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Guided Project

Get Started with Data Science in Agriculture

Yaroslav Vyklyuk

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.

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What's inside

Learning objectives

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

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

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides hands-on training in data analysis tools, such as pandas and seaborn, making it an excellent resource for beginners

Prepares learners for the job market by developing their skills in data handling, analysis, and visualization using industry-standard tools

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Guided Project: Get Started with Data Science in Agriculture with these activities:

Review Python data analysis tools tutorial

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Refresh your understanding of Python data analysis tools before the course begins to build a solid foundation.

Browse courses on Pandas

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Access the pandas getting started tutorial: <link>
Access the seaborn documentation page: <link>

Complete DataCamp's Pandas Intro exercises

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Reinforce your understanding of data handling by completing exercises that focus on importing, cleaning, and manipulating datasets using Pandas.

Browse courses on Pandas

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Register for a free DataCamp account: <link>
Complete the 'Importing Data with Pandas' exercise.
Complete the 'Cleaning Data with Pandas' exercise.

Follow the official Seaborn tutorial

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Familiarize yourself with the capabilities of Seaborn by following a step-by-step tutorial that covers essential data visualization techniques.

Browse courses on Seaborn

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Visit the official Seaborn tutorial page: <link>
Follow the 'Getting Started' section.

Four other activities

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Explore the Plotly Express documentation

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Enhance your knowledge of creating interactive maps by reviewing Plotly Express documentation.

Browse courses on Plotly Express

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Go to the Plotly Express documentation: <link>
Explore the examples section to learn about various map types.

Develop a data analysis project using Pandas and Seaborn

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Apply your skills by creating a data analysis project that incorporates data handling, visualization, and insights generation.

Browse courses on Pandas

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Identify a small dataset that aligns with your interests.
Use Pandas to import and preprocess the data.
Employ Seaborn to visualize and explore the data.
Generate insights and summarize your findings in a short report.

Create a blog post or article on using Python data analysis tools in agriculture

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Solidify your understanding by creating a blog post or article that shares your knowledge of Python data analysis tools in the context of agriculture.

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Identify a specific topic within agricultural data analysis that you would like to explore.
Research and gather relevant information on the topic.
Organize your content into a logical and engaging format.
Publish your blog post or article on a platform or website.

Mentor a junior student or a peer who is interested in agricultural data analysis

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Enhance your knowledge by sharing it with others through mentoring.

Browse courses on Mentoring

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Identify a mentee who is interested in agricultural data analysis.
Meet regularly to provide guidance and support.

Career center

Learners who complete Guided Project: Get Started with Data Science in Agriculture will develop knowledge and skills that may be useful to these careers:

Agricultural Data Scientist

Agricultural Data Scientists are responsible for collecting, cleaning, and analyzing data to improve agricultural practices. By working with data sets and agronomic data, they make recommendations which improve crop yield and reduce operating costs. This course would be particularly relevant to someone wanting to become an Agricultural Data Scientist as it covers the essential tools like Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Agricultural Data Scientist

Data Analyst

Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make better decisions. They work with a variety of data sources, including structured and unstructured data, and use a variety of tools and techniques to analyze data and identify trends. This course would be beneficial for someone wanting to become a Data Analyst as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Data Analyst

Data Scientist

Data Scientists are responsible for developing and implementing data-driven solutions to business problems. They work with a variety of data sources, including structured and unstructured data, and use a variety of tools and techniques to analyze data and identify trends. This course would be beneficial for someone wanting to become a Data Scientist as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for developing and implementing machine learning models to solve business problems. They work with a variety of data sources, including structured and unstructured data, and use a variety of tools and techniques to develop and deploy machine learning models. This course would be of interest to someone wanting to become a Machine Learning Engineer as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Machine Learning Engineer

Statistician

Statisticians are responsible for collecting, analyzing, and interpreting data. They work with a variety of data sources, including structured and unstructured data, and use a variety of tools and techniques to analyze data and identify trends. This course would be beneficial for someone wanting to become a Statistician as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative Analysts are responsible for developing and implementing mathematical and statistical models to solve business problems. They work with a variety of data sources, including structured and unstructured data, and use a variety of tools and techniques to develop and deploy models. This course would be beneficial for someone wanting to become a Quantitative Analyst as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Quantitative Analyst

Business Analyst

Business Analysts are responsible for analyzing data to help businesses make better decisions. They work with a variety of data sources, including structured and unstructured data, and use a variety of tools and techniques to analyze data and identify trends. This course would be may be of interest to someone wanting to become a Business Analyst as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers are responsible for designing, building, and maintaining data pipelines. They work with a variety of data sources, including structured and unstructured data, and use a variety of tools and techniques to design and build data pipelines. This course may be of interest to someone wanting to become a Data Engineer as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Data Engineer

Software Engineer

Software Engineers are responsible for designing, developing, and maintaining software applications. They work with a variety of programming languages and tools to develop and deploy software applications. This course may be of interest to someone wanting to become a Software Engineer as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers are responsible for developing and managing products. They work with a variety of stakeholders, including customers, engineers, and marketers, to develop and launch products. This course may be of interest to someone wanting to become a Product Manager as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Product Manager

Financial Analyst

Financial Analysts are responsible for analyzing financial data to help businesses make better decisions. They work with a variety of data sources, including financial statements and market data, and use a variety of tools and techniques to analyze data and identify trends. This course may be of interest to someone wanting to become a Financial Analyst as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Financial Analyst

Consultant

Consultants are responsible for providing advice and guidance to businesses. They work with a variety of clients, including businesses, governments, and non-profit organizations, to help them solve problems and achieve their goals. This course may be of interest to someone wanting to become a Consultant as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Consultant

Sales Manager

Sales Managers are responsible for leading and managing sales teams. They work with a variety of stakeholders, including customers, sales reps, and marketing teams, to develop and execute sales strategies. This course may be of interest to someone wanting to become a Sales Manager as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Sales Manager

Project Manager

Project Managers are responsible for planning, executing, and closing projects. They work with a variety of stakeholders, including project team members, clients, and stakeholders, to develop and execute project plans. This course may be of interest to someone wanting to become a Project Manager as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Project Manager

Operations Manager

Operations Managers are responsible for planning, executing, and closing operations. They work with a variety of stakeholders, including operations team members, clients, and stakeholders, to develop and execute operations plans. This course may be of interest to someone wanting to become an Operations Manager as it provides a foundation in data analysis using Python, Pandas, and Seaborn. Learners will also gain experience in building interactive maps using Plotly, which is a valuable skill for visualizing data in a geospatial context.

See salaries and explore the career path for Operations Manager

Reading list

We've selected 12 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Guided Project: Get Started with Data Science in Agriculture.

Python for Data Analysis

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Provides a comprehensive introduction to Python data analysis, covering topics such as data cleaning, manipulation, and visualization. Especially helpful for understanding the Python libraries used in the course, including pandas and seaborn.

Guided Project

Get Started with Data Science in Agriculture

Two deals to help you save

What's inside

Learning objectives

Good to know

Save this course

Activities

Career center

Reading list

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