Become a Data Analyst

📊 Data Analyst Full Contents

• 1. Foundations
  • Mathematics (Linear Algebra, Probability, Statistics)
  • Business Knowledge & Analytical Thinking
• 2. Excel / Google Sheets
  • Data Cleaning, Sorting & Filtering
  • Pivot Tables & Lookups (VLOOKUP, HLOOKUP, XLOOKUP)
  • Charts & Dashboards
• 3. SQL
  • Basic Queries (SELECT, WHERE, ORDER BY)
  • Joins, Subqueries, Aggregations
  • Window Functions & CTEs
  • Database Design & Normalization
• 4. Programming
  • Python: Pandas, NumPy, Matplotlib, Seaborn
  • R: dplyr, ggplot2, tidyr
  • Data Cleaning, Wrangling & Automation
• 5. Data Visualization
  • Tableau (Dashboards, Storytelling, Interactivity)
  • Power BI (Data Modeling, DAX Functions, Reports)
  • Excel Dashboards
• 6. Exploratory Data Analysis (EDA)
  • Identifying Trends & Patterns
  • Correlation & Causation
  • Hypothesis Testing & A/B Testing
• 7. Data Cleaning & Preprocessing
  • Handling Missing Values & Outliers
  • Data Transformation & Feature Engineering
  • Working with Large Datasets
• 8. Business & Reporting
  • Understanding KPIs & Metrics
  • Report Building & Storytelling with Data
  • Automating Reports with Python / BI Tools
• 9. Cloud & Big Data (Optional)
  • Google BigQuery, AWS Redshift, Snowflake
  • Hadoop, Spark Basics
• 10. Advanced (Optional)
  • Machine Learning Basics (Regression, Classification, Clustering)
  • Time Series Analysis
  • Natural Language Processing (NLP)