Data Science – Complete Quick Guide

1️⃣ What is Data Science?

Data Science is the field of extracting knowledge, insights, and predictions from structured and unstructured data using:

• Statistics
• Machine Learning
• Programming
• Domain expertise

2️⃣ Data Science Process (CRISP-DM)

1. Business Understanding – Define objectives
2. Data Collection – Gather raw data
3. Data Cleaning & Preprocessing – Handle missing values, outliers
4. Exploratory Data Analysis (EDA) – Understand data patterns
5. Modeling – Apply ML/AI algorithms
6. Evaluation – Validate model performance
7. Deployment & Monitoring – Make model production-ready

3️⃣ Key Skills in Data Science

CategorySkillsProgrammingPython, R, SQLLibrariesPandas, NumPy, Matplotlib, Seaborn, Scikit-learnStatisticsMean, Median, Std, Probability, Hypothesis testingMachine LearningRegression, Classification, Clustering, Decision Trees, Random ForestData VisualizationMatplotlib, Seaborn, Tableau, PowerBIBig DataSpark, Hadoop (optional for advanced roles)Cloud / DevOpsAWS, GCP, Docker (optional for deployment)

4️⃣ Data Types

• Structured Data – Tables, Excel, SQL
• Unstructured Data – Text, Images, Videos
• Semi-Structured Data – JSON, XML, Logs

5️⃣ Common Data Science Tools

• Python / R – Programming
• Jupyter Notebook / RStudio – Interactive coding
• Pandas / NumPy – Data manipulation
• Matplotlib / Seaborn – Visualization
• Scikit-learn – Machine learning
• SQL / NoSQL – Databases
• Tableau / PowerBI – Dashboarding

6️⃣ Basic Python Example (EDA)

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

# Load dataset

data = pd.read_csv("data.csv")

# Overview

print(data.head())

print(data.describe())

print(data.isnull().sum())

# Visualization

sns.heatmap(data.corr(), annot=True)

plt.show()

7️⃣ Machine Learning Workflow

1. Split Data – Train / Test
2. Choose Algorithm – Regression, Classification, Clustering
3. Train Model – Fit model to training data
4. Evaluate – Accuracy, Precision, Recall, F1 Score
5. Tune Hyperparameters – GridSearch, RandomSearch
6. Deploy Model – REST API, Cloud, Dashboard

8️⃣ Popular Algorithms

TypeExamplesRegressionLinear Regression, Lasso, RidgeClassificationLogistic Regression, Decision Tree, Random Forest, SVMClusteringK-Means, DBSCAN, HierarchicalNeural NetworksDeep Learning, CNN, RNN

9️⃣ Big Data & Advanced Topics (Optional)

• Spark / PySpark for distributed processing
• Hadoop HDFS for storage
• NLP (Text analysis)
• Computer Vision (Image/video analysis)
• Time Series Analysis (Stock, IoT, Sensor data)

🔟 Interview Quick Questions

Q: What is Data Science?

A: Extracting insights and predictions from data.

Q: Difference between Data Science, Data Analysis, and Machine Learning?

• Data Analysis – Insights from existing data
• Machine Learning – Predict future outcomes
• Data Science – Full pipeline from data collection to deployment

Q: What is overfitting?

Model performs well on training data but poorly on unseen data.

Q: What is cross-validation?

Technique to evaluate model performance on multiple folds of data.

Q: Which Python libraries are used for ML?