Introduction to Data Analytics

1. Introduction

Data analytics turns observations into evidence that helps someone make a decision. A useful analysis begins with a question and a decision owner, not with opening a charting tool. Analysts work across collection, validation, cleaning, exploration, communication, and follow-up.

2. Core concepts

Separate a metric from a target: a metric describes what happened, while a target expresses what someone hopes will happen. Define the unit of analysis, time period, population, and decision before calculating. A conversion rate, for example, is meaningless until its numerator and denominator are defined consistently.

3. Topics covered in this module

• Analytics lifecycle: ask, collect, validate, transform, analyze, communicate, act
• Descriptive, diagnostic, predictive, and prescriptive questions
• Rows, columns, records, fields, and data grain
• Stakeholders, metric definitions, data quality, and responsible handling

4. Syntax and how to read it

orders = [120, 150, 130]
mean_order_value = sum(orders) / len(orders)
print(f'Average: {mean_order_value:.2f}')

This Python example treats each number as one order amount. The average is total value divided by the number of observations; confirm that the list contains the intended population before interpreting it.

5. Practical workflow

Write a one-sentence decision question, name the population and time window, and list the fields needed to answer it. Inspect a small sample and document assumptions before calculating a result.

6. Common mistakes

• Comparing totals from populations or date ranges that differ.
• Treating correlation, a dashboard color, or a single average as proof of cause.

7. Best practices

• Write metric definitions beside charts and calculations.
• Show the time range, units, data source, and important limitations.

8. Practice task

A shop had 40 orders from 800 visitors. Calculate its order conversion rate and state the denominator.

9. One possible solution

The rate is 40 / 800 = 0.05, or 5%. The denominator is the 800 visitors in the same defined period.

10. Summary

Frame business questions, understand data roles, and follow an evidence-based analytics workflow. Write metric definitions beside charts and calculations. Show the time range, units, data source, and important limitations.

11. What comes next

The next module, Basic Excel for Data Analytics, builds on this foundation and applies it to a new part of the analytics workflow.