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Janani Ravi
As data science and data analytics become ever more popular and more specialized, the number and variety of tools and technologies out there can often seem overwhelming. In this course, Leveraging Online Resources for Python Analytics, you will gain the...
Read more
As data science and data analytics become ever more popular and more specialized, the number and variety of tools and technologies out there can often seem overwhelming. In this course, Leveraging Online Resources for Python Analytics, you will gain the...
Read more
As data science and data analytics become ever more popular and more specialized, the number and variety of tools and technologies out there can often seem overwhelming. In this course, Leveraging Online Resources for Python Analytics, you will gain the ability to find resources that can help you to correctly frame and solve your problem. First, you will survey some of the important visualization libraries, machine learning and deep learning frameworks, and cloud-based solutions out there. Next, you will discover the benefits of using a tool like BigML, which is a platform for building ML models that abstracts away much of the underlying complexity. Democratization of ML is an important trend today, and technologies like BigML are at the forefront of that trend. You will see, for instance, how BigML seamlessly integrates visualizations known as partial dependency plots, which combine the results of large numbers of ML predictions into an easily understandable form so that you can understand exactly what your ML model is doing. Finally, you will round out your knowledge by working with Google Colab, a free web-based way to build models. The models are hosted in Jupyter notebooks that reside on Google Drive and run on virtual machines in the cloud. When you’re finished with this course, you will have the skills and knowledge to quickly and efficiently identify valuable online resources and libraries that will help you on your journey as a data science practitioner.
Register for this course and see more details by visiting:
OpenCourser.com/course/l4hdv8/leveraging
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Good to know
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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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Career center
Learners who complete Leveraging Online Resources for Python Analytics will develop knowledge and skills
that may be useful to these careers:
Data Scientist
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.
Machine Learning Engineer
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.
Data Visualization Specialist
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.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers are responsible for developing and deploying deep learning models to solve real-world problems. This course will help Deep Learning Engineers learn about the tools and technologies used to build and deploy DL models, such as deep learning frameworks and cloud-based solutions.
Machine Learning Researcher
Machine Learning Researcher
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.
Quantitative Analyst
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.
Business Intelligence Analyst
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.
Statistician
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.
Cloud Architect
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.
Data Science Manager
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.
Data Analyst
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.
Data Engineer
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.
Big Data Architect
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.
Software Engineer
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.
Product Manager
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.
For more career information including salaries, visit:
OpenCourser.com/course/l4hdv8/leveraging
Reading list
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Practical guide to using Python for basic automation tasks, providing a gentle introduction to Python's core concepts and its practical applications.
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Comprehensive guide to Python's data analysis ecosystem, including NumPy, Pandas, and Matplotlib, with a focus on practical applications.
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Concise and comprehensive reference to the Python language, covering syntax, built-in functions and objects, and advanced topics.
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Deep dive into Python's advanced features, covering metaprogramming, decorators, generators, and concurrency.
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Collection of practical recipes and solutions for common Python programming tasks, covering a wide range of topics.
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Is an introductory text to Python programming, with a focus on computational thinking and problem-solving.
Comprehensive guide to the Python Standard Library, covering its vast collection of modules and their applications.
Practical guide to testing Python code using the pytest framework, covering unit testing, integration testing, and end-to-end testing.
Practical guide to using Python for bioinformatics tasks, covering sequence analysis, genome assembly, and data visualization.
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Comprehensive guide to using Python for financial analysis and modeling, covering data manipulation, financial calculations, and visualization.
This book, written by the Stanford University professor and former Chief Scientist at Baidu, provides a clear overview of machine learning with lots of diagrams and hands-on practice.
Provides a comprehensive and practical guide to deep learning, including hands-on exercises and real-world examples.
Classic text on machine learning and statistical pattern recognition, with a focus on Bayesian approaches. The author has won the prestigious Turing Award.
Provides a balanced treatment of both statistical and machine learning methods, making it accessible to a wide audience.
Practical guide to machine learning with Python, covering a wide range of topics from data preprocessing to model evaluation.
Gentle introduction to machine learning, suitable for those with no prior knowledge of the field, with a focus on understanding the concepts rather than the math.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/l4hdv8/leveraging
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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:
- Building Your First Python Analytics Solution
- Create and Share Analytics with Jupyter Notebooks
- Python for Data Analysts
- Programming Python Using an IDE
- Leveraging Online Resources for Python Analytics (viewing)
- Importing Data: Python Data Playbook
- Cleaning Data: Python Data Playbook
- Pygal 2: Python Data Playbook
- Understanding Databases with SQLAlchemy 1: Python Data Playbook
- Web Scraping: Python Data Playbook
- Data Wrangling with Python 3
Good to know
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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