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📊 Data Analysis Beginner Course Breakdown
📘 1. Introduction to Data Analysis
What is data analysis?
The data analysis process:
Ask a question
Collect data
Clean the data
Analyze the data
Interpret results
Share findings
Roles: Data Analyst vs Data Scientist vs Business Analyst
📂 2. Types of Data
Qualitative vs. Quantitative
Structured vs. Unstructured
Categorical vs. Continuous
Examples: Sales data, survey data, social media, etc.
🧰 3. Tools of the Trade
Tool Purpose
Excel / Google Sheets Basic analysis, charts, pivot tables
SQL Data extraction and querying
Python (with pandas, matplotlib) Scripting and automation
R Statistics and visualization
Power BI / Tableau Data visualization and dashboards
📝 Start with Excel and SQL. Then learn Python or Power BI.
🧼 4. Data Cleaning & Preparation
Handling missing values
Removing duplicates
Dealing with outliers
Normalizing and standardizing data
Data types and conversions
Tools: Excel (filters, functions), Python (pandas)
📉 5. Exploratory Data Analysis (EDA)
Descriptive statistics (mean, median, mode, std dev)
Data distributions
Correlations
Visualization types:
Histograms
Box plots
Scatter plots
Bar charts
Tools: Python (matplotlib, seaborn), Excel, Power BI
🧠 6. Basic Statistics for Data Analysis
Mean, median, mode
Variance and standard deviation
Probability basics
Correlation vs causation
Sampling techniques
Confidence intervals
🧮 7. Introduction to SQL
SELECT, WHERE, ORDER BY, GROUP BY, JOIN
Aggregation: COUNT, SUM, AVG
Filtering and sorting
Subqueries and nested queries
Use: MySQL, PostgreSQL, or SQLite with sample databases (like Chinook or Northwind)
📊 8. Data Visualization
Best practices: Avoid clutter, use labels, choose correct chart type
Tools:
Excel (pivot charts, sparklines)
Power BI or Tableau (interactive dashboards)
Python: matplotlib, seaborn, plotly
📈 9. Real Projects & Case Studies
Analyze sales trends from an Excel file
Clean and explore survey results
Create a customer churn dashboard in Power BI
SQL challenge: Write queries for HR database
💼 10. Portfolio Building & Career Prep
Present projects on GitHub or Tableau Public
Write a short summary for each: goal, data, method, outcome
Resume tips for analysts
Common interview topics:
SQL queries
Data cleaning steps
Interpreting charts
Explaining findings to non-technical teams
🧭 Suggested Learning Path (6–8 Weeks)
Week Focus
1 Intro to analysis + Excel
2 Data types + cleaning
3 Basic statistics + EDA
4 SQL fundamentals
5 Data visualization
6 Capstone project
7–8 Portfolio + job prep
📚 Top Resources
Free
Google Data Analytics Certificate (Coursera)
SQLZoo
Kaggle (Datasets + Notebooks)
W3Schools SQL
Microsoft Learn – Power BI
Paid
Udemy: Data Analysis Bootcamps
DataCamp: Python, R, SQL tracks
LinkedIn Learning: Excel + BI tools