Here's a step-by-step tutorial on how to do data analytics, even if you're a starter:
✅ Step-by-Step: How to Do Data Analytics
 1. Define the Problem or Goal
Ask:
What decision do I want to make?
What do I want to know or optimize?
 Example: Why are product sales decreasing in Q2?

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2. Get the Data
Get data from sources such as:
Spreadsheets (Excel, Google Sheets)
Databases (SQL, MongoDB)
APIs, Web scraping
Business tools (CRM, Google Analytics)
Example: Download customer feedback and monthly sales data.
 3. Clean and Prepare the Data
Correct issues such as:
Missing values
Unstandardized formats
Duplicate records
Tools: Excel, Python (Pandas), R, Power Query
 Example: Remove blank cells and standardize date formats.
Get to know the data by examining:
Summary statistics (mode, median, mean)
Trends and distributions
Correlations and outliers
Tools: Excel, Python (Matplotlib, Seaborn), Power BI, Tableau
 5. Analyze the Data
Use:
Descriptive analytics to know what occurred

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Diagnostic analytics to discover why
Predictive analytics to predict
Prescriptive analytics to suggest actions
Techniques: A/B testing, regression, clustering, trend analysis
Tools: Excel, Python, R, SQL, SPSS, SAS
Example: Perform a regression model to determine what drives sales.
 6. Visualize the Results
Develop charts, dashboards, or reports to convey findings elegantly.
 Example: A bar chart comparing quarterly sales by region.
 7. Interpret & Act
What story does the data tell?
What action should be taken?
How will results be measured?
 Example: Recommend increasing ad spend in regions with high potential.
 Tools You Can Use
Excel/Google Sheets – good for beginners
SQL – for querying databases
Python or R – for advanced analysis
Power BI / Tableau – for dashboards and visuals
 Tip:
Start small! Practice with simple data (e.g., personal budget, survey responses) before tackling business or large data projects.
Would you like a beginner's roadmap, cheat sheet, or tutorial to get hands-on with your first project?

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