Back to Basics Week 1: Python Programming & Data Science Foundations

Cultivate your data science expertise with KDnuggets’ Back to Basics pathway, which includes Python, data manipulation, and visualization.

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Join KDnuggets with our Back to Basics pathway to get you kickstarted with a new career or a brush up on your data science skills. The Back to Basics pathway is split up into 4 weeks with a bonus week. We hope you can use these blogs as a course guide.

In the first week, we will be learning all about Python, Data Manipulation, and Visualisation.

Day 1 to 3 : Python Essentials for Aspiring Data Scientists An introduction to Python’s role in data science. A beginner-friendly guide to Python’s syntax, data types, and control structures. Interactive coding exercises to solidify your understanding.

: Python Essentials for Aspiring Data Scientists Day 4 : Python Data Structures Demystified Learn about Python’s core data structures with our step-by-step guide. You’ll learn about lists, tuples, dictionaries, and sets—each with practical examples and their significance in data processing.

: Python Data Structures Demystified Day 5 to 6 : Practical Numerical Computation with NumPy and Pandas Discover the power of NumPy and Pandas for numerical analysis and data manipulation, including real-world applications and hands-on exercises.

: Practical Numerical Computation with NumPy and Pandas Day 7 : Data Cleaning Techniques with Pandas Equip yourself with essential data-cleaning skills using Pandas.

: Data Cleaning Techniques with Pandas

Let’s get started.

Getting Started with Python for Data Science

Week 1 – Part 1: Getting Started with Python for Data Science

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

Generative AI, ChatGPT, Google Bard – these are probably a lot of terms you’ve been hearing over the past few months. With this uproar, a lot of you are thinking about getting into the tech field, such as Data Science.

People from different roles want to keep their jobs, so they will aim to develop their skills to fit the current market. It is a competitive market, and we are seeing more and more people building interest in Data Science, where there are thousands of courses online, bootcamps, and Masters (MSc) available in the sector.

Python Basics: Syntax, Data Types, and Control Structures

Week 1 – Part 2: Python Basics: Syntax, Data Types, and Control Structures

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

Are you a beginner looking to learn programming with Python? If so, this beginner-friendly tutorial is for you to familiarize yourself with the basics of the language. This tutorial will introduce you to Python’s—rather English-friendly—syntax. You’ll also learn to work with different data types, conditional statements, and loops in Python.

If you already have Python installed in your development and environment, start a Python REPL and code along. Or if you want to skip the installation—and start coding right away—I recommend heading over to Google Colab and coding along.

Getting Started with Python Data Structures in 5 Steps

Week 1 – Part 3: Getting Started with Python Data Structures in 5 Steps

This tutorial covers Python’s foundational data structures – lists, tuples, dictionaries, and sets. Learn their characteristics, use cases, and practical examples, all in 5 steps.

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

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

Introduction to Numpy and Pandas

Week 1 – Part 4: Introduction to Numpy and Pandas

A primer on using Numpy and Pandas for numerical computation and data manipulation in Python.

If you are working on a data science project, Python packages will ease your life since you just need a few lines of code to do complicated operations, like manipulating the data and applying a machine learning/deep learning model.

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

Data Cleaning with Pandas

Week 1 – Part 5: Data Cleaning with Pandas

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

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

Therefore, it is necessary to prepare your data before it is fed to your model. This preparation of the data by identifying and resolving the potential errors, inaccuracies, and inconsistencies is termed as Data Cleaning.

Data Visualization: Theory and Techniques

Week 1 – Part 6: Data Visualization: Theory and Techniques

Unlocking the secrets of how to observe our data-driven world.

In a digital landscape dominated by big data and intricate algorithms, one would think that the average person is lost in an ocean of numbers and data. Isn’t it?

Yet, the bridge between raw data and comprehensible insights lies in the art of Data Visualization. It’s the compass that directs us, the map that guides us, and the interpreter that decodes the mass amount of data that we encounter daily.

But what’s the magic behind a good visualization? Why does one visualization enlighten while another confuses?

Creating Visuals with Matplotlib and Seaborn

Week 1 – Part 7: Creating Visuals with Matplotlib and Seaborn

Learn the basic Python package visualization for your work.

Data visualization is essential in data work as it helps people understand what happens with our data. It’s hard to ingest the data information directly in a raw form, but visualization would spark people’s interest and engagement. This is why learning data visualization is important to succeed in the data field.

Matplotlib is one of Python’s most popular data visualization libraries because it’s very versatile, and you can visualize virtually everything from scratch. You can control many aspects of your visualization with this package.

On the other hand, Seaborn is a Python data visualization package that is built on top of Matplotlib. It offers much simpler high-level code with various built-in themes inside the package. The package is great if you want a quick data visualization with a nice look.

Wrapping it Up

Congratulations on completing week 1! ??

The team at KDnuggets hope that the Back to Basics pathway has provided readers with a comprehensive and structured approach to mastering the fundamentals of data science.

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

Nisha Arya is a Data Scientist and Freelance Technical Writer. She is particularly interested in providing Data Science career advice or tutorials and theory based knowledge around Data Science. She also wishes to explore the different ways Artificial Intelligence is/can benefit the longevity of human life. A keen learner, seeking to broaden her tech knowledge and writing skills, whilst helping guide others.

Source: www.kdnuggets.com