Advanced Data Science Tutorial

Welcome to the advanced data science tutorial by DarchumsTech. This guide covers essential topics like machine learning, deep learning, natural language processing (NLP), data visualization, and model deployment using Python.

1. Getting Started with Python for Data Science

• Install Python & Jupyter: pip install jupyterlab numpy pandas
• Libraries you'll use: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

2. Exploratory Data Analysis (EDA)

Understand data distribution, correlations, outliers, and missing values.

df.describe()
df.info()
sns.heatmap(df.corr(), annot=True, cmap='coolwarm')

3. Feature Engineering

• Handle missing values, encoding, normalization
• Create meaningful derived features

df['Log_Sales'] = np.log(df['Sales'] + 1)
df.fillna(df.mean(), inplace=True)

4. Machine Learning with Scikit-Learn

• Train-test split, modeling, evaluation
• Models: Logistic Regression, Random Forest, SVM, XGBoost

from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
model = RandomForestClassifier()
model.fit(X_train, y_train)
print(classification_report(y_test, model.predict(X_test)))

5. Deep Learning with TensorFlow/Keras

• Build and train neural networks
• Use CNNs for image data, RNNs for sequences

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
  Dense(128, activation='relu', input_shape=(X_train.shape[1],)),
  Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, epochs=10)

6. Natural Language Processing (NLP)

• Text classification using TF-IDF, Word2Vec, BERT
• Use Hugging Face Transformers

from transformers import pipeline
classifier = pipeline("text-classification")
print(classifier("This tutorial is excellent!"))

7. Model Evaluation & Tuning

• Confusion matrix, AUC-ROC, F1-score
• Use GridSearchCV or RandomizedSearchCV

8. Model Deployment

• Save models with Pickle or Joblib
• Deploy with Flask, FastAPI or Streamlit

import joblib
joblib.dump(model, 'model.pkl')

9. Projects for Practice

• Fake news detection with BERT
• Image classification with CNN
• Time series forecasting (LSTM/ARIMA)
• Sentiment analysis with Transformers

10. Learn More & Stay Updated

• Scikit-learn Docs
• TensorFlow Tutorials
• Hugging Face Docs