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IBM: Guided Project: Get Started with Data Science in Agriculture

Ideal for beginners with some knowledge of Python and statistics, in this one-hour hands-on guided project, you will learn how to use essential Python tools for statistical analysis of agricultural data and present that data on interactive maps

Guided Project: Get Started with Data Science in Agriculture
1 weeks
1 hours per week
Self-paced
Progress at your own speed
Free
Access to course at no cost

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Starts Apr 17
Ends Apr 30
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Starts Apr 30
Enroll
Guided Project: Get Started with Data Science in Agriculture

About this course

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Data science tools have revolutionized the way farmers and agricultural professionals approach their work. Python data analysis tools, such as pandas and seaborn, enable farmers to make data-driven decisions using soil, water, and economic data accounts. Pandas is a Python library used to simplify handling large sets of data. Seaborn is a data visualization library used to quickly create graphs.

This hands-on guided project will prepare you to handle agricultural datasets using these Python tools. You will develop job-ready skills, like how to download, prepare, analyze, and visualize data using Python libraries, including pandas and seaborn. You will learn how to build a trend line in order to forecast future trends, and finally, you will learn how to create interactive maps which show data change over time.

You will be provided with access to a Cloud-based IDE, which has all of the required software, including Python, pre-installed. All you need is a recent version of a modern web browser to complete this project.

At a glance

  • Institution: IBM
  • Subject: Computer Science
  • Level: Intermediate
  • Prerequisites:

    For this project, you will need:

    • Basic Python, pandas, and seaborn skills
    • Fundamental statistics knowledge
    • Essential Plotly skills
  • Language: English
  • Video Transcript: English
  • Associated skills:Trend Line, Data-Driven Decision-Making, Soil Science, Data Science, Data Analysis, Python (Programming Language), Data Visualization, Web Browsers, Seaborn, Statistical Analysis, Python Tools For Visual Studio, Agriculture, Pandas (Python Package)

What you'll learn

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After completing this project, you will be able to:

  • Read a CSV file
  • Convert the CSV file to a DataFrame
  • Preprocess the data
  • Perform statistical analysis of the data and display various summary statistics
  • Visualize data using pandas and seaborn
  • Build interactive maps using Plotly

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