Python A-Z™: Python For Data Science With Real Exercises! from Udemy

Learn Python Programming by doing.

There are lots of Python courses and lectures out there. However, Python has a very steep learning curve and students often get overwhelmed. This course is different.

This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.

After every video you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.

This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.

In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course.

I can't wait to see you in class,

What you will learn:

Learn the core principles of programming
Learn how to create variables
How to visualize data in Seaborn
How to create histograms, KDE plots, violin plots and style your charts to perfection
Learn about integer, float, logical, string and other types in Python
Learn how to create a while() loop and a for() loop in Python
And much more....

Sincerely,

Kirill Eremenko

What's inside

Learning objectives

Learn to program in python at a good level
Learn how to code in jupiter notebooks
Learn the core principles of programming
Learn how to create variables

Learn about integer, float, logical, string and other types in python
Learn how to create a while() loop and a for() loop in python
Learn how to install packages in python
Understand the law of large numbers

Learn to program in python at a good level
Learn how to code in jupiter notebooks
Learn the core principles of programming
Learn how to create variables
Learn about integer, float, logical, string and other types in python
Learn how to create a while() loop and a for() loop in python
Learn how to install packages in python
Understand the law of large numbers

Syllabus

Welcome To The Course

Welcome Challenge!

Here you will learn how to install Anaconda, Python and Jupyter Notebook

Get the Datasets here

Here you will see the difference between violinplots and boxplots, will know what they used for and what executives prefer in their analytics!

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Teaches Seaborn, a powerful data visualization library, which is essential for creating compelling and informative charts and plots for data analysis

Covers core programming principles like variables, loops, and conditional statements, providing a solid foundation for further learning and more advanced programming concepts

Includes hands-on homework exercises such as financial statement analysis and basketball free throws, allowing learners to apply their knowledge to real-world scenarios

Uses Jupyter Notebooks, an interactive coding environment, which is widely used in data science and allows for easy experimentation and documentation of code

Explores NumPy and Pandas, which are fundamental libraries for numerical computing and data manipulation, and are essential tools for any data scientist

Includes advanced tutorials on topics such as keyword arguments and styling tips, which may be useful for learners looking to refine their skills

Reviews summary

Python for data science fundamentals

According to learners, this course provides a solid foundation in Python programming with a focus on data science essentials. Many found the step-by-step approach and practical exercises particularly helpful for beginners. Students appreciated the coverage of key libraries like Numpy, Pandas, and Seaborn. Some reviewers noted that while the course is excellent for getting started, it may not delve deeply into more advanced topics or cover the latest library updates. Overall, it is frequently described as a great starting point for those new to Python for data analysis.

Some parts may need updating.

"Some parts of the course seemed slightly outdated regarding library versions or coding practices."

"Encountered minor issues with code due to changes in library versions since the course was made."

"Hope the instructor keeps the content updated with the latest versions of Pandas and Seaborn."

Introduces essential data science tools.

"Getting introduced to Numpy, Pandas, and Seaborn was very valuable."

"The sections on data frames and visualization were particularly useful."

"Learned how to use the core libraries needed for data analysis in Python."

"Good overview of the standard tools used in the data science world."

"Covered the main packages I needed to start working with data."

Hands-on coding reinforces learning.

"The exercises and homework assignments really helped solidify the concepts."

"I appreciated the real-life examples and analytical challenges."

"Learning by doing through the practical coding tasks was the best part."

"The homework solutions were clear and useful for checking my work."

"The hands-on problems made the abstract concepts much easier to understand."

An accessible entry point into Python.

"This course was an excellent starting point for someone with no prior coding experience."

"I found it very easy to follow along, perfect for absolute beginners."

"The way concepts are introduced step-by-step makes it very beginner-friendly."

"It helped me grasp the basics of Python and data science effectively."

"Great for getting your feet wet in Python data analysis."

May not suit intermediate learners.

"While good for beginners, it doesn't go deep into advanced data manipulation or statistical concepts."

"Felt like it only scratched the surface on some topics like complex visualizations or machine learning."

"Could use more in-depth coverage on complex data cleaning or optimization techniques."

"If you already have some Python experience, you might find parts too basic."

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 A-Z™: Python For Data Science With Real Exercises! with these activities:

Review Core Programming Principles

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Reinforce your understanding of fundamental programming concepts before diving into Python-specific syntax.

Browse courses on Programming Principles

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Review the definitions of variables, loops, and conditional statements.
Practice writing pseudocode for simple programming problems.
Complete online quizzes or exercises on basic programming concepts.

Read 'Automate the Boring Stuff with Python'

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Learn practical Python skills by working through real-world automation examples.

View Automate the Boring Stuff with Python, 3rd... on Amazon

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Read the first few chapters covering basic Python syntax and data types.
Try out the code examples provided in the book.
Work on one of the automation projects described in the book.

Practice Python Syntax on CodingBat

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Solidify your understanding of Python syntax through focused coding exercises.

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Visit the CodingBat website and select the Python section.
Work through the exercises in the 'Warmup-1' and 'String-1' categories.
Aim to solve at least 10 exercises per session.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Create a Python Cheat Sheet

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Consolidate your knowledge by creating a cheat sheet of important Python syntax and functions.

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Review your course notes and identify key concepts.
Organize the information into categories such as data types, loops, and functions.
Write concise explanations and examples for each concept.
Design the cheat sheet for easy readability and quick reference.

Build a Simple Data Visualization Dashboard

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Apply your Python and data visualization skills to create an interactive dashboard.

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Choose a dataset from Kaggle or another public source.
Use Pandas to load and clean the data.
Create several visualizations using Seaborn to explore the data.
Use a framework like Dash or Streamlit to build an interactive dashboard.

Answer Questions on Stack Overflow

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Reinforce your understanding by helping others with their Python-related questions.

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Browse the Python tag on Stack Overflow.
Identify questions that you can answer based on your knowledge.
Provide clear and concise explanations with code examples.

Read 'Python Data Science Handbook'

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Deepen your understanding of Python data science libraries and techniques.

View Python Data Science Handbook: Essential Tools... on Amazon

Show steps

Focus on the chapters covering Pandas and Seaborn.
Work through the examples and exercises provided in the book.
Experiment with different data visualization techniques.

Career center

Learners who complete Python A-Z™: Python For Data Science With Real Exercises! will develop knowledge and skills that may be useful to these careers:

Data Analyst

A data analyst uses programming skills to gather insights from data, often working with databases, spreadsheets, and statistical tools. This course helps build a foundation in Python, which is used to process and visualize data. It also introduces concepts such as data frames, histograms, and other plots in Python. The course includes real-life challenges and homework that help a future data analyst learn to solve business problems via Python programming. Specifically, the sections on Seaborn and data visualization in Python may be useful for a data analyst.

See salaries and explore the career path for Data Analyst

Data Scientist

A data scientist uses programming, statistics, and machine learning to extract knowledge from data. This course helps a data scientist learn Python. This course builds a good foundation in Python and covers many topics a data scientist might encounter in their daily work. For example, this course introduces concepts such as data visualization using Seaborn, loops, functions, and data frames. The many homework problems in this course also can provide real-world practice.

See salaries and explore the career path for Data Scientist

Financial Analyst

A financial analyst analyzes financial data to provide guidance on investment decisions. This role involves working with quantitative data, and Python skills learned in this course can be used to analyze financial statements and model financial outcomes. The course's real-life analytical challenges and homework like the financial statement analysis exercise help a financial analyst develop practical coding skills. The course's modules on dataframes, matrices, and visualizations can be useful for a financial analyst.

See salaries and explore the career path for Financial Analyst

Business Analyst

A business analyst identifies problems and proposes solutions, often using data to support their recommendations. The Python skills built by this course allow a business analyst to examine trends and make data driven decisions. This includes the ability to create visualizations that can be included in reports or presentations. A business analyst can gain useful coding experience from this course's many homework exercises such as financial statement analysis. This course helps develop skills that are very important to a business analyst.

See salaries and explore the career path for Business Analyst

Operations Analyst

An operations analyst analyzes business processes to identify areas for improvement. This often involves analyzing data to determine efficiency. The Python programming skills gained from this course can allow an operations analyst to streamline their process and create visualizations from data. This course's homework, which includes real-life challenges, also help an operations analyst develop practical coding skills. An operations analyst may find the modules on data frames useful.

See salaries and explore the career path for Operations Analyst

Market Research Analyst

Market research analysts study market conditions to examine potential sales of a product or service. This often involves working with large datasets and using statistical analysis. This course can help a market research analyst become fluent in Python, a valuable skill for data analysis. The ability to create visualizations is essential, which this course covers using Seaborn. The various homework exercises also help a market research analyst build experience. The section on dataframes may be particularly relevant for a market research analyst.

See salaries and explore the career path for Market Research Analyst

Machine Learning Engineer

A machine learning engineer builds and deploys machine learning models. This role typically requires an advanced degree. This course helps build a foundation with the programming skills needed by a machine learning engineer. This course teaches the essentials of Python, which is often used in machine learning. The course's modules on data manipulation and visualization are critical to a machine learning engineer. The course provides a useful starting point for machine learning.

See salaries and explore the career path for Machine Learning Engineer

Research Associate

Research associates assist in research projects, often involving data collection, analysis, and interpretation. The programming skills gained in this course, particularly in Python, can help a research associate clean and analyze large datasets. The ability to create visualizations, which is explored in both the Seaborn and data visualization modules, is vital for a research associate who might need to present their findings. The course also provides practical experience with loops, functions, and lists, that a research associate may find useful in their daily tasks.

See salaries and explore the career path for Research Associate

Statistician

A statistician designs studies, collects data, and uses statistical methods to analyze that data. This role typically requires an advanced degree. This course is a useful starting point for a statistician as it builds a foundation in Python programming, a tool often used in statistical work. The course also covers concepts such as data visualization and the law of large numbers. A statistician can learn to work with data in this course. This may be especially useful to a statistician who wants to have practical coding skills.

See salaries and explore the career path for Statistician

Quantitative Analyst

Quantitative analysts, often working in the financial sector, use mathematical and statistical methods to solve complex problems. This role generally requires an advanced degree. The Python programming skills acquired in this course may be helpful to a quantitative analyst who needs to perform statistical analysis. The course covers topics that can be applied to quantitative analysis, including data manipulation and visualization. Specifically, a quantitative analyst can find the sections on loops and functions to be useful for their work.

See salaries and explore the career path for Quantitative Analyst

Bioinformatician

A bioinformatician analyzes biological data using computational tools. This role typically requires an advanced degree. The Python skills learned in this course can help a bioinformatician process and analyze large sets of biological data. This course provides relevant programming skills that a bioinformatician can apply to their work. The course content on data visualization can help a bioinformatician display results clearly and effectively. The many homework exercises also provide valuable hands-on experience that may be useful.

See salaries and explore the career path for Bioinformatician

Research Scientist

A research scientist conducts research and develops solutions, often in a laboratory setting. The programming skills learned in this course can help a research scientist automate data analysis and create visualizations from experimental data. This course can help a research scientist learn a valuable skill like Python. The course provides instruction in working with dataframes and matrices which a research scientist might require.

See salaries and explore the career path for Research Scientist

Software Developer

A software developer designs, writes, and tests code for software applications. They often need a working knowledge of programming languages such as Python. The Python programming skills gained in this course can be helpful to a software developer. This course provides a good foundation in Python that is necessary before more advanced topics are covered. The many homework exercises also give important experience to a software developer. This course covers core programming principles that are important for a software developer to understand.

See salaries and explore the career path for Software Developer

Project Manager

A project manager plans, executes, and oversees project completion. They may work with technical teams. The skills gained in this course are not central to the role of a project manager, but understanding of Python can be useful when coordinating with technical teams. This course can help a project manager understand the type of work that a technical team does, and how they might present their work. The course's section on data visualization might be useful in helping a project manager understand how insights are presented.

See salaries and explore the career path for Project Manager

Database Administrator

A database administrator maintains and manages databases. While this role does not directly involve heavy coding, Python can be useful for automating tasks within data management. This course helps provide a database administrator with skills in Python programming that they can use to process data. The various modules in this course help a database administrator gain the Python tools to interact with databases. The section on data frames may be particularly relevant.

See salaries and explore the career path for Database Administrator

Reading list

We've selected two 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 A-Z™: Python For Data Science With Real Exercises!.

Python Data Science Handbook

Save

Provides a comprehensive overview of essential Python data science tools and techniques. It covers NumPy, Pandas, Matplotlib, and Scikit-learn in detail, making it a valuable reference for data analysis and machine learning tasks. It is particularly useful for understanding the underlying principles and best practices for data manipulation and visualization. This book is commonly used as a textbook at academic institutions and by industry professionals.

Python Data Science Handbook: Essential Tools for...

Paperback

$$$

Automate the Boring Stuff with Python, 3rd Edition

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Provides a practical introduction to Python programming, focusing on automating everyday tasks. It's an excellent resource for beginners and complements the course by offering hands-on examples and projects. The book covers topics such as web scraping, working with Excel spreadsheets, and automating file management, which can enhance your data science workflow. It is commonly used as a textbook at academic institutions.

Automate the Boring Stuff with Python, 3rd Edition

Kindle Edition

Python A-Z™

Python For Data Science With Real Exercises!

What's inside

Learning objectives

Syllabus

Traffic lights

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Reviews summary

Python for data science fundamentals

Activities

Career center

Reading list

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