📌 TDS Newsletter: What Happens When AI Reaches Its Limits?

🗂 Category: THE VARIABLE

🕒 Date: 2025-10-23 | ⏱️ Read time: 4 min read

From afar, new LLMs and the applications they power seem shiny, or even magical. The unrelenting pace…

🤖🧠 PandasAI: Transforming Data Analysis with Conversational Artificial Intelligence

🗓️ 28 Oct 2025
📚 AI News & Trends

In a world dominated by data, the ability to analyze and interpret information efficiently has become a core competitive advantage. From business intelligence dashboards to large-scale machine learning models, data-driven decision-making fuels innovation across industries. Yet, for most people, data analysis remains a technical challenge requiring coding expertise, statistical knowledge and familiarity with libraries like ...

#PandasAI #ConversationalAI #DataAnalysis #ArtificialIntelligence #DataScience #MachineLearning

📌 Writing Powerful Programming Articles: A Guide for Success

🗂 Category: DATA SCIENCE

🕒 Date: 2024-05-31 | ⏱️ Read time: 7 min read

Reflections on 4+ Years of Publishing Programming Articles

🤖🧠 Microsoft Data Formulator: Revolutionizing AI-Powered Data Visualization

🗓️ 28 Oct 2025
📚 AI News & Trends

In today’s data-driven world, visualization is everything. Whether you’re a business analyst, data scientist or researcher, the ability to convert raw data into meaningful visuals can define the success of your decisions. That’s where Microsoft’s Data Formulator steps in a cutting-edge, open-source platform designed to empower analysts to create rich, AI-assisted visualizations effortlessly. Developed by ...

#Microsoft #DataVisualization #AI #DataScience #OpenSource #Analytics

📌 Using NumPy to Analyze My Daily Habits (Sleep, Screen Time & Mood)

🗂 Category: DATA SCIENCE

🕒 Date: 2025-10-28 | ⏱️ Read time: 7 min read

Can I use NumPy to figure out how my habits affect my mood and productivity?

🤖🧠 Google’s GenAI MCP Toolbox for Databases: Transforming AI-Powered Data Management

🗓️ 28 Oct 2025
📚 AI News & Trends

In the era of artificial intelligence, where data fuels innovation and decision-making, the need for efficient and intelligent data management tools has never been greater. Traditional methods of database management often require deep technical expertise and manual oversight, slowing down development cycles and creating operational bottlenecks. To address these challenges, Google has introduced the GenAI ...

#Google #GenAI #Database #AIPowered #DataManagement #MachineLearning

🤖🧠 Wren AI: Transforming Business Intelligence with Generative AI

🗓️ 28 Oct 2025
📚 AI News & Trends

In the evolving world of data and analytics, one thing is certain — the ability to transform raw data into actionable insights defines success. Organizations today are generating more data than ever before, yet accessing and understanding that data remains a significant challenge. Traditional business intelligence tools require technical expertise, SQL knowledge and manual configuration. ...

#WrenAI #GenerativeAI #BusinessIntelligence #DataAnalytics #AI #Insights

📌 Deep Reinforcement Learning: 0 to 100

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2025-10-28 | ⏱️ Read time: 24 min read

Using RL to teach robots to fly a drone

💡 Building a Simple Convolutional Neural Network (CNN)

Constructing a basic Convolutional Neural Network (CNN) is a fundamental step in deep learning for image processing. Using TensorFlow's Keras API, we can define a network with convolutional, pooling, and dense layers to classify images. This example sets up a simple CNN to recognize handwritten digits from the MNIST dataset.

import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras.datasets import mnist
import numpy as np

# 1. Load and preprocess the MNIST dataset
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# Reshape images for CNN: (batch_size, height, width, channels)
# MNIST images are 28x28 grayscale, so channels = 1
train_images = train_images.reshape((60000, 28, 28, 1)).astype('float32') / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float32') / 255

# 2. Define the CNN architecture
model = models.Sequential()

# First Convolutional Block
model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))

# Second Convolutional Block
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))

# Flatten the 3D output to 1D for the Dense layers
model.add(layers.Flatten())

# Dense (fully connected) layers
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax')) # Output layer for 10 classes (digits 0-9)

# 3. Compile the model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Print a summary of the model layers
model.summary()

# 4. Train the model (uncomment to run training)
# print("\nTraining the model...")
# model.fit(train_images, train_labels, epochs=5, batch_size=64, validation_split=0.1)

# 5. Evaluate the model (uncomment to run evaluation)
# print("\nEvaluating the model...")
# test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
# print(f"Test accuracy: {test_acc:.4f}")

Code explanation: This script defines a simple CNN using Keras. It loads and normalizes MNIST images. The Sequential model adds Conv2D layers for feature extraction, MaxPooling2D for downsampling, a Flatten layer to transition to 1D, and Dense layers for classification. The model is then compiled with an optimizer, loss function, and metrics, and a summary of its architecture is printed. Training and evaluation steps are included as commented-out examples.

#Python #DeepLearning #CNN #Keras #TensorFlow

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By: @DataScienceM ✨

💡 Python: Simple K-Means Clustering Project

K-Means is a popular unsupervised machine learning algorithm used to partition n observations into k clusters, where each observation belongs to the cluster with the nearest mean (centroid). This simple project demonstrates K-Means on the classic Iris dataset using scikit-learn to group similar flower species based on their measurements.

import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import numpy as np

# 1. Load the Iris dataset
iris = load_iris()
X = iris.data # Features (sepal length, sepal width, petal length, petal width)
y = iris.target # True labels (0, 1, 2 for different species) - not used by KMeans

# 2. (Optional but recommended) Scale the features
# K-Means is sensitive to the scale of features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 3. Define and train the K-Means model
# We know there are 3 species in Iris, so we set n_clusters=3
kmeans = KMeans(n_clusters=3, random_state=42, n_init=10) # n_init is important for robust results
kmeans.fit(X_scaled)

# 4. Get the cluster assignments for each data point
labels = kmeans.labels_

# 5. Get the coordinates of the cluster centroids
centroids = kmeans.cluster_centers_

# 6. Visualize the clusters (using first two features for simplicity)
plt.figure(figsize=(8, 6))

# Plot each cluster
colors = ['red', 'green', 'blue']
for i in range(3):
    plt.scatter(X_scaled[labels == i, 0], X_scaled[labels == i, 1],
                s=50, c=colors[i], label=f'Cluster {i+1}', alpha=0.7)

# Plot the centroids
plt.scatter(centroids[:, 0], centroids[:, 1],
            s=200, marker='X', c='black', label='Centroids', edgecolor='white')

plt.title('K-Means Clustering on Iris Dataset (Scaled Features)')
plt.xlabel('Scaled Sepal Length')
plt.ylabel('Scaled Sepal Width')
plt.legend()
plt.grid(True)
plt.show()

# You can also compare with true labels (for evaluation, not part of clustering process itself)
# print("True labels:", y)
# print("K-Means labels:", labels)

Code explanation: This script loads the Iris dataset, scales its features using StandardScaler, and then applies KMeans to group the data into 3 clusters. It visualizes the resulting clusters and their centroids using a scatter plot with the first two scaled features.

#Python #MachineLearning #KMeans #Clustering #DataScience

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By: @DataScienceM ✨

📌 Using Claude Skills with Neo4j

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2025-10-28 | ⏱️ Read time: 11 min read

A hands-on exploration of Claude Skills and their potential applications in Neo4j

📌 Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2025-10-28 | ⏱️ Read time: 9 min read

Understanding how AI models “reason” and why it’s not what humans do when we think

📌 Orchestrating a Dynamic Time-series Pipeline in Azure

🗂 Category: DATA ENGINEERING

🕒 Date: 2024-05-31 | ⏱️ Read time: 9 min read

Explore how to build, trigger, and parameterize a time-series data pipeline with ADF and Databricks,…

📌 Training Naive Bayes… Really Fast

🗂 Category: MACHINE LEARNING

🕒 Date: 2024-05-31 | ⏱️ Read time: 14 min read

Performance tuning in Julia

📌 Terraforming Dataform

🗂 Category: DATA ENGINEERING

🕒 Date: 2024-05-31 | ⏱️ Read time: 7 min read

Dataform 101, Part 2: Provisioning with Least Privilege Access Control

🧠 Quiz: What is the primary objective of data mining?

A) To physically store large volumes of data
B) To discover patterns, trends, and useful insights from large datasets
C) To design and implement database management systems
D) To encrypt and secure sensitive data

✅ Correct answer: B

Explanation: Data mining is a process used to extract valuable, previously unknown patterns, trends, and knowledge from large datasets. Its goal is to find actionable insights that can inform decision-making.

#DataMining #BigData #Analytics

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By: @DataScienceM ✨

📌 Computing Minimum Sample Size for A/B Tests in Statsmodels: How and Why

🗂 Category: DATA SCIENCE

🕒 Date: 2024-05-31 | ⏱️ Read time: 11 min read

A deep-dive into how and why Statsmodels uses numerical optimization instead of closed-form formulas

📌 PyTorch Introduction – Training a Computer Vision Algorithm

🗂 Category: ARTIFICIAL INTELLIGENCE

🕒 Date: 2024-05-30 | ⏱️ Read time: 11 min read

In this post of the PyTorch Introduction, we’ll learn how to train a computer vision…

📌 Interpretable Features in Large Language Models

🗂 Category: LARGE LANGUAGE MODELS

🕒 Date: 2024-05-30 | ⏱️ Read time: 9 min read

And other interesting tidbits from the new Anthropic Paper

📌 What 10 Years at Uber, Meta and Startups Taught Me About Data Analytics

🗂 Category: CAREER ADVICE

🕒 Date: 2024-05-30 | ⏱️ Read time: 11 min read

Advice for Data Scientists and Managers