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A Cloud Guru

There is so much data out there, just waiting to be analyzed, but have you ever wondered how much easier it could be to interpret if you just had a way to visualize these datasets in an interactive way? If only there was a tool that could help us import a data set, transform it, perform calculations, analyze and visualize it, then document these processes and steps along the way so it can be shared with others. Well, that is exactly what Jupyter Notebook will allow you to do. In this course, you’ll learn: * How to set up and use Jupyter Notebooks. * What data analytics and visualization is, its importance and how it can be harnessed. * How to take a real dataset and turn it into charts, graphs, interactive elements, predictions, etc. * How to start leveraging Machine Learning techniques to get even more from your data. * How to harness cloud service providers like AWS, Google, Azure’s hosted notebook services in your data science and machine learning lifecycles This course is made for beginners, so if you don’t know anything about data science or machine learning but you’re interested in getting started, you are definitely in the right place. Some Python experience is beneficial but not necessarily required. If you can understand basic coding, you will get along just fine. Come along and learn all about data science, machine learning, and how to do all kinds of amazing things that look and sound like magic to most developers!

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Provides foundational knowledge and skills in data science and machine learning, easing beginners into these fields
Emphasizes practical application through interactive visualizations and hands-on exercises
Covers a comprehensive range of topics, including data analysis, visualization, machine learning techniques, and cloud services
Taught by A Cloud Guru, recognized for their expertise in cloud computing and data science
Utilizes Jupyter Notebooks as a platform, which is commonly used in data science and machine learning
Requires prior experience in Python programming, which may limit accessibility for complete beginners

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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 Introduction to Jupyter Notebooks with these activities:
Understand basics of data science and machine learning concepts
Familiarize yourself with the fundamental principles underlying data science and machine learning, including concepts like supervised learning, unsupervised learning, and model evaluation.
Browse courses on Data Science Concepts
Show steps
  • Review online resources and tutorials on data science and machine learning basics.
  • Enroll in a beginner-friendly online course or workshop on data science or machine learning.
  • Read introductory books or articles on data science and machine learning.
Read 'Python for Data Analysis' by Wes McKinney
Gain a comprehensive understanding of Python's capabilities for data analysis, enhancing your skills in data manipulation, visualization, and statistical modeling.
Show steps
  • Read selected chapters or the entire book to grasp the fundamentals of Python for data analysis.
  • Complete the exercises and examples provided in the book to reinforce your understanding.
  • Refer to the book as a reference guide while working on your data science projects.
Learn Jupyter Notebook Interface and Functionality
Gain proficiency in using Jupyter Notebook's interface and features, enabling you to seamlessly navigate and utilize its capabilities for data analysis and visualization.
Show steps
  • Follow guided tutorials on Jupyter Notebook's website or other reputable online platforms.
  • Explore Jupyter Notebook's documentation and user guides to understand its functionality in detail.
  • Practice working with Jupyter Notebooks by creating and modifying your own notebooks.
Five other activities
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Practice Data Visualization Techniques
Deepen your understanding and proficiency in data visualization techniques by engaging in repetitive exercises and drills, allowing you to master the practical application of these methods.
Show steps
  • Solve coding challenges and exercises on platforms like LeetCode or HackerRank.
  • Complete data visualization tutorials and exercises provided by online courses or platforms.
  • Create interactive data visualizations using libraries like matplotlib, seaborn, or Plotly.
Attend a Data Science Workshop
Participate in a workshop led by experienced data scientists, gaining exposure to practical applications, best practices, and industry trends.
Show steps
  • Identify and register for a data science workshop that aligns with your learning goals.
  • Attend the workshop and actively participate in discussions and hands-on exercises.
  • Network with other participants and industry professionals.
Build a Data Science Project Using Jupyter Notebook
Apply your skills and knowledge to a practical project, solidifying your understanding of the course concepts and gaining valuable hands-on experience.
Show steps
  • Identify a real-world dataset and research relevant machine learning techniques.
  • Develop a data science project proposal outlining your goals, methodology, and expected outcomes.
  • Implement your project using Jupyter Notebook, including data cleaning, feature engineering, model training, and visualization.
  • Document your project, including a report detailing your findings and insights.
Mentor Junior Data Scientists
Reinforce your knowledge by sharing your expertise with others, guiding their understanding of data science concepts and practices.
Show steps
  • Volunteer as a mentor in online forums or communities dedicated to data science.
  • Offer guidance and support to junior data scientists, answering their questions and providing feedback on their work.
  • Organize study groups or workshops to share your knowledge and insights.
Contribute to Open-Source Data Science Projects
Engage in the data science community by contributing to open-source projects, expanding your knowledge, and gaining valuable experience.
Show steps
  • Identify open-source data science projects that align with your interests.
  • Review the project documentation and codebase to understand its purpose and structure.
  • Propose and implement improvements or new features to the project.
  • Collaborate with other contributors and maintainers to ensure code quality and project goals.

Career center

Learners who complete Introduction to Jupyter Notebooks will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts would use the concepts from this course to enhance their ability to turn raw data assets into valuable insights and strategies. Furthermore, this course can help prepare you for related in-demand roles such as Data Analyst, Business Analyst, Quantitative Analyst, and Operations Analyst. If you have a knack for storytelling and visualizing data to provide valuable insights, you will definitely do well as a Data Analyst. This beginner-friendly course ensures you can get started with the right tools and techniques to make a smoother transition into a Data Analyst role.
Data Visualization Specialist
Data Visualization Specialists are responsible for creating visual representations of data that are clear and easy to understand. This course can help you develop the skills needed to be successful in this role. The course will introduce you to the principles of data visualization and provide you with hands-on experience creating visualizations using Jupyter Notebooks.
Data Engineer
A Data Engineer's job involves designing and implementing data management solutions, and to be an effective Data Engineer, the knowledge of concepts such as interactive data tooling and visualization is key. The skills gained from this course will equip you to perform your duties as a Data Engineer seamlessly. This course was strategically developed to ensure that by the end, you will be able to apply the Data Science tools and techniques along with cloud computing. This is a great pick for you if you are looking for a course that will help you in your path to becoming a Data Engineer.
Machine Learning Engineer
A core part of a Machine Learning Engineer's work is to cater to the increasing demand of organizations to harness their data to make predictions and solve complex problems. This course is a great fit and starting point for your journey to become a Machine Learning Engineer. The course touches on concepts of Machine Learning, which is an essential skill in the field. This course will help you move smoothly into the field of Machine Learning Engineering. With this course, you will start leveraging the services of cloud providers and use their hosted notebook services to advance your data science and machine learning initiatives.
Data Science Manager
Data Science Managers are responsible for leading and managing teams of data scientists. This course can help you develop the skills needed to be successful in this role. The course will provide you with a strong foundation in data science and machine learning, as well as the leadership and management skills needed to lead a team.
Business Intelligence Analyst
Business Intelligence Analysts use data to help businesses make better decisions. This course can help you develop the skills needed to be successful in this role. The course will provide you with a strong foundation in data analysis and visualization, as well as the business knowledge needed to make better decisions.
Quantitative Analyst
A Quantitative Analyst heavily relies on data analytics and visualization to make informed decisions. The concepts in this course will greatly benefit you and give you the competitive advantage needed in this role. As a beginner, this course is ideal as it allows you to start your journey on the right foot and maximize your potential as a Quant or Quantitative Analyst.
Statistician
Statisticians are always in need of new and effective ways to gather, analyze, and visualize data. This course would be a great fit for anyone seeking to develop their skills as a Statistician. The concepts in this course will help you to visualize and interpret data more effectively, which is a key skill for any Statistician.
Market Researcher
Market Researchers collect, analyze, and interpret data to understand consumer behavior and trends. This course can help you develop the skills needed to be successful in this role. The course will provide you with a solid foundation in data visualization and analysis, which are essential skills for Market Researchers.
Data Scientist
A Data Scientist would use the concepts from this course to delve deeper into interactive data tooling and visualizations to aid in their ability to perform advanced analytics and data modeling for various stakeholders. Data science plays an important role in correlating large datasets and providing insights that aid in decision making, a factor which has led to the increase in demand for Data Scientists. This course has been built from the ground up to ensure that by the end, you will be able to apply the Data Science tools and techniques along with cloud computing. This course may be a great fit for you if you intend to transition into a Data Scientist role.
Operations Research Analyst
Operations Research Analysts use data to improve the efficiency of operations. This course can help you develop the skills needed to be successful in this role. The course will provide you with a strong foundation in data analysis and visualization, as well as the mathematical and analytical skills needed to improve the efficiency of operations.
Financial Analyst
Financial Analysts use data to make investment recommendations. This course can help you develop the skills needed to be successful in this role. The course will provide you with a strong foundation in data analysis and visualization, as well as the financial knowledge needed to make investment recommendations.
Actuary
Actuaries use data to assess risk and uncertainty. This course can help you develop the skills needed to be successful in this role. The course will provide you with a strong foundation in data analysis and visualization, as well as the mathematical skills needed to assess risk and uncertainty.
Software Engineer
Taking this course would aid a Software Engineer in acquiring and developing the skills needed to build and maintain the infrastructure for data science and machine learning applications. If you wish to expand your capabilities to the development and maintenance of machine learning tools and solutions, then the concepts in this course, such as data visualization and data analytics, will greatly benefit you. With this course, you will get to apply the Data Science tools and techniques along with cloud computing to help you in building and maintaining these applications.
Business Analyst
For a Business Analyst, data visualization and analysis is an essential skill, and thus, this course can provide you with the necessary skills to perform your work with more efficiency. This course is a good starting point for individuals interested in transitioning into a Business Analyst role.

Reading list

We've selected eight 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 Introduction to Jupyter Notebooks.
Provides a comprehensive introduction to the Python programming language and its various libraries for data science. It covers topics such as data manipulation, visualization, machine learning, and cloud computing.
Provides a practical introduction to deep learning. It covers topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive introduction to the Pandas library for data analysis. It covers topics such as data manipulation, cleaning, and visualization.
Provides a practical introduction to data science for business professionals. It covers topics such as data collection, analysis, and decision making.
Provides a comprehensive introduction to machine learning with Scikit-Learn, Keras, and TensorFlow. It covers topics such as supervised learning, unsupervised learning, and deep learning.

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