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Updated in [April 29th, 2023]

This course provides an introduction to Docker, Data Science, Jupyter, Python, Data Analysis, Data Visualization, and Open Source. Participants will learn how to use Docker to create and manage data science environments, how to use Jupyter notebooks to create and share data science projects, and how to use Python for data analysis and data visualization. Additionally, participants will gain an understanding of open source tools and technologies for data science. By the end of the course, participants will have the skills and knowledge to create and share data science projects using Docker, Jupyter, Python, and open source tools.

[Applications]
After completing this course, students can apply their knowledge to a variety of projects. They can use Docker to create and manage data science environments, use Jupyter Notebooks to write and execute Python code, and use data analysis and data visualization techniques to explore and interpret data. Additionally, they can explore open source projects related to data science and use them to create their own projects.

[Career Paths]
1. Data Scientist: Data Scientists use their knowledge of mathematics, statistics, and computer science to analyze large datasets and uncover insights. They use a variety of tools, such as Jupyter Notebooks, Python, and Docker, to develop models and algorithms that can be used to make predictions and decisions. Data Scientists are in high demand, and the field is expected to continue to grow as more organizations rely on data-driven decision making.

2. Data Analyst: Data Analysts use their knowledge of data analysis and visualization to interpret data and draw meaningful conclusions. They use tools such as Jupyter Notebooks, Python, and Docker to analyze data and create visualizations that can be used to inform decisions. Data Analysts are in high demand, and the field is expected to continue to grow as more organizations rely on data-driven decision making.

3. Data Engineer: Data Engineers use their knowledge of software engineering and data management to design and build data pipelines and systems. They use tools such as Jupyter Notebooks, Python, and Docker to develop and maintain data systems that can be used to store, process, and analyze large datasets. Data Engineers are in high demand, and the field is expected to continue to grow as more organizations rely on data-driven decision making.

4. Open Source Developer: Open Source Developers use their knowledge of open source software development to create and maintain open source projects. They use tools such as Jupyter Notebooks, Python, and Docker to develop and maintain open source projects that can be used by other developers. Open Source Developers are in high demand, and the field is expected to continue to grow as more organizations rely on open source software.

[Education Paths]
1. Bachelor of Science in Data Science: This degree path focuses on the development of data science skills, such as data analysis, data visualization, and open source software development. It also covers topics such as machine learning, artificial intelligence, and big data. This degree path is becoming increasingly popular as businesses and organizations look to leverage data to make better decisions.

2. Master of Science in Data Science: This degree path focuses on advanced data science topics, such as machine learning, artificial intelligence, and big data. It also covers topics such as data analysis, data visualization, and open source software development. This degree path is becoming increasingly popular as businesses and organizations look to leverage data to make better decisions.

3. Doctor of Philosophy in Data Science: This degree path focuses on the development of advanced data science skills, such as machine learning, artificial intelligence, and big data. It also covers topics such as data analysis, data visualization, and open source software development. This degree path is becoming increasingly popular as businesses and organizations look to leverage data to make better decisions.

4. Master of Science in Data Analytics: This degree path focuses on the development of data analytics skills, such as data mining, predictive analytics, and data visualization. It also covers topics such as machine learning, artificial intelligence, and big data. This degree path is becoming increasingly popular as businesses and organizations look to leverage data to make better decisions.

Course Syllabus

How to install Docker?

Starting Jupyter

Mapping Ports

Running in detached Mode

Facing a Persistence Problem

Solving the Persistence Problem