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Leveraging Online Resources for Python Analytics

Janani Ravi
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This course is no longer available. Find something similar by browsing:
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Read about what's good
what should give you pause
and possible dealbreakers
Suitable for data science practitioners looking to enhance their skillset with online resources and libraries
Provides resources that can assist in framing and solving problems effectively
Helps learners understand the benefits and applications of BigML for building machine learning models
Introduces Google Colab as a free and accessible platform for building models
May be suitable for individuals seeking to advance their knowledge in data science analytics
Provides an overview of popular visualization libraries, machine learning frameworks, and cloud-based solutions

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Learners who complete Leveraging Online Resources for Python Analytics will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use scientific methods and processes to extract knowledge and insights from data. This course will help Data Scientists learn about the tools and technologies used to build and deploy machine learning and deep learning models, including visualization libraries, cloud-based solutions, and tools for building ML models that abstract away much of the underlying complexity, such as BigML.
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Data Visualization Specialist
Data Visualization Specialists are responsible for creating visualizations that communicate data effectively. This course will help Data Visualization Specialists learn about the tools and technologies used to create data visualizations, such as visualization libraries and cloud-based solutions.
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Machine Learning Researchers are responsible for developing new and innovative machine learning algorithms and techniques. This course will help Machine Learning Researchers learn about the tools and technologies used to build and deploy ML models, such as machine learning frameworks and cloud-based solutions.
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Deep Learning Engineer
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Machine Learning Engineer
Machine Learning Engineers are responsible for developing and deploying machine learning and deep learning models to solve real-world problems. This course will help Machine Learning Engineers learn about new and innovative tools and technologies for building and deploying ML models, such as BigML and Google Colab.
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Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. This course will help Statisticians learn about the tools and technologies used to analyze data and build data-driven models, such as visualization libraries and machine learning frameworks.
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Business Intelligence Analyst
Business Intelligence Analysts are responsible for providing business insights to help organizations make informed decisions. This course will help Business Intelligence Analysts learn about the tools and technologies used to analyze data and build data-driven solutions, such as visualization libraries and cloud-based solutions.
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Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze data and help make investment decisions. This course will help Quantitative Analysts learn about the tools and technologies used to analyze data and build data-driven models, such as visualization libraries, machine learning frameworks, and cloud-based solutions.
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Data Science Manager
Data Science Managers are responsible for leading and managing data science teams. This course will help Data Science Managers learn about the tools and technologies used in data science, such as visualization libraries, machine learning frameworks, and cloud-based solutions.
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Cloud Architect
Cloud Architects are responsible for designing and building cloud-based solutions. This course will help Cloud Architects learn about the tools and technologies used to build and deploy cloud-based solutions, such as visualization libraries, machine learning frameworks, and cloud-based solutions.
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Big Data Architect
Big Data Architects are responsible for designing and building big data systems. This course may be useful for Big Data Architects looking to learn about the tools and technologies used to analyze large datasets, such as visualization libraries, machine learning frameworks, and cloud-based solutions.
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Data Analyst
Data Analysts discover, clean, and analyze data to provide insights and help organizations make informed decisions. This course may be useful for those looking to learn about the tools and technologies used by Data Analysts, such as visualization libraries, machine learning frameworks, and cloud-based solutions.
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Data Engineer
Data Engineers build and maintain the infrastructure and tools used to store, process, and analyze data. This course may be useful for Data Engineers looking to learn about the tools and technologies used in data engineering, such as visualization libraries, machine learning frameworks, and cloud-based solutions.
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Software Engineer
Software Engineers are responsible for designing and building software applications. This course may be useful for Software Engineers looking to learn about the tools and technologies used in data science and machine learning, such as visualization libraries and machine learning frameworks.
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Product Manager
Product Managers are responsible for managing the development and launch of new products. This course may be useful for Product Managers looking to learn about the tools and technologies used in data science and machine learning, such as visualization libraries and machine learning frameworks.
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Course image
Time
2 hours
Via
Pluralsight
Instructor
Janani Ravi
This course belongs to a collection called Python for Data Analysts. It's comprised of 11 courses:
  1. Building Your First Python Analytics Solution
  2. Create and Share Analytics with Jupyter Notebooks
  3. Python for Data Analysts
  4. Programming Python Using an IDE
  5. Leveraging Online Resources for Python Analytics (viewing)
  6. Importing Data: Python Data Playbook
  7. Cleaning Data: Python Data Playbook
  8. Pygal 2: Python Data Playbook
  9. Understanding Databases with SQLAlchemy 1: Python Data Playbook
  10. Web Scraping: Python Data Playbook
  11. Data Wrangling with Python 3
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Suitable for data science practitioners looking to enhance their skillset with online resources and libraries
Provides resources that can assist in framing and solving problems effectively
Helps learners understand the benefits and applications of BigML for building machine learning models
Introduces Google Colab as a free and accessible platform for building models
May be suitable for individuals seeking to advance their knowledge in data science analytics
Provides an overview of popular visualization libraries, machine learning frameworks, and cloud-based solutions
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