The Big Data bible from Stanford: MapReduce, Spark, recommendation systems, PageRank, locality-sensitive hashing, Large scale machine learning and mining social networks/streams all explained clearly with real algorithms you can code today. 500 pages of pure gold.

If you want to become a Data Scientist, this is the path to follow.

🛠️ Running Code in Jupyter Notebooks

Jupyter Notebooks let you write & run code interactively.
Here’s a quick guide to make your workflow smoother:

▶️ Kernel & Code Cells
- Each notebook is tied to a single kernel (e.g. IPython).
- Code cells are where you write and execute code.

⌨️ Useful Shortcuts
- Shift + Enter → run current cell, move to next
- Alt + Enter → run current cell, insert new one below
- Ctrl + Enter → run current cell, stay in place

🔄 Kernel Management
- Interrupt the kernel if code hangs.
- Restart kernel to reset memory & variables.

🖥️ Output Handling
- Results & errors appear directly under the cell.
- Long-running code outputs appear as they’re generated.
- Large outputs can be scrolled or collapsed for clarity.

💡 Pro Tip:
Always “Restart & Run All” before sharing or saving a notebook.
This ensures reproducibility and clean results.

👉   Explore

6 Must-Know Data Engineering Tools For Beginners

Covers Spark for ML, graph processing (GraphFrames), and integration with Hadoop from Stanford University.

The Difference Between Model Accuracy and Business Accuracy

A model can be 95% accurate…
yet deliver 0% business value.

Why❔
Because data science metrics ≠ business metrics.

📌 Examples:
- A fraud model catches tiny fraud but misses large ones
- A churn model predicts already obvious churners
- A recommendation model boosts clicks but reduces revenue

Always align ML metrics with business KPIs.
Otherwise, your “great model” is just a great illusion.

✅ Complete AI (Artificial Intelligence) Roadmap 🤖🚀 

1️⃣ Basics of AI 
🔹 What is AI? 
🔹 Types: Narrow AI vs General AI 
🔹 AI vs ML vs DL 
🔹 Real-world applications 

2️⃣ Python for AI
🔹 Python syntax & libraries 
🔹 NumPy, Pandas for data handling 
🔹 Matplotlib, Seaborn for visualization 

3️⃣ Math Foundation
🔹 Linear Algebra: Vectors, Matrices 
🔹 Probability & Statistics 
🔹 Calculus basics 
🔹 Optimization techniques 

4️⃣ Machine Learning (ML)
🔹 Supervised vs Unsupervised 
🔹 Regression, Classification, Clustering 
🔹 Scikit-learn for ML 
🔹 Model evaluation metrics 

5️⃣ Deep Learning (DL)
🔹 Neural Networks basics 
🔹 Activation functions, backpropagation 
🔹 TensorFlow / PyTorch 
🔹 CNNs, RNNs, LSTMs 

6️⃣ NLP (Natural Language Processing)
🔹 Text cleaning & tokenization 
🔹 Word embeddings (Word2Vec, GloVe) 
🔹 Transformers & BERT 
🔹 Chatbots & summarization 

7️⃣ Computer Vision
🔹 Image processing basics 
🔹 OpenCV for CV tasks 
🔹 Object detection, image classification 
🔹 CNN architectures (ResNet, YOLO) 

8️⃣ Model Deployment
🔹 Streamlit / Flask APIs 
🔹 Docker for containerization 
🔹 Deploy on cloud: Render, Hugging Face, AWS 

9️⃣ Tools & Ecosystem
🔹 Git & GitHub 
🔹 Jupyter Notebooks
🔹 DVC, MLflow (for tracking models) 

🔟 Build AI Projects
🔹 Chatbot, Face recognition 
🔹 Spam classifier, Stock prediction 
🔹 Language translator, Object detector 

Normalization vs Standardization: Why They’re Not the Same

People treat these two as interchangeable. they’re not.

👉 Normalization (Min-Max scaling):
Compresses values to 0–1.
Useful when magnitude matters (pixel values, distances).

👉 Standardization (Z-score):
Centers data around mean=0, std=1.
Useful when distribution shape matters (linear/logistic regression, PCA).

🔑 Key idea:
Normalization preserves relative proportions.
Standardization preserves statistical structure.

Pick the wrong one, and your model’s geometry becomes distorted.

Hey everyone 👋

Tomorrow we are kicking off a new short & free series called:

📊 Data Importing Series 📊

We’ll go through all the real ways to pull data into Python:
→ CSV, Excel, JSON and more
→ Databases & SQL databases 
→ APIs, Google Sheets, even PDFs & web scraping

Short lessons, ready-to-copy code, zero boring theory.

First part drops tomorrow.
Turn on notifications so you don’t miss it 🔔

Who’s excited? React with a 🔥 if you are.

Click Me Load More a CSV file in Python

CSV stands for Comma-Separated Values the most common format for tabular data everywhere.
With pandas, turning a CSV into a powerful, queryable DataFrame takes just a few clear lines.

# Import the pandas library
import pandas as pd

# Specify the path to your CSV file
filename = "data.csv"

# Read the CSV file into a DataFrame
df = pd.read_csv(filename)

#Checking the first five rows
df.head()

Next up ➡️ Click Me Load More an Excel file in Python

👉Join @datascience_bds for more
Part of the @bigdataspecialist family

Click Me Load More an Excel file in Python

Excel files are packed with headers, logos, merged cells, and multiple sheets but pandas handles it all.
With just a few extra parameters, you can skip junk rows, pick exact columns,e.t.c

# Import the pandas library 
import pandas as pd

# Specify the path to your Excel file (.xlsx or .xls)
filename = "data.xlsx"

# Read the Excel file into a DataFrame
# Common options you'll use all the time:
df = pd.read_excel(
    filename,
    sheet_name=0,              # 0 = first sheet
    header=0,                  # Row (0-indexed) to use as column names
    skiprows=4,                # Skip first 4 rows
    nrows=1000,                # Load only first 1000 rows
)
# Check the first five rows
df.head()

Next up ➡️ Click Me Load More a text file in Python

👉Join @datascience_bds for more
Part of the @bigdataspecialist family

Click Me Load More a text file in Python

Text files (.txt) are perfect for logs, books, raw notes, or any unstructured data.
With one clean line using pathlib, you can load an entire novel, log file, or dataset into a string

# Click Me Load More a text file in Python

filename = 'huck_finn.txt'                  # Name of the file to open

file = open(filename, mode='r')             # Open file in read mode ('r')
                                            # Use encoding='utf-8' if needed

text = file.read()                          # Read entire content into a string

print(file.closed)                          # False → file is still open

file.close()                                # Always close the file when done!
                                            # Prevents memory leaks & file locks

print(file.closed)                          # Now True → file is safely closed

print(text)                                 # Display the full text content

Next up ➡️ Click Me Load More a JSON file in Python

👉Join @datascience_bds for more
Part of the @bigdataspecialist family