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Data Science - Working with Data

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Data Science - Working with Data
  • Description
  • Outcome
  • Certification
  • In this free online course Data Science - Working with Data you will be introduced to methods for preparing data, how to differentiate between continuous and categorical variables, and what quantization and scaling involve.

    The course begins by introducing you to the data flow in Azure ML, you will learn about batch and real time processing, and the different types of joins you can use on your data. You will learn about R and Python programing languages and how they can be used in a data science project.

    Next, you will be introduced to data sampling and preparation. You will learn about continuous and categorical variables, and what quantization can do for your data. The course will teach you about data munging which is the process of manually converting or mapping data from one "raw" form into another format, and how it is the most time-consuming part of a data science project. You will also learn about handling errors and outliers in your project. Finally, you will learn about scaling using either Python, R or Azure ML module for scaling.

    This free Alison Course will be of great interest to those who wish to learn about data science.

    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 course Introduction to Data Science.

  • Having completed this course you will be able to:
    - Describe the flow of data in a Azure ML experiment.
    - Discuss the differences between using R and Python.
    - Identify which programming language suits you better R or Python.
    - Describe installing both R and Python in your Azure ML environment.
    - Discuss data preparation also known as data munging to prepare data for your project.
    - Explain what quantizing your variables is and does.
    - Explain how to deal with missing values in your data sets.
    - Describe why you should scale your variables.

  • 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 share your success. Plus it’s:

    • Ideal for including in CVs, job applications and portfolios
    • An indication of your ability to learn and achieve high results
    • An incentive to continue to empower yourself through learning
    • A tangible way of supporting the Alison mission to empower people everywhere through education.

Modules List( 3 )
  • Data Science - Working with Data
  • Data Science - Working with Data - Course Resources
  • Module 1: Data Acquisition and Programming Language for Data Science
    • Learning Outcomes
    • Data Acquisition and Flow
    • R and Python in Azure ML
    • R in Azure AL
    • Python in Azure ML
    • Lesson Summary
  • Module 2: Data Sampling and Preparation
    • Learning Outcomes
    • Data Sampling and Quantization
    • Missing and Repeated Values
    • Outliers and Errors
    • Visualizing and Handling Outliers in Scripts
    • Scaling Data
    • Lesson Summary
  • END OF COURSE ASSESSMENT
  • Module 3: Data Science - Working with Data Assessment
    • Data Science - Working with Data Assessment
Topics List ( 6 )
Module 1: Data Acquisition and Programming Language for Data Science
In this module you will be introduced to the Data flow in Azure ML, you will learn about batch and real time processing. You will learn about different types of joins you can use on your data. You will learn about R and Python programing languages and what they can do for a data science project.
Topics List ( 7 )
Module 2: Data Sampling and Preparation
In this module you will be introduced to Data sampling and Preparation. You will learn about continuous and categorical variables. You will learn what quantization is and can do for your data. You will learn about Data munging and how it’s the most time-consuming part of a data science project. You will learn about handling errors and outliers in your project. You will learn about scaling using either python, R or Azure ML module for scaling.
Topics List ( 1 )
Module 3: Data Science - Working with Data Assessment
You must score 80% or more to pass this assessment.
Course Features
  • Duration

    2-3 Hours

  • Publisher

    Channel 9

  • Video

    Yes

  • Audio

    Yes

  • Assessment

    Yes

  • Certification

    Yes

  • Price

    Free

  • Reward

    50 Pts

  • Responsive

    No

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