Data Cleaning 🧹

Removing duplicates, fixing missing or inconsistent data.

👉 Tools: Excel, Python (Pandas), SQL

2️⃣ Descriptive Statistics 📈

Mean, median, mode, standard deviation—basic measures to summarize data.

👉 Used for understanding data distribution

3️⃣ Data Visualization 📊

Creating charts and dashboards to spot patterns.

👉 Tools: Power BI, Tableau, Matplotlib, Seaborn

4️⃣ Exploratory Data Analysis (EDA) 🔍

Identifying trends, outliers, and correlations through deep data exploration.

👉 Step before modeling

5️⃣ SQL for Data Extraction 🗃️

Querying databases to retrieve specific information.

👉 Focus on SELECT, JOIN, GROUP BY, WHERE

6️⃣ Hypothesis Testing ⚖️

Making decisions using sample data (A/B testing, p-value, confidence intervals).

👉 Useful in product or marketing experiments

7️⃣ Correlation vs Causation 🔗

Just because two things are related doesn’t mean one causes the other!

8️⃣ Data Modeling 🧠

Creating models to predict or explain outcomes.

👉 Linear regression, decision trees, clustering

9️⃣ KPIs & Metrics 🎯

Understanding business performance indicators like ROI, retention rate, churn.

🔟 Storytelling with Data 🗣️

Translating raw numbers into insights stakeholders can act on.

👉 Use clear visuals, simple language, and real-world impact

❤️ React for more