  • numerical or string operations can be performed on the data
The point is to manipulate the data into a form that enables discovery of relationships and regularities among the elements of data. Visualization of data often helps to get a better understanding of the data. Another useful tool for data analysis is machine learning, where a mathematical or statistical model is fitted to the data. These models can then be used to make predictions of new data, or can be used to explain or describe the current data.

What you will learn
  • Python programs
    • After the course, you can confidently write basic level Python programs without constantly consulting language/library documentation.
  • Machine learning types
    • After the course, you will know the main types of machine learning: supervised learning: regression and classification, unsupervised learning: clustering, dimensionality reduction, (density estimation)
  • Data analysis projects
    • After the course, you can apply basic data analysis skills to a simple project on an application field

Data Analysis with Python

University of Helsinki
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Affiliate notice

About this courseIn this course an overview is given of different phases of the data analysis pipeline using Python and its data analysis ecosystem. What is typically done in data analysis? We assume that data is already available, so we only need to download it. After downloading the data it needs to be cleaned to enable further analysis. In the cleaning phase the data is converted to some uniform and consistent format. After which the data can, for instance, be
  • combined or divided into smaller chunks
  • grouped or sorted,
  • condensed into small number of summary statistics
  • numerical or string operations can be performed on the data
The point is to manipulate the data into a form that enables discovery of relationships and regularities among the elements of data. Visualization of data often helps to get a better understanding of the data. Another useful tool for data analysis is machine learning, where a mathematical or statistical model is fitted to the data. These models can then be used to make predictions of new data, or can be used to explain or describe the current data.

What you will learn
  • Python programs
    • After the course, you can confidently write basic level Python programs without constantly consulting language/library documentation.
  • Machine learning types
    • After the course, you will know the main types of machine learning: supervised learning: regression and classification, unsupervised learning: clustering, dimensionality reduction, (density estimation)
  • Data analysis projects
    • After the course, you can apply basic data analysis skills to a simple project on an application field

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