How to do data analytics? #1

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

Define the Problem or Goal
Ask:
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What decision do I want to make?

What do I want to know or optimize?

Example: Why are product sales decreasing in Q2?

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.

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

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Analyze the Data
Use:

Descriptive analytics to know what occurred

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.

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.

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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: Please visit our website:- <a href="https://www.sevenmentor.com/data-analytics-courses-in-pune.php">Data Analytics Classes in Pune </a> What decision do I want to make? What do I want to know or optimize? Example: Why are product sales decreasing in Q2? 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 Please visit our website:-<a href="https://www.sevenmentor.com/data-analytics-courses-in-pune.php"> Data Analytics Course in Pune</a> 5. Analyze the Data Use: Descriptive analytics to know what occurred 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. Please visit our website:- <a href="https://www.iteducationcentre.com/data-analytics-courses-in-pune.php">Data Analytics Training in Pune</a>