storytelling with data

by Cole Nussbaumer Knaflic


📄 284 pages

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#datascience #datavisualization
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Leap Learning

LEAP by Thoughtjumper is an intelligent learning tool designed to enhance the learning experience. It aims to guide individuals in effective learning across various domains such as business, data science, technology, design, and more.

The tool offers learning quests in a wide range of subjects, allowing users to select their desired topics such as web development, digital marketing, data science, finance, and more.LEAP is focused on helping users learn faster and better.

It provides an intelligent guidance system that adapts to individual learning preferences. By decluttering distractions, LEAP allows users to solely focus on their learning, leading to a more immersive experience.

💰Price: Free

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9 types of data visualization

In this article, I will guide you through the wonderful world of data visualization and expand your knowledge about the way you can display your data and how to tell your data story to your specific audience.

Let’s start with data visualization in its most basic form; the (static) chart. Charts are used to display large amounts of data in a condensed and easy-to-understand manner. They are graphical representations of data which makes it easy and fast to digest by the brain. Moreover, charts make it apparent to find hidden information and insights that are otherwise hard to find from a table with data.

There are a lot of types of charts, each with its own function. The most commonly known charts are the bar chart, the line chart, and the pie chart. Charts form the basis for all types of data visualizations I will discuss in this blog.

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Ocean Data in Canada

Learn what ocean data are, how they're being used, and the ways in which you can access open ocean data.

Rating ⭐️: 4.7 out 5
Students 👨‍🎓 : 1368
Duration ⏰ : 49min of on-demand video
Created by 👨‍🏫: Katherine Luber, Jacob Thompson, Shayla Fitzsimmons

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Your Ultimate guide to Permutations

Have you ever marveled at how many ways you can arrange a set of items when the order truly matters? In this article, I will explain permutations, exploring how they help determine the number of possible arrangements in a set.

If you find my articles interesting, don’t forget to clap and follow 👍🏼, these articles take times and effort to do!

Permutations

“A permutation is a mathematical technique that determines the number of possible arrangements in a set when the order of the arrangements matters. Common mathematical problems involve choosing only several items from a set of items in a certain order. “[1]

Types of permutations

1 / Permutations Without Repetition : used when each item in the set can only appear once in each arrangement.

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Data Science Core Concepts 2023

Data Science Core Concepts

Rating ⭐️: 4.8 out 5
Students 👨‍🎓 : 1551
Duration ⏰ : 1hr 49min of on-demand video
Created by 👨‍🏫: Python Only Geeks

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Ray

Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Creator: ray-project
Stars ⭐️: 33.3k
Forked By: 5.6k
https://github.com/ray-project/ray

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Mastering Probability and Combinatorics

"Mastering the Essentials: Probability and Combinatorics Explained"

Rating ⭐️: 4.0 out 5
Students 👨‍🎓 : 1,129
Duration ⏰ : 1hr 24min of on-demand video
Created by 👨‍🏫: Akhil Vydyula

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Data Science Portfolios, Speeding Up Python, KANs, and Other May Must-Reads

Python One Billion Row Challenge — From 10 Minutes to 4 Seconds
With a longstanding reputation for slowness, you’d think that Python wouldn’t stand a chance at doing well in the popular “one billion row” challenge. Dario Radečić’s viral post aims to show that with some flexibility and outside-the-box thinking, you can still squeeze impressive time savings out of your code.

N-BEATS — The First Interpretable Deep Learning Model That Worked for Time Series Forecasting
Anyone who enjoys a thorough look into a model’s inner workings should bookmark Jonte Dancker’s excellent explainer on N-BEATS, the “first pure deep learning approach that outperformed well-established statistical approaches” for time-series forecasting tasks.

Build a Data Science Portfolio Website with ChatGPT: Complete Tutorial
In a competitive job market, data scientists can’t afford to be coy about their achievements and expertise. A portfolio website can be a powerful way to showcase both, and Natassha Selvaraj’s patient guide demonstrates how you can build one from scratch with the help of generative-AI tools.

A Complete Guide to BERT with Code
Why not take a step back from the latest buzzy model to learn about those precursors that made today’s innovations possible? Bradney Smith invites us to go all the way back to 2018 (or several decades ago, in AI time) to gain a deep understanding of the groundbreaking BERT (Bidirectional Encoder Representations from Transformers) model.

Why LLMs Are Not Good for Coding — Part II
Back in the present day, we keep hearing about the imminent obsolescence of programmers as LLMs continue to improve. Andrea Valenzuela’s latest article serves as a helpful “not so fast!” interjection, as she focuses on their inherent limitations when it comes to staying up-to-date with the latest libraries and code functionalities.

PCA & K-Means for Traffic Data in Python
What better way to round out our monthly selection than with a hands-on tutorial on a core data science workflow? In her debut TDS post, Beth Ou Yang walks us through a real-world example—traffic data from Taiwan, in this case—of using principle component analysis (PCA) and K-means clustering.

12 Fundamental Math Theories Needed to Understand AI

1. Curse of Dimensionality
This phenomenon occurs when analyzing data in high-dimensional spaces. As dimensions increase, the volume of the space grows exponentially, making it challenging for algorithms to identify meaningful patterns due to the sparse nature of the data.
2. Law of Large Numbers
A cornerstone of statistics, this theorem states that as a sample size grows, its mean will converge to the expected value. This principle assures that larger datasets yield more reliable estimates, making it vital for statistical learning methods.
3. Central Limit Theorem
This theorem posits that the distribution of sample means will approach a normal distribution as the sample size increases, regardless of the original distribution. Understanding this concept is crucial for making inferences in machine learning.
4. Bayes’ Theorem
A fundamental concept in probability theory, Bayes’ Theorem explains how to update the probability of your belief based on new evidence. It is the backbone of Bayesian inference methods used in AI.
5. Overfitting and Underfitting
Overfitting occurs when a model learns the noise in training data, while underfitting happens when a model is too simplistic to capture the underlying patterns. Striking the right balance is essential for effective modeling and performance.
6. Gradient Descent
This optimization algorithm is used to minimize the loss function in machine learning models. A solid understanding of gradient descent is key to fine-tuning neural networks and AI models.
7. Information Theory
Concepts like entropy and mutual information are vital for understanding data compression and feature selection in machine learning, helping to improve model efficiency.
8. Markov Decision Processes (MDP)
MDPs are used in reinforcement learning to model decision-making scenarios where outcomes are partly random and partly under the control of a decision-maker. This framework is crucial for developing effective AI agents.
9. Game Theory
Old school AI is based off game theory. This theory provides insights into multi-agent systems and strategic interactions among agents, particularly relevant in reinforcement learning and competitive environments.
10. Statistical Learning Theory
This theory is the foundation of regression, regularization and classification. It addresses the relationship between data and learning algorithms, focusing on the theoretical aspects that govern how models learn from data and make predictions.
11. Hebbian Theory
This theory is the basis of neural networks, “Neurons that fire together, wire together”. Its a biology theory on how learning is done on a cellular level, and as you would have it — Neural Networks are based off this theory.
12. Convolution (Kernel)
Not really a theory and you don’t need to fully understand it, but this is the mathematical process on how masks work in image processing. Convolution matrix is used to combine two matrixes and describes the overlap.

Special thanks to Jiji Veronica Kim for this list.


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streamlit

Streamlit — A faster way to build and share data apps.

Creator: Streamlit
Stars ⭐️: 35.4k
Forked By: 3.1k
https://github.com/streamlit/streamlit

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