Data Science & Machine Learning: Key Concepts and Tools

Logistic Regression

Predicts binary outcomes like yes/no or 0/1. (Like deciding if an email is spam.)

Decision Tree

Splits data into branches based on rules. (Like asking "Is it raining?" then deciding to bring an umbrella.)

Random Forest

Collection of decision trees voting together. (Like asking multiple friends for advice.)

Overfitting

Model memorizes data instead of learning patterns. (Like memorizing practice questions, not concepts.)

Cross-Validation

Tests a model with different train/test splits. (Like practicing a speech with multiple audiences.)

Statistics

Study of collecting, analyzing, and interpreting data. (Like summarizing a classroom's test scores.)

Probability

Likelihood of an event occurring between 0 and 1. (Like flipping a coin and predicting heads.)

Hypothesis Testing

Determines if a result is due to chance or a real effect. (Like testing if a new study method works.)

Correlation

Measures how strongly two variables move together. (Like linking hours of sleep to energy levels.)

Linear Regression

Predicts a numeric value using a straight-line relationship. (Like estimating house price by size.)

Feature Engineering

Creating new variables to improve models. (Like combining height and weight into BMI.)

Regularization

Penalizes large weights to prevent overfitting. (Like discouraging overcomplicated answers.)

PCA

Reduces data dimensions while keeping patterns. (Like turning a long movie into a short trailer.)

Python

Versatile language for data science and ML. (Like a Swiss Army knife for coding.)

R

Language for statistics and visualization. (Like Excel but programmable.)

SQL

Queries and manages structured databases. (Like asking a librarian for specific books.)

Bash/Shell

Automates commands and tasks. (Like giving your computer a to-do list.)

Java

Common backend and big-data language. (Like a sturdy foundation for big apps.)

NumPy

Fast numerical computation in Python. (Like a high-speed calculator.)

pandas

Data organization and analysis in Python. (Like coding with spreadsheets.)

matplotlib

Makes static charts. (Like drawing graphs with code.)

seaborn

Stylish, statistical visualizations. (Like decorating charts.)

scikit-learn

Library for ML algorithms. (Like a toolbox for prediction.)

TensorFlow

Deep learning framework by Google. (Like building neural nets with LEGO.)

PyTorch

Flexible deep learning library by Meta. (Like an experimental lab for AI.)

Keras

Simplified deep learning interface. (Like a friendly front-end for TensorFlow.)

Statsmodels

Statistical modeling in Python. (Like R's regression functions.)

SciPy

Scientific computing and optimization. (Like a scientific calculator.)

XGBoost

Gradient boosting library for fast ML. (Like turbocharging predictions.)

LightGBM

Lightweight, fast boosting model. (Like XGBoost's speedy cousin.)

CatBoost

Boosting library that handles categorical data. (Like XGBoost that understands text labels.)

Data Cleaning

Fixing or removing incorrect or missing data. (Like tidying a messy spreadsheet.)

Missing Value Imputation

Filling blanks with averages or predictions. (Like guessing skipped test answers.)

Outlier Detection

Finding extreme or unusual values. (Like spotting a runner at 100 mph.)

One-Hot Encoding

Turning categories into 0/1 columns. (Like checkboxes for ice cream flavors.)

Label Encoding

Assigning numbers to categories. (Like small=1, medium=2, large=3.)

Normalization

Scaling data to 0-1. (Like matching different music volumes.)

Standardization

Centering data to mean 0, std 1. (Like grading on a curve.)

ETL

Extract, Transform, Load process. (Like shopping, washing, and storing groceries.)

API Data Retrieval

Pulling data via web APIs. (Like ordering takeout and getting a meal back.)

Web Scraping

Collecting data from websites. (Like copying all prices from a store automatically.)

Data Integration

Combining sources into one view. (Like merging grades and attendance into one record.)

Data Transformation

Changing data format or shape. (Like rearranging ingredients before cooking.)

SQL Databases

Store structured tables of data. (Like labeled filing cabinets.)

NoSQL Databases

Handle flexible, unstructured data. (Like a box of sticky notes with loose rules.)

MySQL/PostgreSQL

Popular SQL database systems. (Like reliable file cabinets.)

MongoDB

NoSQL document database. (Like digital folders with flexible fields.)

Hadoop

Distributed framework for large data storage. (Like splitting a huge book among friends.)

Spark

Engine for fast distributed processing. (Like Hadoop's faster cousin.)

Databricks

Collaborative platform around Spark. (Like Google Docs for big-data code.)

Airflow

Automates and schedules data workflows. (Like a calendar that runs scripts for you.)

Kafka

Real-time data streaming system. (Like a conveyor belt for continuous updates.)

Snowflake

Cloud data warehouse. (Like an online filing cabinet with unlimited space.)

BigQuery

Google's serverless warehouse. (Like querying billions of rows instantly.)

AWS S3

Amazon's cloud storage. (Like an infinite online drive.)

ETL Pipelines

Automated data movement processes. (Like an assembly line for cleaning and storing data.)

Tableau

Interactive dashboards for storytelling. (Like turning spreadsheets into visual stories.)

Power BI

Microsoft tool for business reports. (Like Tableau inside the Office suite.)

matplotlib

Static Python charting library. (Like drawing with precise tools.)

seaborn

Statistical visualization library. (Like adding style to graphs.)

Plotly

Interactive plotting library. (Like hoverable, zoomable charts.)

ggplot2

R library for layered plots. (Like building art with data.)

Looker

Cloud BI and data exploration tool. (Like real-time dashboards for teams.)

D3.js

JS library for web visuals. (Like coding interactive art in a browser.)

Excel/Sheets Charts

Basic built-in graphing. (Like sketching drafts before full dashboards.)

AWS

Cloud platform for computing, ML, and storage. (Like renting virtual computers.)

AWS S3

Cloud storage for any data type. (Like an online hard drive.)

AWS EC2

Virtual servers for running apps. (Like borrowing a supercomputer.)

AWS Lambda

Serverless code execution. (Like lights turning on automatically.)

AWS SageMaker

ML development platform. (Like a full lab for model training.)

GCP

Google's cloud service. (Like running apps on Google's servers.)

BigQuery

Serverless SQL data warehouse. (Like analyzing data at lightning speed.)

Vertex AI

Managed ML platform by Google. (Like an AI workshop that sets itself up.)

Azure

Microsoft's cloud environment. (Like Windows in the cloud.)

Azure ML Studio

Drag-and-drop ML builder. (Like Lego blocks for models.)

Azure Synapse

Data integration and analytics. (Like joining multiple lakes into one.)

Docker

Packages apps and dependencies. (Like a lunchbox that works anywhere.)

Kubernetes

Manages many Docker containers. (Like an orchestra conductor.)

Flask

Lightweight Python web framework. (Like a simple café website in code.)

FastAPI

High-speed API framework. (Like Flask but faster.)

Streamlit

Turns Python scripts into web apps. (Like instant dashboards.)

MLflow

Tracks ML experiments and models. (Like a lab notebook for ML runs.)

MLOps

Managing ML model lifecycle. (Like DevOps for data models.)

CI/CD

Automates testing and deployment. (Like a conveyor belt for code.)

MLflow

Tracks experiments and versions. (Like logging every experiment.)

Kubeflow

Runs ML pipelines on Kubernetes. (Like an automated assembly line.)

DVC

Version control for data/models. (Like saving checkpoints in a game.)

Git/GitHub

Version control and collaboration. (Like Google Docs for code.)

Jenkins

Automates build/test pipelines. (Like a robot that checks every change.)

Airflow

Schedules and monitors data tasks. (Like autopilot for workflows.)

Docker

Packages ML environments. (Like shipping your model in a safe box.)

Kubernetes

Scales and manages containers. (Like traffic control for servers.)

Excel

Spreadsheet for data analysis. (Like a digital notebook of formulas.)

Google Sheets

Collaborative spreadsheets. (Like shared Excel in the cloud.)

Tableau

Visual dashboards for insights. (Like colorful stories from data.)

Power BI

Microsoft BI dashboards. (Like Tableau with Excel integration.)

Google Data Studio

Free Google dashboard builder. (Like linking Analytics and Sheets visually.)

SAS

Enterprise analytics platform. (Like R + Python for corporations.)

SPSS

Statistical tool for research. (Like point-and-click regression.)

Alteryx

No-code data prep and blending tool. (Like puzzle pieces that snap together.)