Basic Data Analytics

📊 Basic Data Analytics

✅ 1. What is Data Analytics?

Data Analytics is the process of collecting, organizing, analyzing, and interpreting data to generate useful insights for making informed decisions. It helps organizations understand trends, patterns, and relationships in data to improve business outcomes.

Example: Analyzing sales data to identify which products are most popular among customers.

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✅ 2. Why is Data Analytics Important?

Reason	Impact
Informed Decision-Making	Data-driven insights improve strategic planning.
Identify Trends and Patterns	Understand customer behavior, market shifts.
Improve Efficiency and Reduce Costs	Optimize operations and resource allocation.
Enhance Customer Experience	Personalize services based on data.
Risk Management	Detect anomalies and prevent potential issues.

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✅ 3. Types of Data Analytics

Type	Description	Example
Descriptive Analytics	Summarizes past data to understand what happened.	Monthly sales reports.
Diagnostic Analytics	Examines data to understand why something happened.	Analyzing why sales dropped last month.
Predictive Analytics	Uses historical data to predict future outcomes.	Forecasting next quarter's revenue.
Prescriptive Analytics	Suggests actions based on data analysis.	Recommending marketing strategies.

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✅ 4. Basic Steps in Data Analytics Process

Step	Description
1. Data Collection	Gathering data from multiple sources (e.g., databases, surveys).
2. Data Cleaning	Removing duplicates, fixing errors, handling missing values.
3. Data Exploration (EDA)	Understanding data patterns and summaries.
4. Data Analysis	Applying statistical methods or tools to extract insights.
5. Data Visualization	Presenting data in charts, graphs, dashboards for better understanding.
6. Interpretation and Reporting	Making sense of data and communicating results to stakeholders.

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✅ 5. Common Data Analytics Techniques

Technique	Purpose
Summarization (Aggregation)	Summing or averaging data for insights.
Filtering and Sorting	Focus on specific data points (e.g., top customers).
Correlation Analysis	Understanding relationships between variables.
Trend Analysis	Observing data patterns over time.
Segmentation	Dividing data into meaningful groups (e.g., customer segments).
Statistical Analysis	Applying mean, median, mode, standard deviation.

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✅ 6. Basic Tools for Data Analytics

Tool	Usage
Microsoft Excel / Google Sheets	Data analysis, pivot tables, charts.
SQL (Structured Query Language)	Querying and analyzing database data.
Tableau / Power BI	Data visualization and dashboard creation.
Python (Pandas, Matplotlib)	Advanced data manipulation and visualization.
R (ggplot2, dplyr)	Statistical analysis and visualizations.

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✅ 7. Simple Example of Data Analytics Flow

Business Question:

What is the trend in monthly sales over the past year?

Steps to Answer:

1. Collect sales data from the sales system.

2. Clean data: Remove duplicates, fix dates.

3. Aggregate data to get total sales per month.

4. Visualize using a line chart to show trends.

5. Interpret: Identify peak and low sales months.

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✅ 8. Basic Data Analytics Metrics to Know

Metric	Meaning	Example
Average (Mean)	Sum of values divided by count.	Average sale per customer.
Median	Middle value when sorted.	Median salary in a company.
Mode	Most frequently occurring value.	Most sold product.
Standard Deviation	Measure of data spread.	Variation in monthly sales.
Growth Rate (%)	Rate of increase/decrease over time.	Monthly revenue growth.

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✅ 9. Data Visualization Basics

Chart Type	When to Use	Example
Bar Chart	Compare categories.	Sales by region.
Line Chart	Show trends over time.	Monthly sales trend.
Pie Chart	Show proportion of categories.	Market share by company.
Scatter Plot	Analyze correlation between two variables.	Ad spend vs. sales.
Histogram	Distribution of a single variable.	Age distribution of customers.

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✅ 10. Example Use Case: Basic Sales Analysis

Step	Example
Question	What are the top 5 products sold this quarter?
Data Source	Sales transaction database.
Data Cleaning	Remove duplicates, correct product names.
Analysis	Aggregate total sales per product.
Visualization	Bar chart of top 5 products.
Insight	Identify best-selling products for promotions.

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✅ 11. Role of Business Analyst in Data Analytics

Task	Responsibility
Gather Business Requirements	Understand what stakeholders want to analyze.
Identify Data Sources	Locate where data is stored.
Define Metrics and KPIs	Clarify which numbers matter for the business.
Collaborate with Data Teams	Work with data analysts and IT teams.
Present Insights	Communicate findings clearly and effectively.

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✅ Conclusion

Data Analytics is a powerful tool for Business Analysts to turn raw data into actionable insights. Understanding the basics of data collection, cleaning, analysis, and visualization is crucial to support business decision-making.