Data Science Tools List Every Professional Should Know

import pandas as pd

# Load a CSV file into a DataFrame
df = pd.read_csv("sales.csv", parse_dates=["order_date"])

# Quick health check
print(df.shape)
print(df.isna().sum())

# Clean: drop duplicates, fill missing city
df = df.drop_duplicates()
df["city"] = df["city"].fillna("Unknown")

# Monthly revenue by city
df["month"] = df["order_date"].dt.to_period("M")
summary = df.groupby(["month", "city"])["amount"].sum().reset_index()
print(summary.head())

-- Monthly revenue by city
SELECT
  DATE_TRUNC('month', order_date) AS month,
  city,
  SUM(amount) AS revenue,
  COUNT(DISTINCT customer_id) AS customers
FROM orders
WHERE order_date >= DATE '2026-01-01'
GROUP BY 1, 2
ORDER BY month, revenue DESC;

-- Note: DATE_TRUNC works in PostgreSQL.
-- In MySQL use DATE_FORMAT(order_date, '%Y-%m-01').

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y)

model = make_pipeline(StandardScaler(), LogisticRegression(max_iter=1000))
model.fit(X_train, y_train)
print(classification_report(y_test, model.predict(X_test)))

import seaborn as sns
import matplotlib.pyplot as plt

# Built-in sample dataset (downloads on first use)
tips = sns.load_dataset("tips")

fig, axes = plt.subplots(1, 2, figsize=(10, 4))
sns.histplot(tips["total_bill"], bins=20, ax=axes[0])
axes[0].set_title("Bill distribution")

sns.boxplot(data=tips, x="day", y="tip", ax=axes[1])
axes[1].set_title("Tip by day")

plt.tight_layout()
plt.savefig("eda.png", dpi=150)

# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate      # Windows: .venv\Scripts\activate

# Install tools and freeze versions
pip install pandas numpy scikit-learn matplotlib seaborn jupyter
pip freeze > requirements.txt

# Anyone can now recreate your setup
pip install -r requirements.txt

# Version control
git init
git add .
git commit -m "Initial analysis project"

# app.py  - run with: streamlit run app.py
import streamlit as st
import pandas as pd

st.title("Sales explorer")
file = st.file_uploader("Upload a CSV", type="csv")

if file is not None:
    df = pd.read_csv(file)
    st.write("Rows:", len(df))
    column = st.selectbox("Numeric column", df.select_dtypes("number").columns)
    st.line_chart(df[column])

STAGE 1 - CORE (learn first)
[ ] SQL: SELECT, WHERE, JOIN, GROUP BY, window functions
[ ] Python basics: lists, dicts, functions, loops
[ ] pandas: read, filter, groupby, merge, pivot
[ ] NumPy: arrays, vector maths
[ ] Jupyter: markdown notes + code

Proof: one cleaned dataset + EDA notebook on GitHub

STAGE 2 - BUILD
[ ] Matplotlib + seaborn: 6 chart types you can explain
[ ] Statistics: mean, median, variance, correlation, tests
[ ] scikit-learn: split, pipeline, cross-validation
[ ] Metrics: accuracy, precision, recall, RMSE
[ ] Git: commit, branch, push, pull request

Proof: one end-to-end ML project with README

STAGE 3 - SHIP
[ ] Streamlit or Flask/FastAPI demo
[ ] Virtual env + requirements.txt
[ ] Docker basics: build and run an image
[ ] One cloud notebook or storage service
[ ] Optional: PyTorch or TensorFlow basics

Proof: a live demo link in your resume