Data Science — Get Started

Python setup

# Create virtual environment
python -m venv datascience-env
source datascience-env/bin/activate

# Install packages
pip install numpy pandas matplotlib seaborn scikit-learn jupyterlab

Jupyter Lab

# Start Jupyter Lab
jupyter lab

First analysis

import pandas as pd
import matplotlib.pyplot as plt

# Load data
df = pd.read_csv('data.csv')

# Basic info
print(df.head())
print(df.describe())

# Simple plot
df['column'].hist()
plt.show()

R setup

# Install R
# Install RStudio

# Install packages
install.packages(c("tidyverse", "ggplot2", "dplyr"))

Google Colab

# No installation needed
# Access at colab.research.google.com
# Free GPU available

Best practices

1. Use virtual environments
2. Version control code
3. Document analysis
4. Reproducible workflows

Mini Practice

1. Set up Python environment
2. Install Jupyter Lab
3. Load a dataset
4. Create a simple visualization

Up Next

Continue with Statistics - Statistical foundations.