Master Python in 14 Days: Numpy, Pandas, Seaborn, RegEx, OOP from Udemy

Python is a high level dynamic programming language founded in 1991. The inspiration for the name ‘Python’ was from the comedy television show Monty Python’s Flying Circus. Today, Python is a very popular programming language which is extensively used in many organizations around the world and is one of the top programming languages in the software industry today.

Notably, Python has emerged as the No. 1 Programming language of choice across domains like artificial intelligence, data science, mobile applications, web development and machine learning.

Hence, learning python has become a necessity for those aspiring for a career in software industry and for those who are already in the IT industry. Even if you are new to programming, this course is a good starting point.

A key aspect of the course is the use of google cloud based development environment – colab. You may wonder what is the big deal. Well, for starters, you don't need to download anything to get started. You will use a development environment that can be accessed on your browser using your email id. As more and more companies embrace cloud in a big way, it has become imperative for programmers to gain knowledge and expertise to code in cloud.

The course covers the following concepts:

· Variables

· Operators

· Conditional statements

· For and While Loops

· Functions

· Four types of Arrays – List, Tuple, Set and Dictionary

· NumPy

· In NumPy, we will cover, how to shape arrays, iterate arrays, joining arrays, splitting arrays, searching arrays and sorting arrays.

· Pandas

· We will also explain data analysis using pandas

· Data visualization using matplotlib, seaborn, altair, dash, bokeh

. Regular Expressions (RegEx)

. Different functions like recursive, lambda functions in addition to regular functions

. OOP (Object Oriented Programming) - Basic & Advanced OOP concepts

. User defined or Extended Data Structure

But what are the features that make Python so easy to use?

One of the biggest advantages Python has over other programming languages is its readability and large standard library that makes coding easier. It is portable and interactive across various operating systems and has user friendly data structures that can be easily implemented. Moreover, Python also supports object oriented programming and has applications that varies across several different fields.

Applications of Python

Python’s popularity has made it a very useful tool to develop many applications. The wide selection of libraries and frameworks available makes it one very useful in the field of data analysis and machine learning. These libraries can be used for various purposes such as natural language process, speech synthesis, complex data analysis and so on. Python is also used in prototyping and scripting which helps in the development of embedded applications. Thus, the popularity of python is greatly beneficial for applications that require easier code maintenance and efficient versatility.

What's inside

Syllabus

Introduction

Day 1: Getting Started, Variables and Operators

Day 1: Introduction to Colab: Google Cloud Development Environment

Day 1: Getting Started with Python

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Uses Google Colab, which allows learners to gain experience coding in a cloud-based environment, increasingly important as more companies embrace cloud technologies

Covers a wide range of topics, from basic syntax to advanced concepts like OOP and concurrency, providing a comprehensive introduction to Python

Includes data visualization libraries like Matplotlib, Seaborn, Altair, Dash, and Bokeh, which are essential for data analysis and presentation in various industries

Devotes a significant portion to NumPy and Pandas, which are fundamental libraries for data manipulation and analysis in data science and machine learning

Includes regular expressions (RegEx), which are useful for pattern matching and text processing in various programming tasks

Features 'Test Your Understanding' and 'Debug the code' sections after each day, which may help learners reinforce their knowledge and practice their debugging skills

Reviews summary

Fast-paced python and data tools

According to students, this course offers a fast-paced way to learn Python, providing excellent coverage of essential data science libraries like Numpy, Pandas, and Seaborn. Many appreciated the practical examples and hands-on coding, finding them useful for real-world application, and liked the convenient Google Colab environment which requires no setup. However, the rapid speed means the course lacks depth in advanced topics such as OOP and data structures. Students note the 'Master' title can be misleading, as the course is more of a rapid introduction or refresh, particularly challenging for absolute beginners while being well-suited for those with some prior programming knowledge.

Convenient, no setup required.

"The use of Colab is a great plus, no setup required."

"Loved the Colab environment."

"Gets you hands-on with code quickly using Colab."

"Colab setup is indeed convenient."

"Using Google Colab simplifies the setup process significantly."

Helpful hands-on coding and projects.

"The hands-on coding and projects are the strongest part of the course for me."

"The practical examples and demos are very helpful."

"The practical exercises and projects reinforce learning."

"The labs are practical and help solidify understanding."

"I learned how to use practical tools and strategies that I could apply immediately."

Good coverage of Numpy, Pandas, Seaborn.

"The coverage of Numpy, Pandas, and Seaborn is excellent, with practical examples."

"Good overview of Python fundamentals and key libraries like Pandas and Numpy."

"Numpy, Pandas, Seaborn demos were practical and well-explained."

"The sections on data analysis with Pandas were particularly useful for my work."

"Provides a solid foundation in Python and covers essential libraries like NumPy, Pandas, and Seaborn effectively."

"The data science parts (NumPy, Pandas) are the highlight for me."

Lacks depth in advanced concepts.

"Some parts felt a bit rushed, especially the advanced topics like data structures. The explanations could be deeper..."

"Advanced concepts like Concurrency were barely touched upon."

"The later parts on advanced OOP and data structures felt a bit tacked on and not as well-explained..."

"Needed to supplement heavily with other resources, especially for the advanced topics."

"The breadth is impressive... but the depth is lacking due to the 14-day limit."

"Advanced OOP and data structures were mentioned but not explored in enough detail."

Covers topics quickly, perhaps too fast.

"The pace is quick but manageable, perfect for someone wanting to refresh or quickly learn Python."

"Some parts felt a bit rushed, especially the advanced topics like data structures. The explanations could be deeper..."

"As a complete beginner, I found it overwhelming at times... when it got to Numpy and Pandas, the pace picked up significantly."

"The course covers a lot of ground very quickly... As a complete beginner, I found it overwhelming at times."

"Found this course misleading. 'Master' in 14 days is not realistic. It's a surface-level overview... The content jumps between topics too fast."

"The speed is its main issue. 'Master' is an overstatement. It's more like a rapid introduction. Needed to supplement heavily..."

"It's a race against time, not mastery... Beginners will struggle."

Better for refresh/experienced than beginners.

"Perfect for someone wanting to refresh or quickly learn Python."

"As a complete beginner, I found it overwhelming at times."

"Not ideal for absolute beginners."

"Good for a quick dive."

"Useful for a quick refresh, but not for deep understanding or beginners."

"Beginners will struggle."

"Those with some background will find it a decent... overview."

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 Master Python in 14 Days: Numpy, Pandas, Seaborn, RegEx, OOP with these activities:

Review Basic Python Syntax

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Reinforce your understanding of fundamental Python syntax, including variable assignment, data types, and basic operators, to prepare for more advanced topics.

Browse courses on Python Syntax

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Read through introductory Python tutorials or documentation.
Write and execute simple Python scripts to test your understanding.
Complete online quizzes or exercises on basic Python syntax.

Review 'Python Crash Course'

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Solidify your understanding of Python fundamentals and gain hands-on experience through practical projects.

View Python Crash Course, 2nd Edition: A Hands-On,... on Amazon

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Read the relevant chapters covering basic Python syntax and data structures.
Work through the example projects to apply your knowledge.
Experiment with the code and modify it to explore different functionalities.

Practice NumPy Array Manipulations

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Sharpen your skills in manipulating NumPy arrays, including reshaping, slicing, and broadcasting, to efficiently process numerical data.

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Solve coding challenges on platforms like HackerRank or LeetCode focusing on NumPy arrays.
Implement common array operations from scratch to deepen your understanding.
Experiment with different array manipulation techniques on real-world datasets.

Four other activities

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Create a Pandas Data Analysis Report

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Apply your Pandas skills to analyze a real-world dataset and create a comprehensive report summarizing your findings.

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Choose a dataset from a public repository like Kaggle or UCI Machine Learning Repository.
Use Pandas to clean, transform, and analyze the data.
Create visualizations using Matplotlib or Seaborn to support your analysis.
Write a report summarizing your findings and insights.

Build a Data Visualization Dashboard

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Develop a dynamic data visualization dashboard using libraries like Dash or Bokeh to present insights from a dataset.

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Select a dataset and identify key metrics to visualize.
Design the layout and interactivity of your dashboard.
Implement the dashboard using Dash or Bokeh.
Deploy your dashboard to a web server or cloud platform.

Contribute to an Open Source Project

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Contribute to an open-source Python project that utilizes NumPy, Pandas, or data visualization libraries to gain real-world experience and collaborate with other developers.

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Find an open-source project on GitHub that aligns with your interests and skills.
Identify an issue or feature request that you can contribute to.
Submit a pull request with your changes.
Respond to feedback from the project maintainers and revise your code as needed.

Review 'Fluent Python'

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Deepen your understanding of advanced Python concepts and best practices for writing clean and efficient code.

View Fluent Python: Clear, Concise, and Effective... on Amazon

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Read the chapters covering advanced topics like metaclasses, descriptors, and concurrency.
Experiment with the code examples and try to apply them to your own projects.
Research and explore related topics to further expand your knowledge.

Career center

Learners who complete Master Python in 14 Days: Numpy, Pandas, Seaborn, RegEx, OOP will develop knowledge and skills that may be useful to these careers:

Python Developer

A Python developer specializes in building applications and systems using the Python programming language. This course helps someone become a Python developer, because it teaches the core language concepts and advanced features relevant for building robust Python applications. The coverage of object oriented programming, data structures, and regular expressions is crucial for developing efficient and maintainable code. Furthermore, learning to code in cloud based environments are essential skills for modern Python developers.

See salaries and explore the career path for Python Developer

Data Scientist

A data scientist uncovers insights from large datasets using various techniques, including machine learning, statistical modeling, and data visualization. This course helps someone interested in becoming a data scientist through its comprehensive Python training. This includes coverage of NumPy, Pandas, Seaborn, and other tools critical for data manipulation, analysis, and visualization. The section on regular expressions is also valuable for data cleaning and preprocessing. The use of cloud-based environments directly prepares you for modern data science workflows.

See salaries and explore the career path for Data Scientist

Web Developer

A web developer creates and maintains websites and web applications. This course helps in building skills in Python, often used in backend web development frameworks like Django and Flask. The course offers training in core programming concepts, data structures, and object oriented programming. It will help with building robust and scalable web applications. Learning to code Python in cloud environments is essential for modern web developers.

See salaries and explore the career path for Web Developer

Machine Learning Engineer

A machine learning engineer creates and implements machine learning algorithms and models. This includes designing experiments, developing models, and deploying them into production systems. This course helps machine learning engineers by providing training in Python, the primary language in the field. The training in NumPy and Pandas are relevant for data manipulation. Object oriented programming is also helpful for structuring complex machine learning projects. The hands-on experience with cloud-based development environments is particularly useful, as machine learning often involves cloud-based resources.

See salaries and explore the career path for Machine Learning Engineer

DevOps Engineer

A DevOps engineer automates and streamlines software development and deployment processes. This course helps build necessary skills in Python, a common language for automation and scripting in DevOps. It introduces core programming concepts, data structures, and object oriented programming. Exposure to cloud environment makes the path to becoming a DevOps engineer particularly useful.

See salaries and explore the career path for DevOps Engineer

Bioinformatician

A bioinformatician analyzes biological data using computational tools and methods. This course helps build the Python skills that are frequently needed in bioinformatics. It is useful when working with biological data, so experience with NumPy and Pandas will be relevant for data manipulation. The course modules covering regular expressions are also helpful for parsing biological sequences and data formats. Cloud environments are a great way to analyze and distribute large datasets.

See salaries and explore the career path for Bioinformatician

Data Analyst

A data analyst examines data to draw conclusions about it to help companies make better decisions. This involves collecting, cleaning, and interpreting data sets. This course may be useful because it helps build a foundation in Python, a crucial language for data analysis, along with key libraries such as Pandas, NumPy, and Seaborn. The sections on data visualization and regular expressions are particularly apt for someone aspiring to be a data analyst. Furthermore, the use of cloud-based development environments is directly relevant to modern data analysis workflows.

See salaries and explore the career path for Data Analyst

Business Intelligence Analyst

A business intelligence analyst examines and interprets data to create reports and dashboards that inform business decisions. This course helps prepare business intelligence analysts by teaching Python and various libraries relevant to the profession. Pandas is useful for data manipulation, Seaborn is useful for data visualization, and regular expressions are useful for data cleaning. The visualization features of the course are particularly suited to someone wishing to become a Business Intelligence Analyst.

See salaries and explore the career path for Business Intelligence Analyst

Software Engineer

A software engineer designs, develops, and tests software applications. This course helps in building a foundation in Python, a versatile language used in numerous software development areas. A good software engineer will show competence in object oriented programming which will lead to proficiency in software design. The course's coverage of data structures, regular expressions, and various programming paradigms will enhance your problem-solving capabilities as a software engineer. Exposure to cloud-based development also aligns with modern software development trends.

See salaries and explore the career path for Software Engineer

Quantitative Analyst

A quantitative analyst, often working in finance, develops and implements mathematical models for pricing, trading, and risk management. This course may prove useful, as it helps build core Python skills, which are increasingly used in quantitative finance. The focus on NumPy and Pandas are particularly relevant for numerical computations and data analysis. Additionally, the modules on regular expressions and object oriented programming may prove useful.

See salaries and explore the career path for Quantitative Analyst

Research Scientist

A research scientist conducts research to advance knowledge in a specific field, often involving data analysis and modeling. This course may be useful, since it develops your skills in Python, a leading language for scientific computing and data analysis. The NumPy, Pandas, and data visualization modules are particularly pertinent for handling and interpreting research data. Skills in object oriented programming may also be useful for those looking to organize and structure research code effectively.

See salaries and explore the career path for Research Scientist

Statistician

A statistician collects, analyzes, and interprets numerical data to draw conclusions and make informed decisions. A course like this may be useful because it provides a foundation in Python, a language used in statistical analysis. The NumPy and Pandas modules are particularly relevant for data handling and manipulation, while the data visualization tools can enhance the presentation and interpretation of statistical results. A background in object oriented programming will also help when building statistical models.

See salaries and explore the career path for Statistician

System Administrator

A system administrator manages and maintains computer systems and servers. This course helps System Administrators by building skills in Python, which is often used for scripting and automation tasks. The course covers essential programming concepts, and the modules on regular expressions can be particularly helpful for parsing log files and automating system maintenance tasks. Moreover, exposure to cloud-based development aligns with the increasing prevalence of cloud infrastructure management.

See salaries and explore the career path for System Administrator

Embedded Systems Engineer

An embedded systems engineer designs, develops, and tests software for embedded systems, which are specialized computer systems within devices. This course may be useful, as Python is used increasingly in embedded systems for scripting, testing, and rapid prototyping. Knowledge in object oriented programming may be helpful in designing and implementing embedded software. The course covers many topics that one would find useful on the path to becoming an embedded systems engineer.

See salaries and explore the career path for Embedded Systems Engineer

Database Administrator

A database administrator manages and maintains databases, ensuring their security, availability, and performance. A course like this may be useful, as Python can be used for database automation tasks, scripting, and data manipulation. The course's coverage of regular expressions can be helpful for data validation and cleaning. The skills in object oriented programming may also be useful for structuring database related code. While database administrators do not program full time, certain features of this course can be useful.

See salaries and explore the career path for Database Administrator

Master Python in 14 Days

Numpy, Pandas, Seaborn, RegEx, OOP

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Fast-paced python and data tools

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