Back to the Basics Week 1: Foundations of Python Programming and Data Science

Back to the Basics Week 1: Foundations of Python Programming and Data Science

Back to the Basics Week 1: Foundations of Python Programming and Data Science
Photo by author

Join KDnuggets through our Back to Basics path to start a new career or improve your data science skills. The Back to Basics course is divided into 4 weeks with an additional week. We hope you can use these blogs as a guide for your course.

In the first week, we will learn all about Python, data processing, and visualization.

  • Day 1 to 3:Python basics for aspiring data scientists
    • An introduction to the role of Python in data science.
    • A beginner-friendly guide to Python syntax, data types, and control structures.
    • Interactive programming exercises to enhance your understanding.
  • the fourth day:Demystifying Python data structures
    • Learn about basic data structures in Python with our step-by-step guide. You’ll learn about lists, tuples, dictionaries and sets – each with practical examples and their importance in data manipulation.
  • Day 5 to 6: Practical numerical calculation with NumPy and Pandas
    • Discover the power of NumPy and Pandas in numerical analysis and data processing, including real-world applications and practical exercises.
  • the seventh day: Data cleaning techniques with pandas
    • Equip yourself with basic data cleaning skills with Pandas.

Let’s get started.

Week One – Part One: Getting started with Python for data science

A beginner’s guide to setting up Python and understanding its role in data science.

Genetic AI, ChatGPT, Google Bard – these are probably a lot of terms you’ve heard over the past few months. With all the hype, many of you are thinking about getting into the technology field, such as data science.

People in different roles want to keep their jobs, so they aim to develop their skills to fit the current market. It is a competitive market, and we are seeing more and more people taking an interest in data science, with thousands of online courses, bootcamps and Masters (MSc) available in this sector.

Week One – Part Two: Python basics: syntax, data types, and control structures

Want to learn Python? Get started today by learning Python syntax, supported data types, and control structures.

Are you a beginner looking to learn programming using Python? If so, this beginner-friendly tutorial is for you to learn the basics of the language. This tutorial will introduce you to Python syntax, which is fairly English-friendly. You will also learn how to work with different data types, conditional statements, and loops in Python.

If you already have Python installed in your development and environment, start the Python REPL and recode with it. Or if you want to skip installation – and start coding right away – I recommend heading over to Google Colab and start coding.

Week One – Part Three: Get started with Python data structures in 5 steps

This tutorial covers basic data structures in Python – lists, tuples, dictionaries, and sets. Learn about its features, use cases and practical examples, all in 5 steps.

If you want to implement a solution to a problem by stringing together a series of commands in steps of an algorithm, then at some point the data will need to be processed, and data structures will become necessary.

These data structures provide a way to organize and store data efficiently, and are essential for quickly creating modular code that can perform useful functions and scale well. Python, a specific programming language, has a series of its own built-in data structures.

Week One – Part Four: Introduction to Numpy and Pandas

An introductory book on using Numpy and Pandas for numerical calculation and data manipulation in Python.

If you are working on a data science project, Python packages will make your life easier because you only need a few lines of code to do complex operations, such as processing data and applying a machine learning/deep learning model.

When starting your data science journey, it is recommended that you start by learning two of the most useful Python packages: NumPy and Pandas. In this article, we introduce these two libraries. Let’s get started!

Week One – Part Five: Data cleaning with pandas

This step-by-step tutorial is for beginners to guide them through the process of cleaning and pre-processing data using the powerful Pandas library.

Our data often comes from multiple sources and is not clean. It may contain missing values, duplicates, wrong or unwanted formats, etc. Running your experiments on this messy data leads to incorrect results.

Therefore, it is essential to prepare your data before feeding it to your model. This data preparation by identifying and resolving potential errors, inaccuracies, and inconsistencies is called “data cleaning.”

Week One – Part Six: Data Visualization: Theory and Techniques

Unlock the secrets of how to monitor our data-driven world.

In a digital landscape dominated by big data and complex algorithms, one might think that the average person is lost in an ocean of numbers and data. is not it?

However, the bridge between raw data and understandable insights lies in the art of data visualization. It is the compass that guides us, the map that guides us, and the translator that deciphers the vast amount of data we encounter every day.

But what is the magic behind a good visualization? Why does one perception illuminate while the other confuses?

Week One – Part Seven: Create visualizations using Matplotlib and Seaborn

Learn to visualize the basic Python package for your business.

Data visualization is essential in data work because it helps people understand what is happening to our data. Data information is difficult to digest directly in raw form, but visualization can get people interested and engaged. That’s why learning data visualization is important to achieve success in data.

Matplotlib is one of the most popular data visualization libraries in Python because it is so versatile, you can visualize almost everything from scratch. You can control many aspects of your visualization using this package.

On the other hand, Seaborn is a Python data visualization package built on top of Matplotlib. It provides much simpler high-level code with many features included within the package. The package is great if you want fast data visualization with a nice look.

Congratulations on completing week one! ??

The KDnuggets team hopes that Back to Basics has provided readers with a comprehensive, structured approach to mastering the fundamentals of data science.

Week 2 will be published next week on Monday – stay tuned!

Nisha Arya He is a freelance data scientist and technical writer. She is particularly interested in providing career advice or educational programs and theoretical knowledge about data science. She also wants to explore different ways in which AI can benefit human longevity. An enthusiastic learner, she seeks to expand her technical knowledge and writing skills, while helping to mentor others.

You may also like...

Leave a Reply

Your email address will not be published. Required fields are marked *