Now, let's move to the next topic of data analytics roadmap:

Types of Data ✍️

You work with three data types.

1. Structured Data
• Fixed rows and columns
• Easy to store and query
• Lives in databases and spreadsheets
• Examples: Sales table with date, product, revenue; Employee table with ID, department, salary
• Where you see it: Excel, SQL databases, CRM and ERP systems

2. Semi-structured Data
• No fixed table format
• Has tags or keys
• Needs parsing before analysis
• Examples: JSON from APIs, XML files, Log files
• Where you see it: Web applications, Mobile apps, Cloud systems

3. Unstructured Data
• No defined format
• Harder to analyze
• Needs advanced tools
• Examples: Text reviews, Emails, Images, audio, video
• Where you see it: Social media posts, Customer feedback, Call recordings

Why this matters to you
• Most analyst jobs start with structured data
• Semi-structured data appears in modern products
• Unstructured data leads to AI and NLP roles

Mini task for today
1. Open Excel. Create a structured table with 3 columns and 5 rows.
2. Download a sample JSON file from any API site. Identify keys and values.

Next topic: Tools used in data analytics.

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Now, let's move to the next topic of data analytics roadmap:

Tools Used in Data Analytics ✅

You don't need every tool, you need the right stack.

Core tools to learn first:

1. Excel
- Fast cleaning and quick analysis
- Used in almost every company
- Focus on: Filters, sorting, IF, COUNTIFS, SUMIFS, pivot tables, basic charts
- Real use: Clean raw CSV files, build quick reports

2. SQL
- Data lives in databases, Excel breaks on large data
- Focus on: SELECT, WHERE, GROUP BY, HAVING, JOINS, subqueries
- Real use: Pull monthly sales data, join customer and orders tables

3. Visualization tool (Power BI or Tableau)
- Decision makers read charts, not tables
- Focus on: Connecting data sources, basic charts, filters, simple dashboards
- Real use: Sales dashboard, KPI tracking

4. Python (optional at start)
- Automation and deeper analysis
- Focus on: Pandas basics, reading CSV and Excel, simple grouping and filtering

Mini task:
- Install Excel alternative (Google Sheets works)
- Install MySQL or PostgreSQL
- Install Power BI Desktop or Tableau Public

👉 Next up: Excel basics for data analytics

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Excel Basics for Data Analytics

Excel sits at the start of most analysis work.

What you use Excel for
• Cleaning raw data
• Exploring patterns
• Quick summaries for teams

Core concepts you must know
• Data setup
– Freeze header row. View → Freeze Top Row.
– Convert range to table. Ctrl + T.
– Use proper headers. No merged cells. One value per cell.

• Data cleaning
– Remove duplicates. Data → Remove Duplicates.
– Trim extra spaces. =TRIM(A2)
– Convert text to numbers. =VALUE(A2)
– Fix date format. Format Cells → Date.
– Handle blanks. Filter blanks, fill or delete.
– Find and replace. Ctrl + H.

• Essential formulas
– Math and counts
▪ SUM. =SUM(A2:A100)
▪ AVERAGE. =AVERAGE(A2:A100)
▪ MIN. =MIN(A2:A100)
▪ MAX. =MAX(A2:A100)
▪ COUNT. Counts numbers.
▪ COUNTA. Counts non blanks.
▪ COUNTBLANK. Counts blanks.
– Conditional formulas
▪ IF. =IF(A2>5000,"High","Low")
▪ IFS. Multiple conditions.
▪ AND. =AND(A2>5000,B2="West")
▪ OR. =OR(A2>5000,A2<1000)
– Lookup formulas
▪ XLOOKUP. =XLOOKUP(A2,Sheet2!A:A,Sheet2!B:B)
▪ VLOOKUP. Old but common.
▪ INDEX + MATCH. Powerful alternative.
– Text formulas
▪ LEFT. =LEFT(A2,4)
▪ RIGHT. =RIGHT(A2,2)
▪ MID. =MID(A2,2,3)
▪ LEN. =LEN(A2)
▪ CONCAT or TEXTJOIN.
▪ LOWER, UPPER, PROPER.
– Date formulas
▪ TODAY. Current date.
▪ NOW. Date and time.
▪ YEAR, MONTH, DAY.
▪ DATEDIF. Date difference.
▪ EOMONTH. Month end.

• Sorting and filtering
– Sort by multiple columns.
– Filter by value, color, condition.
– Top 10 filter for quick insights.

• Conditional formatting
– Highlight duplicates.
– Color scales for trends.
– Rules for thresholds. Example. Sales > 10000 in green.

• Pivot tables
– Insert → PivotTable.
– Rows. Category or Product.
– Values. Sum, Count, Average.
– Filters. Date, Region.
– Refresh after data update.

• Charts you must know
– Column. Comparison.
– Bar. Ranking.
– Line. Trends over time.
– Pie. Share or percentage.
– Combo. Actual vs target.

• Data validation
– Dropdown list. Data → Data Validation → List.
– Prevent wrong entries.

• Useful shortcuts
– Ctrl + Arrow. Jump data.
– Ctrl + Shift + Arrow. Select range.
– Ctrl + 1. Format cells.
– Ctrl + L. Apply filter.
– Alt + =. Auto sum.
– Ctrl + Z / Y. Undo redo.

• Common analyst mistakes to avoid
– Merged cells.
– Hard coded totals.
– Mixed data types in one column.
– No backup before cleaning.

• Daily practice task
– Download any sales CSV.
– Clean it.
– Build one pivot table.
– Create one chart.

Excel Resources: https://whatsapp.com/channel/0029VaifY548qIzv0u1AHz3i

Data Analytics Roadmap: https://whatsapp.com/channel/0029VaGgzAk72WTmQFERKh02/1354

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Now, let's move to the next topic of data analytics roadmap:

SQL Basics for Data Analytics

What SQL does
- Pull data from databases
- Filter large datasets
- Combine tables
- Summarize metrics

Core clauses
- SELECT: Choose columns
Example: SELECT name, sales FROM orders;
- FROM: Source table
Example: FROM orders;
- WHERE: Filter rows
Example: WHERE sales > 5000;
- ORDER BY: Sort results
Example: ORDER BY sales DESC;
- LIMIT: Restrict rows
Example: LIMIT 10;

Filtering operators
- =, <>, >, <, >=, <=
- BETWEEN for ranges
- IN for lists
- LIKE for patterns
Example: WHERE region IN ('East','West');

Logical conditions
- AND
- OR
- NOT

Aggregations
- GROUP BY: Group rows
Example: GROUP BY product;
- Aggregate functions: COUNT, SUM, AVG, MIN, MAX
- HAVING: Filter after aggregation
Example: HAVING SUM(sales) > 100000;

JOINS
- INNER JOIN: Matching rows only
- LEFT JOIN: All left rows, matching right
- RIGHT JOIN: All right rows, matching left
- FULL JOIN: All rows from both tables
Example:

SELECT o.order_id, c.customer_name 
FROM orders o 
INNER JOIN customers c 
ON o.customer_id = c.customer_id;

NULL handling
- IS NULL
- IS NOT NULL
- COALESCE(column, 0)

Subqueries
Query inside a query
Example:

SELECT * 
FROM orders 
WHERE sales > (SELECT AVG(sales) FROM orders);

Window functions
- ROW_NUMBER: Unique row number
- RANK: Ranking with gaps
- PARTITION BY: Reset calculation per group
Example:
ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC)

Common mistakes
- Forgetting GROUP BY columns
- Using WHERE instead of HAVING
- Wrong join condition
- Ignoring NULLs

Daily practice
- Write 5 SELECT queries
- Use 1 JOIN
- Use 1 GROUP BY
- Handle NULL values

SQL Resources: https://whatsapp.com/channel/0029VanC5rODzgT6TiTGoa1v

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Now, let's move to the next topic of data analytics roadmap:

Power BI Basics for Data Analytics ✅

What Power BI Does
- Connects to data sources
- Transforms data
- Builds dashboards
- Shares insights

Core Components
- Power BI Desktop: main tool for reports, modeling, and visuals
- Power BI Service: cloud sharing and collaboration

Data Sources
- Excel
- CSV
- SQL Server
- MySQL, PostgreSQL
- Web APIs

Data Loading
- Home → Get Data
- Choose source
- Load or Transform

Power Query Basics
- Clean data before analysis
- Remove duplicates
- Change data types
- Split columns
- Rename columns
- Filter rows

Data Model
- Tables connect using relationships
- One to many is standard
- Avoid many to many early
- Use proper keys

DAX Basics
- Measures run at report level
- Calculated columns run row by row
- Common DAX measures:
- Total Sales = SUM(Sales[Amount])
- Total Orders = COUNT(Sales[OrderID])
- Average Sales = AVERAGE(Sales[Amount])

Time Intelligence Basics
- YTD sales
- MTD sales
- Previous month comparison

Visuals You Must Know
- Table
- Matrix
- Bar chart
- Line chart
- KPI card
- Pie chart

Filters and Slicers
- Page level filters
- Visual level filters
- Slicers for user interaction

Dashboard Design Rules
- One page focus
- Use consistent colors
- Show KPIs on top
- Avoid clutter

Daily Practice Task
- Load a sales Excel file
- Clean data in Power Query
- Create 3 measures
- Build one dashboard page

Power BI Resources: https://whatsapp.com/channel/0029Vai1xKf1dAvuk6s1v22c

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Now, let's move to the next topic of data analytics roadmap:

Statistics Basics for Data Analysts ✅

Why Statistics Matters
- Explain trends
- Compare performance
- Avoid wrong conclusions

Denoscriptive Statistics
- Mean: Average value. Example: Average monthly sales ₹45,000.
- Median: Middle value. Handles outliers better than mean. Example: Typical salary in a team.
- Mode: Most frequent value. Example: Most sold product.

Spread of Data
- Range: Max minus min.
- Variance: Spread from the mean.
- Standard Deviation: How far values move from average. Low value means stable data.
Example: Avg sales ₹10,000. Std dev ₹500 means stable. Std dev ₹5,000 means volatile.

Percentages and Ratios
- Growth Rate: (Current - Previous) / Previous
- Conversion Rate: Leads to customers.

Correlation
- Relationship between two variables. Range: -1 to +1.
- Positive: Move together. Negative: Move opposite.
Example: Ad spend vs sales correlation 0.8.

Outliers
- Extreme values. Skew averages. Identify using sorting or box plots.

Sampling
- Small part of data. Saves time and cost.
- Full data often large. Samples give direction.

Common Mistakes
- Trusting averages only.
- Ignoring outliers.
- Confusing correlation with causation.

Mini Task
Take any sales data. Calculate mean, median, std dev. Check for outliers.

Statistics Resources: https://whatsapp.com/channel/0029Vat3Dc4KAwEcfFbNnZ3O

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Business Metrics Every Data Analyst Must Know ✅

Revenue Metrics
- Revenue: Total income from sales (e.g., monthly revenue ₹25 lakh)
- Gross Revenue vs Net Revenue: Gross (before costs), Net (after discounts and returns)
- Average Order Value: Revenue ÷ number of orders (e.g., ₹1,200 per order)

Growth Metrics
- Growth Rate: (Current − Previous) ÷ Previous (e.g., 15% month-over-month)
- Year-over-Year Growth: Compare same period last year

Customer Metrics
- Customer Count: Total active customers
- New vs Returning Customers: Shows retention strength
- Customer Acquisition Cost: Total marketing spend ÷ new customers
- Customer Lifetime Value: Total revenue from one customer over time

Retention and Churn
- Retention Rate: Customers who stayed ÷ total customers
- Churn Rate: Customers lost ÷ total customers (e.g., 1,000 customers, lost 50, churn rate 5%)

Marketing Metrics
- Conversion Rate: Conversions ÷ visitors
- Click-Through Rate: Clicks ÷ impressions
- Return on Ad Spend: Revenue ÷ ad spend

Product Metrics
- Daily Active Users: Users active per day
- Monthly Active Users: Users active per month
- DAU to MAU Ratio: Engagement strength

Operations Metrics
- Order Fulfillment Time: Time to deliver order
- Defect Rate: Defective units ÷ total units

Mini Task
Pick one business (E-commerce or EdTech). List 5 metrics it should track. Write one question each metric answers.

Let's take E-commerce:
1. Revenue: What's our total sales this month?
2. Customer Acquisition Cost: How much are we spending to acquire each new customer?
3. Retention Rate: How many customers are coming back to shop?
4. Average Order Value: What's the average amount customers are spending per order?
5. Order Fulfillment Time: How quickly are we delivering orders?

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SQL vs NoSQL Databases: Quick Comparison ✅

SQL Databases
- Structured data
- Fixed schema
- Table-based storage
- Strong consistency
- Popular tools: MySQL, PostgreSQL, SQL Server, Oracle
- Best use cases: Banking systems, ERP and CRM, transaction-heavy apps, reporting and analytics
- Job roles: Data Analyst, Backend Developer, Database Engineer, BI Developer
- Hiring reality: Mandatory in enterprises, core skill for analytics roles, used in almost every company
- India salary range: Fresher (4-7 LPA), Mid-level (8-18 LPA)
- Real tasks: Write complex queries, join multiple tables, build reports, ensure data integrity

NoSQL Databases
- Semi-structured or unstructured data
- Flexible schema
- Document, key-value, or graph based
- High scalability
- Popular tools: MongoDB, Cassandra, DynamoDB, Redis
- Best use cases: Real-time apps, big data systems, IoT platforms, rapidly changing products
- Job roles: Backend Developer, Data Engineer, Cloud Engineer, Platform Engineer
- Hiring reality: Strong demand in startups, common in cloud-native systems, often paired with SQL
- India salary range: Fresher (5-8 LPA), Mid-level (10-22 LPA)
- Real tasks: Store JSON documents, handle large traffic, design scalable schemas, optimize read and write speed

Quick Comparison
- Schema: SQL (fixed), NoSQL (flexible)
- Scaling: SQL (vertical), NoSQL (horizontal)
- Consistency: SQL (strong), NoSQL (eventual)
- Queries: SQL (powerful), NoSQL (simpler)

Role-based Choice
- Data Analyst: SQL required
- Backend Developer: Both useful
- Data Engineer: SQL + NoSQL
- Startup products: NoSQL preferred

Best Career Move
- Learn SQL first
- Add NoSQL for modern systems
- Use both in real projects

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Both 🙏
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✅ End to End Data Analytics Project Roadmap

Step 1. Define the business problem
Start with a clear question.
Example: Why did sales drop last quarter?
Decide success metric.
Example: Revenue, growth rate.

Step 2. Understand the data
Identify data sources.
Example: Sales table, customers table.
Check rows, columns, data types.
Spot missing values.

Step 3. Clean the data
Remove duplicates.
Handle missing values.
Fix data types.
Standardize text.
Tools: Excel or Power Query SQL for large datasets.

Step 4. Explore the data
Basic summaries.
Trends over time.
Top and bottom performers.
Examples: Monthly sales trend, top 10 products, region-wise revenue.

Step 5. Analyze and find insights
Compare periods.
Segment data.
Identify drivers.
Examples: Sales drop in one region, high churn in one customer segment.

Step 6. Create visuals and dashboard
KPIs on top.
Trends in middle.
Breakdown charts below.
Tools: Power BI or Tableau.

Step 7. Interpret results
What changed?
Why it changed?
Business impact.

Step 8. Give recommendations
Actionable steps.
Example: Increase ads in high margin regions.

Step 9. Validate and iterate
Cross-check numbers.
Ask stakeholder questions.

Step 10. Present clearly
One-page summary.
Simple language.
Focus on impact.

Sample project ideas
• Sales performance analysis.
• Customer churn analysis.
• Marketing campaign analysis.
• HR attrition dashboard.

Mini task
• Choose one project idea.
• Write the business question.
• List 3 metrics you will track.

Example: For Sales Performance Analysis

Business Question: Why did sales drop last quarter?

Metrics:
1. Revenue growth rate
2. Sales target achievement (%)
3. Customer acquisition cost (CAC)

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Data Analyst Interview Preparation Roadmap ✅

Technical skills to revise

- SQL
Write queries from scratch.
Practice joins, group by, subqueries.
Handle duplicates and NULLs.
Window functions basics.

- Excel
Pivot tables without help.
XLOOKUP and IF confidently.
Data cleaning steps.

- Power BI or Tableau
Explain data model.
Write basic DAX.
Explain one dashboard end to end.

- Statistics
Mean vs median.
Standard deviation meaning.
Correlation vs causation.

- Python. If required
Pandas basics.
Groupby and filtering.

Interview question types

- SQL questions
Top N per group.
Running totals.
Duplicate records.
Date based queries.

- Business case questions
Why did sales drop.
Which metric matters most and why.

- Dashboard questions
Explain one KPI.
How users will use this report.

- Project questions
Data source.
Cleaning logic.
Key insight.
Business action.

Resume preparation
- Must have Tools section.
- One strong project.
- Metrics driven points.
Example: Improved reporting time by 30 percent using Power BI.

Mock interviews
- Practice explaining out loud.
- Time your answers.
- Use real datasets.

Daily prep plan
1 SQL problem.
1 dashboard review.
10 interview questions.

- Common mistakes
Memorizing queries.
No project explanation.
Weak business reasoning.

- Final task
- Prepare one project story.
- Prepare one SQL solution on paper.
- Prepare one business metric explanation.

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