Intro to Data Science: QuickStart Guide + AI & ChatGPT Prize

Discover the six-week data scientist roadmap, covering statistics, data science process, visualization, databases, machine learning, Python, and deep learning, with hands-on projects and checkpoints to enter real-world data work.

Explore the data science process as an end-to-end lifecycle: identify the question, prepare the data, analyze, visualize, and present findings.

Explore a data science challenge by analyzing customer variables and training data to predict term deposit subscription, then learn visualization basics to turn insights into smarter decisions.

Import pandas as pd in python (spyder), load the dataset with read_excel, and split independent variables X and the energy as the dependent variable using iloc.

This Python tutorial demonstrates splitting a dataset into training and test sets using scikit-learn's train_test_split, preparing X_train, X_test, y_train, and y_test for a regression model.

Test a trained regression model on the test set using the predict method in scikit-learn. Compare predictions to real results and recognize gradient boosting and xgboost as powerful approaches.

Explore a classification task predicting breast tumor type (benign or malignant) using the breast cancer Wisconsin dataset, with nine independent features and handling missing values before applying gradient boosting.

Learn to build and interpret a confusion matrix in Python with scikit-learn, compute accuracy from truth and predictions, and examine true positives, true negatives, false positives, false negatives—gradient boosting.

Explore why Amazon Web Services dominates the cloud for building and running machine learning models, with guidance to launch and connect a Linux EC2 instance using the AWS free tier.

Explain why AWS powers machine learning on the cloud with EC2 per second billing, flexible vCPUs and memory, plus affordable EBS and S3 storage for data.