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Kennedy Behrman, Alfredo Deza, and Noah Gift

In this first course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will learn how to set up a version-controlled Python working environment which can utilize third party libraries. You will learn to use Python and the powerful Pandas library for data analysis and manipulation. Additionally, you will also be introduced to Vim and Visual Studio Code, two popular tools for writing software. This course is valuable for beginning and intermediate students in order to begin transforming and manipulating data as a data engineer.

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What's inside

Syllabus

Getting Started with Python
This week, you will learn how to set up an isolated Python environment with third party libraries and apply it by setting up a virtual environment including Pandas and Jupyter.
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Essential Python
This week, you will learn how to create and use Python Sequences, Dictionaries, Sets, List Comprehensions, and Generators. Additionally, you will learn how to apply these by manipulating client data in a Jupyter notebook.
Data in Python: Pandas and Alternatives
This week, you will learn how to load data into a Pandas DataFrame and write statements to select columns and rows from a DataFrame. Additionally, you will apply comparison and boolean operators as a method of selecting data.
Python Development Environments
This week, you will learn the basics of some popular development environments and apply it by writing code in Vim and Visual Studio Code. Additionally, you will learn how to check your code into a Git repository.

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Teaches the fundamentals of Python, including data types, control flow, and functions
Covers advanced Python topics such as object-oriented programming and data analysis with Pandas
Introduces popular development environments such as Vim and Visual Studio Code
Provides hands-on experience with Python coding through Jupyter notebooks
Requires basic programming knowledge and familiarity with data structures

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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 Python and Pandas for Data Engineering with these activities:
Review basic Python programming concepts
Refreshing your understanding of basic Python programming concepts will provide a solid foundation for the more advanced topics covered in this course.
Browse courses on Python Basics
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  • Review online tutorials or documentation on Python basics
  • Practice writing simple Python programs
Review data science and machine learning concepts
Refreshing your knowledge of data science and machine learning concepts will provide context for the application of Python in these fields.
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  • Review lecture notes or textbooks on data science and machine learning
  • Complete practice problems or exercises
Participate in study groups or hackathons
Engaging in study groups or hackathons provides opportunities to collaborate with peers, exchange ideas, and learn from different perspectives, fostering a deeper understanding.
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  • Find or create a study group with other students in the course
  • Establish regular meeting times and set discussion topics
  • Actively participate in discussions and ask questions
Five other activities
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Create a cheatsheet for Python syntax
Creating a cheatsheet will help you remember and quickly reference key Python syntax rules, improving your efficiency and understanding of the language.
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  • Review and identify essential Python syntax rules
  • Organize and categorize the rules into meaningful sections
  • Create a visually appealing and easy-to-read cheatsheet
Solve coding challenges on Python code review platforms
Actively solving coding challenges will sharpen your Python problem-solving skills, enhance your understanding of data structures and algorithms, and prepare you for real-world programming scenarios.
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  • Join a reputable Python coding challenge platform
  • Select challenges that align with your learning goals
  • Attempt to solve the challenges independently
  • Review solutions and learn from others' approaches
Follow tutorials on advanced Python data manipulation techniques
Exploring advanced data manipulation techniques through guided tutorials will enhance your proficiency in handling and transforming data, a crucial skill for data engineers.
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  • Identify specific areas where you want to improve your data manipulation skills
  • Find comprehensive tutorials that cover these advanced techniques
  • Follow the tutorials step-by-step and practice the techniques
Develop a data engineering project using Python
Embarking on a data engineering project will provide hands-on experience in applying the skills and knowledge acquired in the course, solidifying your understanding and building your portfolio.
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  • Identify a real-world problem or dataset that interests you
  • Design and implement a data engineering pipeline using Python
  • Document your project and share your findings
Contribute to open-source Python projects
Contributing to open-source Python projects allows you to engage with the wider Python community, learn from experienced developers, and enhance your practical skills.
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  • Identify open-source Python projects that align with your interests
  • Familiarize yourself with the project's codebase and documentation
  • Propose and implement improvements or fixes

Career center

Learners who complete Python and Pandas for Data Engineering will develop knowledge and skills that may be useful to these careers:
Data Engineer
Data Engineers wield Python and Pandas as essential tools for data analysis and manipulation, two skills that are highly sought after in the field. This course offers learners a chance to gain foundational knowledge in these technologies, building blocks necessary for a successful career in data engineering. Furthermore, it introduces learners to popular development tools like Vim and Visual Studio Code, empowering them to effectively write and maintain data engineering code.
Data Analyst
Data Analysts utilize Python and Pandas to uncover insights from raw data. This course in Python and Pandas for Data Engineering provides a solid foundation for aspiring Data Analysts, equipping them with the skills to clean, transform, and analyze large datasets. Moreover, exposure to development environments like Vim and Visual Studio Code fosters proficiency in writing and debugging code, essential qualities for Data Analysts.
Machine Learning Engineer
Machine Learning Engineers employ Python and Pandas for preprocessing, data analysis, and model development. This course provides a comprehensive introduction to these technologies, offering a strong foundation for aspiring Machine Learning Engineers. By gaining proficiency in data manipulation and analysis, learners can build robust machine learning models that drive data-driven decision-making.
Data Scientist
Data Scientists heavily rely on Python and Pandas for data analysis and exploration. This course offers an excellent opportunity for aspiring Data Scientists to develop these core competencies. Through hands-on exercises and real-world examples, learners will master techniques for data cleaning, transformation, and visualization, laying the groundwork for successful careers in data science.
Software Engineer
Software Engineers may work with Python and Pandas when developing data-driven applications. This course provides a valuable introduction to these technologies, helping Software Engineers expand their skillset and gain a competitive edge. By learning to manipulate and analyze data effectively, they can build more robust and efficient software solutions.
Financial Analyst
Financial Analysts frequently use Python and Pandas to analyze financial data and build models. This course provides a valuable introduction to these technologies, helping aspiring Financial Analysts enhance their skillset and stay competitive. By gaining proficiency in data manipulation and analysis, they can make more informed investment decisions and provide valuable insights to clients.
Business Analyst
Business Analysts often leverage Python and Pandas for data analysis and presentation. This course offers a great opportunity for aspiring Business Analysts to develop these core competencies. Through practical exercises, learners will gain proficiency in manipulating and interpreting data, enabling them to make data-driven recommendations and drive business decisions.
Product Manager
Product Managers often work with data to understand user behavior and market trends. This course in Python and Pandas for Data Engineering provides a solid foundation for aspiring Product Managers, equipping them with the skills to analyze data effectively. By gaining proficiency in data manipulation and visualization, they can make data-driven decisions and build products that meet user needs.
Marketing Manager
Marketing Managers leverage data to understand customer behavior and market trends. This course in Python and Pandas for Data Engineering provides a solid foundation for aspiring Marketing Managers, equipping them with the skills to analyze data effectively. By gaining proficiency in data manipulation and visualization, they can make data-driven marketing decisions and develop effective campaigns.
Project Manager
Project Managers may encounter data analysis tasks during project planning and execution. This course offers a valuable introduction to Python and Pandas, providing aspiring Project Managers with the skills to handle data more effectively. By gaining proficiency in data manipulation and analysis, they can make informed decisions, track progress, and manage resources efficiently.
Operations Manager
Operations Managers often rely on data to improve efficiency and optimize processes. This course offers a valuable introduction to Python and Pandas, providing aspiring Operations Managers with the skills to analyze data more effectively. By gaining proficiency in data manipulation and analysis, they can make informed decisions, allocate resources efficiently, and streamline operations.
IT Manager
IT Managers often oversee data-related projects and initiatives. This course provides a valuable introduction to Python and Pandas, helping aspiring IT Managers gain a deeper understanding of data management and analysis. By gaining proficiency in these technologies, they can make more informed decisions, allocate resources effectively, and ensure the smooth operation of IT systems.
Risk Manager
Risk Managers analyze data to identify and assess potential risks. This course in Python and Pandas for Data Engineering provides a solid foundation for aspiring Risk Managers, equipping them with the skills to handle data effectively. By gaining proficiency in data manipulation and visualization, they can make data-driven decisions and develop effective risk management strategies.
Sales Manager
Sales Managers often leverage data to understand customer behavior and identify sales opportunities. This course in Python and Pandas for Data Engineering provides a solid foundation for aspiring Sales Managers, equipping them with the skills to analyze data effectively. By gaining proficiency in data manipulation and visualization, they can make data-driven sales decisions and develop effective sales strategies.
UX Designer
UX Designers may use data to understand user behavior and improve user experience. This course offers a valuable introduction to Python and Pandas, providing aspiring UX Designers with the skills to analyze data more effectively. By gaining proficiency in data manipulation and visualization, they can make data-driven design decisions and create user-centric products.

Reading list

We've selected 14 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 Python and Pandas for Data Engineering.
Practical guide to using Python for data analysis. It covers all the essential topics, from data loading and cleaning to data visualization and modeling.
Comprehensive guide to using Pandas for data manipulation. It covers all the essential topics, from data loading and cleaning to data transformation and aggregation.
Comprehensive guide to the Python programming language. It covers all the basics, from data types and variables to functions and classes. It valuable reference for beginners and experienced programmers alike.
Comprehensive guide to data mining. It covers all the essential topics, from data preparation and feature selection to model evaluation and deployment.
Comprehensive guide to deep learning. It covers all the essential topics, from neural networks and convolutional neural networks to recurrent neural networks and generative adversarial networks.
Classic textbook on statistical learning. It covers all the essential topics, from linear regression and logistic regression to decision trees and support vector machines.
More accessible introduction to statistical learning. It covers all the essential topics, but with a focus on making the material easy to understand.
Comprehensive guide to advanced R programming. It covers all the essential topics, from data manipulation and visualization to statistical modeling and machine learning.
Comprehensive guide to machine learning in Python. It covers all the essential topics, from data loading and cleaning to data visualization and modeling.
Comprehensive guide to reinforcement learning. It covers all the essential topics, from Markov decision processes and value functions to policy evaluation and improvement.
Practical guide to deep learning with Fastai and PyTorch. It covers all the essential topics, from data loading and preparation to model training and evaluation.

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