Data science/ML/AI

Data Science Full Course For Beginners
⏰ 24 hours long

Created by IBM ✅

https://www.youtube.com/watch?v=WlLgysXJ0Ec

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SQL Mindmap

𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬 vs 𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬

Selecting the right database depends on your data needs—vector databases excel in similarity searches and embeddings, while graph databases are best for managing complex relationships between entities.


𝐕𝐞𝐜𝐭𝐨𝐫 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Data Encoding: Vector databases encode data into vectors, which are numerical representations of the data.
- Partitioning and Indexing: Data is partitioned into chunks and encoded into vectors, which are then indexed for efficient retrieval.
- Ideal Use Cases: Perfect for tasks involving embedding representations, such as image recognition, natural language processing, and recommendation systems.
- Nearest Neighbor Searches: They excel in performing nearest neighbor searches, finding the most similar data points to a given query efficiently.
- Efficiency: The indexing of vectors enables fast and accurate information retrieval, making these databases suitable for high-dimensional data.

𝐆𝐫𝐚𝐩𝐡 𝐃𝐚𝐭𝐚𝐛𝐚𝐬𝐞𝐬:
- Relational Information Management: Graph databases are designed to handle and query relational information between entities.
- Node and Edge Representation: Entities are represented as nodes, and relationships between them as edges, allowing for intricate data modeling.
- Complex Relationships: They excel in scenarios where understanding and navigating complex relationships between data points is crucial.
- Knowledge Extraction: By indexing the resulting knowledge base, they can efficiently extract sub-knowledge bases, helping users focus on specific entities or relationships.
- Use Cases: Ideal for applications like social networks, fraud detection, and knowledge graphs where relationships and connections are the primary focus.

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧:
Choosing between a vector and a graph database depends on the nature of your data and the type of queries you need to perform. Vector databases are the go-to choice for tasks requiring similarity searches and embedding representations, while graph databases are indispensable for managing and querying complex relationships.

Source: Ashish Joshi

15 different Careers in AI

Proficiency in data science skills by job role

Python for Deep Learning: Build Neural Networks in Python

Complete Deep Learning Course to Master Data science, Tensorflow, Artificial Intelligence, and Neural Networks

Rating ⭐️: 4.2 out 5
Students 👨‍🎓 : 145651
Duration ⏰ : 2 hours on-demand video
Created by 👨‍🏫: Meta Brains, school of AI

🔗 Course Link

⚠️ Its free for first 1000 enrollments only!


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Data Science

common data analysis and machine learning tasks using python

Creator: Ujjwal Karn
Stars ⭐️: 5.3k
Forked By: 1.5k
GithubRepo: https://github.com/ujjwalkarn/DataSciencePython


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Roadmap To Master Machine Learning

Big Data Pipeline Cheatsheet

Begin to Use Cloud Computing with Anaconda Cloud Notebook

Begin to use Cloud Computing and Anaconda Cloud Notebook with Python, Data Science and Machine Learning [2024]

Rating ⭐️: 4.9 out 5
Students 👨‍🎓 : 1,028
Duration ⏰ : 40min on-demand video
Created by 👨‍🏫: Henrik Johansson

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🌳 What is a Decision Tree? 🌳

Imagine you're trying to figure out what to eat for dinner. 🍕🥗🍔 A decision tree is like a flowchart that helps you make choices based on yes/no questions:

Are you in the mood for something light?
Yes ➡️ Salad 🥗
No ➡️ Are you craving something cheesy?
Yes ➡️ Pizza 🍕
No ➡️ Burger 🍔

That's the essence of how decision trees work in machine learning!

🤖 In Machine Learning Terms:

Nodes: Questions (e.g., Is the price > $50?)
Branches: Possible answers (e.g., Yes/No)
Leaves: Final decisions or predictions (e.g., "Expensive" or "Affordable")

📊 They're used for tasks like:
✅ Classifying emails as spam or not.
✅ Predicting if a customer will buy a product.
✅ Diagnosing diseases in healthcare.

🎯 Why are they Awesome?

Simple to understand (even for non-techies).
Visual and interpretable (you can see the logic behind predictions).
Great for small-to-medium datasets.

⚡️ Limitations:

They can "overfit" (become too specific).
Not the best for very large datasets or complex problems.

🛠 Pro Tip:
To handle overfitting, use Random Forests 🌲🌲 or Gradient Boosted Trees 🚀—advanced versions of decision trees.

What do you think about decision trees? Drop your 🌳 below if you love their simplicity!

Top Machine Learning algorithms

Data Science Life Cycle

Data Science for Value-Chain Management

How can you leverage data science to optimize operations and boost profitability?

Value Chain Management (VCM) refers to organizing activities that add value to the goods or services to achieve a competitive advantage in the marketplace.

This method helps organizations to effectively respond to market trends and improve efficiency to boost profitability.

We quickly delve into the fundamental components of Value Chain Management.

We will then explore four examples of data science applications to support strategic primary activities.

The value chain framework was originally introduced in Michael Porter's book “Competitive Advantage: Creating and Sustaining Superior Performance”.

This revolutionized how businesses perceive their operations by dissecting any business into a series of interconnected activities that contribute to creating and delivering value to customers.

Hands On Python Data Science - Data Science Bootcamp

Master Python for Data Science with Real-World Applications: Dive Deep into Data Analysis, Machine Learning

Rating ⭐️: 4.3 out 5
Students 👨‍🎓 : 4865
Duration ⏰ : 5.5 hours on-demand video
Created by 👨‍🏫: Sayman Creative Institute

🔗 COURSE LINK

⚠️ Its free for first 1000 enrollments only!


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The Data Science Process

Exploratory Data Analysis

macos

OSX (macOS) inside a Docker container.

Creator: Dockur
Stars ⭐️: 5.2k
Forked By: 185
https://github.com/dockur/macos

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Top 10 Data Libraries for Python

Characteristics of a Data whisperer

Data Science Trends in 2024

Forecasting vs. Predictive Analytics: The Obama Example
Analytics can influence elections, not just predict them. This article explores how the Obama campaign used predictive analytics to outmaneuver traditional forecasting.

Forecasting vs. Predictive Analytics
Nate Silver’s forecasting predicted state outcomes, while Obama’s team used predictive analytics to score individual voters, targeting those most likely to be persuaded.

Impact of Predictive Analytics
The Obama campaign optimized interactions, avoiding “do-not-disturb” voters and improving ad spending effectiveness by 18%.

Conclusion
Predictive analytics enables organizations to shape outcomes through personalized insights, distinguishing it from forecasting’s broad predictions.

Essential Machine Learning Algorithms for Data Scientists

Master essential machine learning algorithms and elevate your data science skills

Rating ⭐️: 4.6 out 5
Students 👨‍🎓 : 791
Duration ⏰ : 43min of on-demand video
Created by 👨‍🏫: Arunkumar Krishnan

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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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Salaries of In-demand data science jobs

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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Data Analyst Skills Required by Employers

Data Science in health care

Data Analysis Skills

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.

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

🔗 Course Link

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Data Science : Definition, Challenges and Use cases

Data Science Minimum: 10 Essential Skills You Need to Know to Start Doing Data Science

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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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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How Data Science Is Helping in Robotics and Artificial Intelligence

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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Latex Cheat Sheet of data science

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

🔗 Course Link

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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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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

🔗 Link

storytelling with data

by Cole Nussbaumer Knaflic


📄 284 pages

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Probability for Data Science

Covers the probability concepts essential for data science

Rating ⭐️: 4.7 out 5
Students 👨‍🎓 : 2917
Duration ⏰ : 1hr 56min of on-demand video
Created by 👨‍🏫: Anand Seetharam

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The four V's of big data

Data Pipeline Overview

Modern Data Scientist

What the industry needs?

Rating ⭐️: 4.5 out 5
Students 👨‍🎓 : 3158
Duration ⏰ : 1hr 40 min of on-demand video
Created by 👨‍🏫: Prof Poornachandra Sarang, Ph.D.

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Data Science Full Course - 12 Hours | Data Science For Beginners | Data Science Tutorial | Edureka

This Edureka Data Science Full Course video will help you understand and learn Data Science Algorithms in detail. This Data Science Tutorial is ideal for both beginners as well as professionals who want to master Data Science Algorithms.


✅ Free Online Course
🏃‍♂️ Self paced
Duration ⏰ : 11-12 hours long
Source: Edureka

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Virgilio Data Science

This repository contains articles, GitHub repos and Kaggle kernels which provides data science and machine learning projects with code.

Creator: virgili0
Stars ⭐️: 13.9k
Forked By: 2.5k
https://github.com/virgili0/Virgilio

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Data Science Advanced Analytics Interview Prep. Kit - 182+

A walkthrough from the essentials of 182+ data science interview questions from linear regression to advance analytics

Rating ⭐️: 4.6 out 5
Students 👨‍🎓 : 2676
Duration ⏰ : 1hr 2min of on-demand video
Created by 👨‍🏫: Rupak Bob Roy

🔗 Course Link

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8 Great Data Science Books For Beginners

In the past few years public interest in data science has surged. What had been a fairly esoteric field is now a common topic in the news, in politics and international law, and in our social media feeds. Data literacy is becoming a highly desired skill in every industry, and consumers enter data points into massive business intelligence systems every day. Whether you just want to stay informed in the data craze or you’re looking to kickstart your data science or data literacy journey, this article features a list of books that can help newcomers navigate the world of data science.

1. “The Data Science Handbook
2. “Doing Data Science: Straight Talk from the Frontline” by Cathy O'Neil and Rachel Schutt
3. “Numsense! Data Science for the Layman: No Math Added” by Annalyn Ng and Kenneth Soo
4. “The Art of Data Science” by Roger D. Peng and Elizabeth Matsui
5. “Data Science For Dummies” by Lillian Pierson
6. “Big Data For Dummies” by Judith Hurwitz, Alan Nugent, Fern Halper, and Marcia Kaufman
7. “Data Jujitsu: The Art of Turning Data into Product” by DJ Patil
8. “Big Data: A Revolution That Will Transform How We Live, Work, and Think” by Viktor Mayer-Schönberger and Kenneth Cukier

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Learning Data Science: Understanding the Basics

Many of the people who work on data science teams won't be data scientists. They'll be the managers and associates who want to gain real business value from your organization's data. These team members need to understand the language of data science so they can ask better questions, understand processes, and help effectively lead their teams and organizations to making better data-driven decisions. This course is an introduction to data science for people who aren't planning on being full-time data scientists. It introduces big data concepts, tools, and techniques, including gathering and sorting data, working with databases, understanding structured and unstructured data types, and applying statistical analysis. Business coach and author Doug Rose helps you speak the language of data science so that you can guide your organization through the opportunities and limitations in this dramatically growing field.


✅ Free Online Course
🏃‍♂️ Self paced
Duration ⏰ : 1-2 hours long
4 quizzes
Source: linkedin learning

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Data Science Platform Market Size

Data Science Full Course 2024 | Data Science Course For Beginners | Learn Data Science |

This Data Science Course 2024 covers all the important skill sets required to get you a job as a Data Scientist!

This particular Data Science Course For Beginners has been taught by our top industry experts associated with companies like Google, Meta, and Microsoft. So, stay until the end of this video, you will understand Data Science from Fundamentals To Solving Basic Data Science Problems Using Machine Learning Algorithms.

With this Data Science Course 2024, we will give you the Fundamental idea of what is data science, What Datasets are all about, Basic Machine Learning Algorithms, and how you can solve real-world problems like heart disease detection using Data science.

We will also help you with the trending project idea which is creating a Chatbot of your own like ChatGPT using data science. We will also highlight some of the important data science interview questions.


✅ Free Online Course
🏃‍♂️ Self paced
Duration ⏰ : 11-12 hours long
Source: Intellipaat

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DATA SCIENCE :THEORIES , MODELS , ALGORITHMS , AND ANALYTICS

by S A N J I V R A N J A N D A S


📄 462 pages

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Data Science BootCamp - From Analysing Data To Creating ML Models

This free Data Science Bootcamp will help you get started on the roadmap towards a career as top Data Scientist. Master the basics of Python, Tableau, ML, AI and more.


✅ Free Online Course
🏃‍♂️ Self paced
Duration ⏰ : 6 weeks long
Source: geeksforgeeks

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Become a Citizen Data Scientist with HyperSense-AI Studio

Use R Programming for Scientific Research

Rating ⭐️: 4.6 out 5
Students 👨‍🎓 : 3139
Duration ⏰ : 1.5 hours on-demand video
Created by 👨‍🏫: Learning Hypersense

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Data Science Projects

Collection of data science projects in Python

Creator: Vaibhav Singh
Stars ⭐️: 1.5k
Forked By: 406
https://github.com/veb-101/Data-Science-Projects

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7 Categorical Data Encoding Techniques

Checklist to become a Data Analyst

10 Best Practices for Data Science

The main bottleneck in data science are no longer compute power or sophisticated algorithms, but craftsmanship, communication, and process.

And that the aim is to not only produce work that is accurate and correct, but also can be understood, work that others can collaborate on.

Rule 1: Start Organized, Stay Organized
Rule 2: Everything Comes from Somewhere, and the Raw Data is Immutable
Rule 3: Version Control is Basic Professionalism
Rule 4: Notebooks are for Exploration, Source Files are for Repetition
Rule 5: Tests and Sanity Checks Prevent Catastrophes
Rule 6: Fail Loudly, Fail Quickly
Rule 7: Project Runs are Fully Automated from Raw Data to Final Outputs
Rule 8: Important Parameters are Extracted and Centralized
Rule 9: Project Runs are Verbose by Default and Result in Tangible Artifacts
Rule 10: Start with the Simplest Possible End-to-End Pipeline
Lessons

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How To Use R Programming for Research

Use R Programming for Scientific Research

Rating ⭐️: 4.5 out 5
Students 👨‍🎓 : 19,897
Duration ⏰ : 1.5 hours on-demand video
👩‍💻 2 coding exercises
⬇️ 29 downloadable resources
Created by 👨‍🏫: Prof Asad Rasul

🔗 COURSE LINK

⚠️ Its free for first 1000 enrollments only!


#R_Programming
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Practical Deep Learning For Coders

This 7-week course is designed for anyone with at least a year of coding experience, and some memory of high-school math. You will start with step onelearning how to get a GPU server online suitable for deep learningand go all the way through to creating state of the art, highly practical, models for computer vision, natural language processing, and recommendation systems.

🆓 Free Online Course
Rating⭐️: 4.1 out 5
Duration ⏰: 7 weeks
💻 Lecture Videos
🏃‍♂️ Self paced
Teacher 👨‍🏫 : Prof. Jeremy Howard

🔗 Course Link

#programming #deeplearning
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Big Data

Building the machine learning model

Applications of Deep Learning

Data Science vs Mathematics

Python for Data Science with Assignments

A Comprehensive and Practical Hands-On Guide to Learning Python for Beginners, Aspiring Developers, Self-Learners, etc.

Rating ⭐️: 4.7 out 5
Students 👨‍🎓 : 18046
Duration ⏰ : 9.5 hours on-demand video
Created by 👨‍🏫: Meritshot Academy

🔗 Course Link

⚠️ Its free for first 1000 enrollments only!


#python #datascience
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Completely unimportant but an interesting fact
we have 7777 subscribers ATM

Statistics test flow chart

Accelerate Data Science Workflows with Zero Code Changes
by nvidia

Across industries, modern data science requires large amounts of data to be processed quickly and efficiently. These workloads need to be accelerated to ensure prompt results and increase overall productivity. NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. In this workshop, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows.
By participating in this course, you will:
Understand the benefits of a unified workflow across CPUs and GPUs for data science tasks
Learn how to GPU-accelerate various data processing and machine learning workflows with zero code changes
Experience the significant reduction in processing time when workflows are GPU-accelerated

Prerequisites:
Basic understanding of data processing and knowledge of a standard data science workflow on tabular data
Experience using common Python libraries for data analytics
Tools, libraries, frameworks used: NVIDIA RAPIDS (cuDF, cuML, cuGraph), pandas, scikit-learn, and NetworkX


🆓 Free Online Course
⏰ Duration : More than 1 hour
🏃‍♂️ Self paced
✅ Certification available

Course Link


#datascience #nvidia 
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The Data Science Sandwich

Data Science Techniques

Important Data Terms

Statistical models cheatsheet

Statistical distributions cheatsheet