In Data Science you can find multiple data distributions...

But where are they typically found?

Check examples of 4 common distributions:

1️⃣ Normal Distribution:
Often found in natural and social phenomena where many factors contribute to an outcome. Examples include heights of adults in a population, test scores, measurement errors, and blood pressure readings.

2️⃣ Uniform Distribution:
This appears when every outcome in a range is equally likely. Examples include rolling a fair die (each number has an equal chance of appearing) and selecting a random number within a fixed range.

3️⃣ Binomial Distribution:
Used when you're dealing with a fixed number of trials or experiments, each of which has only two possible outcomes (success or failure), like flipping a coin a set number of times, or the number of defective items in a batch.

4️⃣ Poisson Distribution:
Common in scenarios where you're counting the number of times an event happens over a specific interval of time or space. Examples include the number of phone calls received by a call centre in an hour or the probability of taxi frequency.


Each distribution offers insights into the underlying processes of the data and is useful for different kinds of statistical analysis and prediction.

Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:

1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.

Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.

Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.

2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.

These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.

Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.

3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.

Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.

4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.

LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.

5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.

Python Roadmap for Data Science in 2024

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Learn Data Cleaning with Python

Perform Data Cleaning Techniques with the Python Programming Language. Practice and Solution Notebooks included.

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🚀 Deep Learning CNN Project: Cat vs Dog Classification

🔍 Key Highlights:
📸 25,000 training images, 12,500 testing images
🧠 Custom fully connected layers
➡️ Binary Cross-Entropy loss function
⚙️ Exponential decay and learning rate schedule

🛠 Tools & Libraries:
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Data Analytics Skills that will get you hired

𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴

𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 is an indispensable stage in the data science workflow, crucial for the success of downstream processes such as analytics and machine learning modeling. It involves a comprehensive set of operations that prepare raw data for further processing and analysis. This stage is fundamental because it directly impacts the quality of insights derived from the data and the performance of predictive models.

𝗧𝗵𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝗰𝗲 𝗼𝗳 𝗱𝗮𝘁𝗮 𝗽𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 stems from the fact that real-world data is often incomplete, inconsistent, and lacking in certain behaviors or trends. It may contain errors, outliers, or noise that can significantly distort results and lead to misleading conclusions.
𝗧𝗵𝗲𝗿𝗲𝗳𝗼𝗿𝗲, preprocessing aims to clean and organize the data, enhancing its quality and making it more suitable for analysis.

👉 I’ve compiled the following list which includes 𝗼𝘃𝗲𝗿 𝗮 𝟭𝟱𝟬 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗱𝗮𝘁𝗮 𝗽𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀, ranging from basic data cleaning techniques like handling missing values and outliers to more advanced procedures like 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴, 𝗵𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝗶𝗺𝗯𝗮𝗹𝗮𝗻𝗰𝗲𝗱 𝗱𝗮𝘁𝗮𝘀𝗲𝘁𝘀, 𝗮𝗻𝗱 𝗽𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗳𝗼𝗿 𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗱𝗮𝘁𝗮 𝘁𝘆𝗽𝗲𝘀 𝗹𝗶𝗸𝗲 𝘁𝗲𝘅𝘁 𝗮𝗻𝗱 𝗶𝗺𝗮𝗴𝗲𝘀.

Mastery of these techniques is crucial for anyone looking to delve into data science, as they lay the groundwork for all subsequent steps in the data analysis and machine learning pipeline.

Business Analytics vs Data Analytics

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Best Platforms to Learn Business Analytics

Deep Learning for Beginners
Deep Learning: A Visual Approach

The Foundation of Data Science

Data Science for Beginners: Your Step-by-Step Guide To Start

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Data-Scientist-In-Python

This repository contains notes and projects of Data scientist track from dataquest course work.

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YouTube Channels for Business Analyst