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9. Advanced Data Science Topics

After mastering the fundamentals (EDA, preprocessing, classical ML), this section introduces more advanced and highly in-demand areas in 2026: time series, NLP, deep learning, and deployment/MLOps. These topics are essential for real-world projects, research papers, and industry roles.

9.1 Time Series Analysis & Forecasting

Time series data has a temporal order (stock prices, sales, weather, sensor readings). The goal is to understand patterns (trend, seasonality, cycles) and predict future values.

Key concepts

• Trend: long-term increase/decrease

• Seasonality: repeating patterns (weekly, monthly, yearly)

• Stationarity: statistical properties constant over time (most models require it)

• Autocorrelation: correlation with lagged values

Popular libraries

• statsmodels (ARIMA, SARIMA)

• Prophet (Facebook)

• sktime (modern, scikit-learn compatible)

Basic ARIMA example

Python

import pandas as pd import matplotlib.pyplot as plt from statsmodels.tsa.arima.model import ARIMA from statsmodels.tsa.stattools import adfuller # Load sample time series (AirPassengers or your own data) url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv" df = pd.read_csv(url, parse_dates=['Month'], index_col='Month') series = df['Passengers'] # Check stationarity result = adfuller(series) print("ADF Statistic:", result[0]) print("p-value:", result[1]) # if >0.05 → not stationary → difference # Fit ARIMA (p,d,q) – here (5,1,0) is a good start model = ARIMA(series, order=(5,1,0)) model_fit = model.fit() # Forecast next 12 months forecast = model_fit.forecast(steps=12) print("Forecast:", forecast) # Plot plt.plot(series, label='Actual') plt.plot(forecast, label='Forecast', color='red') plt.title("Air Passengers Forecast") plt.legend() plt.show()

Prophet (very easy & powerful)

Python

from prophet import Prophet df_prophet = df.reset_index().rename(columns={'Month': 'ds', 'Passengers': 'y'}) m = Prophet(yearly_seasonality=True) m.fit(df_prophet) future = m.make_future_dataframe(periods=12, freq='MS') forecast = m.predict(future) m.plot(forecast) plt.title("Prophet Forecast – Air Passengers") plt.show()

When to use what (2026):

• Short-term, classic data → ARIMA/SARIMA

• Business time series with holidays → Prophet

• Multivariate → VAR, LSTM (deep learning)

9.2 Natural Language Processing (NLP) Basics

NLP deals with text data: sentiment analysis, chatbots, translation, summarization, etc.

Essential libraries in 2026

• NLTK / spaCy (traditional)

• Transformers (Hugging Face) → state-of-the-art (BERT, GPT-style)

Basic NLP pipeline with spaCy

Python

import spacy nlp = spacy.load("en_core_web_sm") text = "Apple is looking at buying U.K. startup for $1 billion in 2026" doc = nlp(text) for token in doc: print(token.text, token.pos_, token.dep_) # Named Entity Recognition (NER) for ent in doc.ents: print(ent.text, ent.label_) # Output: # Apple ORG # U.K. GPE # $1 billion MONEY # 2026 DATE

Sentiment Analysis with Hugging Face (easiest & best in 2026)

Python

from transformers import pipeline sentiment_pipeline = pipeline("sentiment-analysis") reviews = [ "This product is amazing! Love it.", "Worst experience ever. Do not buy.", "It's okay, nothing special." ] results = sentiment_pipeline(reviews) for review, res in zip(reviews, results): print(f"Review: {review}") print(f"Sentiment: {res['label']} (score: {res['score']:.4f})\n")

Text Classification (custom model) Use Hugging Face Trainer API or scikit-learn + TF-IDF.

9.3 Introduction to Deep Learning with TensorFlow/Keras

Deep learning = neural networks with many layers. In 2026, Keras (inside TensorFlow) is still the easiest high-level API for beginners.

Basic Neural Network (Classification)

Python

import tensorflow as tf from tensorflow.keras import layers, models from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler iris = load_iris() X, y = iris.data, iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) model = models.Sequential([ layers.Dense(16, activation='relu', input_shape=(4,)), layers.Dense(8, activation='relu'), layers.Dense(3, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(X_train, y_train, epochs=50, validation_split=0.2, verbose=1) test_loss, test_acc = model.evaluate(X_test, y_test) print(f"Test accuracy: {test_acc:.4f}")

Why Keras in 2026?

• Simple & readable (Sequential / Functional API)

• Built-in callbacks (EarlyStopping, ModelCheckpoint)

• Integrates with TensorFlow ecosystem (TensorBoard, TPU support)

9.4 Model Deployment & MLOps Basics

Deployment = putting model into production so others can use it.

Popular options in 2026 (easy to advanced):

• Streamlit / Gradio → interactive web apps (fastest)

• FastAPI + Uvicorn → production API

• Flask / Django → traditional web

• Docker + Kubernetes → scalable deployment

• MLflow / BentoML → full MLOps

Simple Streamlit app example

Python

# app.py import streamlit as st from sklearn.linear_model import LogisticRegression import numpy as np st.title("Simple Iris Flower Prediction") sepal_length = st.slider("Sepal Length", 4.0, 8.0, 5.0) sepal_width = st.slider("Sepal Width", 2.0, 4.5, 3.5) petal_length = st.slider("Petal Length", 1.0, 7.0, 4.0) petal_width = st.slider("Petal Width", 0.1, 2.5, 1.3) model = LogisticRegression() # load your trained model here prediction = model.predict([[sepal_length, sepal_width, petal_length, petal_width]]) st.write(f"Predicted class: {prediction[0]}")

Run:

Bash

pip install streamlit streamlit run app.py

MLOps Basics (2026 essentials)

• Version control data & models → DVC

• Track experiments → MLflow

• Package models → BentoML / ONNX

• Deploy → Docker + Render / Railway / AWS / GCP

Mini Summary Project – End-to-End Churn Prediction

1. Load data → EDA (section 5)

2. Preprocess → scale, encode (section 6)

3. Train Random Forest / XGBoost

4. Evaluate → cross-validation, ROC-AUC

5. Deploy simple Streamlit app for prediction

This completes the full Advanced Data Science Topics section — now you have exposure to time series, NLP, deep learning, and deployment!asses and Objects – Basic Building Blocks section — the heart of OOP in Python!

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