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Data Science - Visualizing Data and Exploring Models

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Data Science - Visualizing Data and Exploring Models
  • Description
  • Outcome
  • Certification
  • In this free online course Data Science - Visualizing Data and Exploring Models you will learn about applying visualizations to display your data and about feature engineering and constructing machine learning models.

    The course begins by introducing you to exploratory data analysis and data visualization. You will learn about the functions of exploratory data analysis and what factors can affect your views of data. You will learn about the different types of charts you can use to visualize your data, and also about the libraries and packages available for R and Python for visualizing your data.

    Next, you will be introduced to building models, including feature engineering and constructing models. The course describes what feature engineering is and how to construct a machine learning model in R and Python. You will learn about evaluating your model, and how to evaluate a model in Azure ML, R and Python.

    This free Alison Course will be of great interest to those who wish to learn and expand their knowledge of data science practices and procedures.

    Prerequisites: To complete this course successfully you need a basic knowledge of mathematics, including linear algebra. Additionally, some programming experience, ideally in either R or Python, is assumed and you will need to have completed the previous courses 'Introduction to Data Science' and 'Data Science - Working with Data'.

  • Having completed this course you will be able to:
    - Discuss the importance of data exploration.
    - Describe what you use for data visualizations in R.
    - Describe what you use for data visualizations in Python.
    - List the different types of plots or charts you can use to visualize your data.
    - Discuss the process of feature engineering.
    - Describe features and methods available in R for creating Machine learning models.
    - Describe features and methods available in Python for creating Machine learning models.
    - Describe the process and options for evaluating your machine learning model.

  • All Alison courses are free to study. To successfully complete a course you must score 80% or higher in each course assessments. Upon successful completion of a course, you can choose to make your achievement formal by purchasing an official Alison Diploma, Certificate or PDF.

    Having an official Alison document is a great way to celebrate and share your success. It is:

    • Ideal to include with CVs, job applications and portfolios
    • A way to show your ability to learn and achieve high results

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