Guides, tutorials, & news from Codistwa

Today, we're diving into a topic that's been on many minds lately: Learning Data Science. You might have seen the buzz around it, maybe even tried to tackle it yourself. But here's the thing - learning Data Science doesn't need to be difficult! If you're finding it tough, you're not alone. But it might just be that you haven't found the right method yet. So, let's talk about how you can make your Data Science journey smoother and more enjoyable.

Today, we're diving into the world of machine learning and debunking some common myths that might be holding you back. If you've ever felt intimidated by the idea of learning machine learning, this post is for you. So, let's jump right in!

Today, we're diving into the world of machine learning and debunking some common myths that might be holding you back. If you've ever felt intimidated by the idea of learning machine learning, this post is for you. So, let's jump right in!

If you want to succeed, getting started with data science should be a no-brainer. Let me guide you with these simple tips.

Is Kaggle good for learning data science? Learn how to break into data science with kaggle

In today's rapidly evolving data science landscape, creating a compelling portfolio and crafting an impressive resume has become essential in order to stand out in a highly competitive job market. However, many aspiring data scientists find it challenging to create a portfolio and resume that truly showcases their skills and potential. If you're struggling with your data science portfolio/resume, don't worry - this article is here to guide you through the pitfalls and provide actionable solutions to help you elevate your game.

In today's competitive job market, technical interviews are crucial for many companies, particularly in the tech industry. These interviews assess problem-solving skills, technical knowledge, and ability to work under pressure. However, not all candidates succeed. In this article, we'll discuss common reasons why technical interviews fail and how to avoid these pitfalls.

Learning data science can be a daunting task and very overwhelming if we don't have a plan. One day I heard about the Feynman Technique, named after the renowned physicist Richard Feynman.

In this article, we'll explore five common reasons why you might be facing difficulties and provide actionable solutions to help you overcome them.

Do you know the differences between AI, Machine Learning, and NLP? While the terms are often used interchangeably, each field has its own unique focus and applications.

When working with datasets, data scientists and data analysts use Exploratory Data Analysis (EDA) to gain insights and understand the data. EDA involves analyzing and summarizing the data to identify patterns, anomalies, and hypotheses through visualization and examination.

December 2019. Little did I know, I was in for an extremely difficult month. My career as an employee was slowly but surely heading toward a huge letdown.

It's important to understand basic concepts such as probability, descriptive statistics, statistical inference, and mathematics such as linear algebra and optimizations.

Data Science is a rapidly evolving and complex discipline, so it's not surprising that many people feel overwhelmed by the sheer amount of knowledge that needs to be acquired. However, there are ways to overcome this frustration and keep moving forward. Here are five tips to help you achieve your data science learning goals.

How do we know when a credit card is fraudulent or not? This is a classification problem statement.

How do we know when a credit card is fraudulent or not? This is a classification problem statement.

The Gini index is used for decision trees. Indeed, how do we know how to separate the root node? Well, there are a couple of methods, and the Gini index is a good one. It allows checking if the leaves containing labels are pure or impure.