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Data Types, Sources, and Structure

Lesson 4: Data Types, Sources, and Structure

What kinds of data do analysts work with?

Type

Description

Examples

Common tools

StructuredConsistent rows and columnsSpreadsheets, database tablesExcel, SQL
Semi-structuredSome organisation, but fields can vary between recordsJSON events, XML, log filesPython, SQL with JSON functions
UnstructuredNo predefined formatFree text, images, audio, PDFsNLP tools, specialised libraries

Most entry-level analytics work uses structured data.

Common field types

Field type

Description

Example

Can you calculate with it?

Numeric (continuous)Measured quantityRevenue, weight, timeYes
Numeric (discrete)Counted quantityNumber of itemsYes
Categorical (nominal)Labels with no orderRegion, product categoryCount only
Categorical (ordinal)Labels with a natural orderLow / Medium / High; ratings 1–5Count, rank, sometimes median
Date / timePoints in timeOrder date, timestampYes, for durations and trends
BooleanTrue / falseIs_returning_customerCount, percentage
IdentifierUniquely labels a recordOrder ID, customer IDNo
  • Numbers may represent measurements (revenue) or labels (a product code).
  • A code like 00127 should not be treated as a quantity just because it contains digits. Storing it as a number would also drop the leading zeros.

Where does data come from?

Source

What it contains

Example

Transaction systemsOrders, payments, bookingsE-commerce database
CRM toolsCustomer contacts, leads, dealsSalesforce, HubSpot
Web / app analyticsPage views, events, sessionsGoogle Analytics, Mixpanel
SurveysOpinions and self-reported dataCustomer satisfaction forms
SpreadsheetsManual or exported dataBudget trackers
Application logsSystem events and errorsServer logs
Public datasetsOpen government or research dataCensus data, Kaggle datasets
APIsData fetched from other servicesWeather, exchange rates

Before using any source, check:

  • Owner – who maintains it and who can answer questions?
  • Update frequency – real-time, daily, monthly?
  • Definitions – what does each field mean?
  • Permissions – are you allowed to use it for this purpose?
  • Known gaps – missing periods, tracking changes, known errors?

Example

An order table:

order_id

order_date

customer_id

product_category

quantity

unit_price

region

100012026-07-01C204Books2350South
100022026-07-01C118Electronics14,999North

Column

Role

order_idIdentifies a record
order_dateSupports time-based analysis
customer_idLinks to the customers table
product_category, regionCategories for grouping
quantity, unit_priceSupport calculations (revenue = quantity × unit_price)

Data quality questions

Dimension

Question to ask

Example problem

CompletenessAre required values present?15% of rows have no region
ValidityDo values follow the rules?Negative quantities
ConsistencyAre values recorded the same way?"Chennai", "chennai", "CHN"
UniquenessAre records duplicated?The same order appears twice
TimelinessIs the data current enough?Data last refreshed three weeks ago
AccuracyDoes it match reality?Prices differ from the product catalogue

A date stored as text, a duplicated order, or inconsistent region names can all distort the result.

Key takeaways

  • Understand what each column means before calculating anything.
  • A field's meaning matters more than its appearance or storage format.
  • Always check quality across completeness, validity, consistency, uniqueness, timeliness, and accuracy.

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Data Analytics Tutorial

J

Jebasta

Data Analytics Tutorial

MODULE 1 : Foundations of Data Analytics
lessonIntroduction - Foundations of Data Analytics
lessonWhat is Data Analystics
lessonTypes of Data Analytics
lessonThe Data Analytics Workflow
lessonData Types, Sources, and Structure
lessonTurning a Business Request into an Analytical Question
MODULE 2 : Spreadsheets and Practical Data Handling
lessoniIntroduction - Spreadsheets and Practical Data Handling
lessonUnderstanding Spreadsheet Structure
lessonFormulas and Cell References
lessonEssential Functions for Analysis
lessonSorting, Filtering, and Data Validation
lessonLookups and Pivot Tables
MODULE 3 : SQL for Data Analytics
lessonSQL for Data Analytics - Introduction
lessonDatabases and Your First SQL Query
lessonFiltering, Sorting, and Limiting Results
lessonAggregate Functions and GROUP BY
lessonJoining Related Tables
lessonCASE, Subqueries, and CTEs
MODULE 4 : Python for Data Analytics
lessonPython for Data Analytics Introduction
lessonPython Basics for Analysts
lessonConditions, Loops, and Functions
lessonReading and Inspecting Data with pandas
lessonCleaning and Transforming Data
lessonGrouping, Summaries, and Visualisation
MODULE 5 : Statistics for Data Analytics
lessonStatistics for Data Analytics Introduction
lessonDescriptive Statistics
lessonDistributions, Percentiles, and Outliers
lessonSampling and Uncertainty
lessonHypothesis Testing and Confidence Intervals
lessonCorrelation, Regression, and Causation
MODULE 6 : Data Visualization Reporting and Dashboards
lessonData Visualisation, Reporting, and Dashboards Introduction
lessonChoosing the Right Chart
lessonVisual Design and Avoiding Misleading Charts
lessonKPIs and Dashboard Planning
lessonBuilding Reports in BI Tools
lessonPresenting an Insight and Recommendation
MODULE 7 : Exploratory Data Analysis and Data Cleaning
lessonExploratory Data Analysis and Data Cleaning - Introduction
lessonStarting an Exploratory Data Analysis
lessonMissing Values and Their Meaning
lesson Duplicates, Inconsistent Labels, and Data Types
lesson Outliers and Validation Rules
lessonA Reproducible Cleaning Pipeline
MODULE 8 : Business Product and Marketing Analytics
lessonBusiness, Product, and Marketing Analytics Introduction
lessonSales and Revenue Analytics
lessonCustomer Retention and Churn
lessonMarketing Funnel and Campaign Analytics
lessonProduct Analytics and User Journeys
lessonOperational Analytics and Root-Cause Investigation
MODULE 9 : Advanced Analytics - Forecasting and Machine Learning Basics
lessonAdvanced Analytics, Forecasting, and Machine Learning Basics - Introduction
lessonForecasting and Time-Series Basics
lessonRegression and Classification
lessonClustering and Segmentation
lessonModel Evaluation and Choosing Metrics
lessonOverfitting, Leakage, and Responsible Modelling
MODULE 10 : Data Workflows - Warehousing and Professional Practice
lessonData Workflows, Warehousing, and Professional Practice - Introduction
lessonOperational Databases and Data Warehouses
lessonETL, ELT, and Data Pipelines
lessonAutomation, Scheduling, and Monitoring
lessonDocumentation, Version Control, and Collaboration
lessonPrivacy, Governance, and Ethical Practice
MODULE 11 : Final Lesson: Bringing It All Together
lessonFinal Lesson: Bringing It All Together

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Data Analysis and Processing with Python using Pandas - Data Science Course

5 hrs

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Data Analytics Tutorial

MODULE 1 : Foundations of Data Analytics
lessonIntroduction - Foundations of Data Analytics
lessonWhat is Data Analystics
lessonTypes of Data Analytics
lessonThe Data Analytics Workflow
lessonData Types, Sources, and Structure
lessonTurning a Business Request into an Analytical Question
MODULE 2 : Spreadsheets and Practical Data Handling
lessoniIntroduction - Spreadsheets and Practical Data Handling
lessonUnderstanding Spreadsheet Structure
lessonFormulas and Cell References
lessonEssential Functions for Analysis
lessonSorting, Filtering, and Data Validation
lessonLookups and Pivot Tables
MODULE 3 : SQL for Data Analytics
lessonSQL for Data Analytics - Introduction
lessonDatabases and Your First SQL Query
lessonFiltering, Sorting, and Limiting Results
lessonAggregate Functions and GROUP BY
lessonJoining Related Tables
lessonCASE, Subqueries, and CTEs
MODULE 4 : Python for Data Analytics
lessonPython for Data Analytics Introduction
lessonPython Basics for Analysts
lessonConditions, Loops, and Functions
lessonReading and Inspecting Data with pandas
lessonCleaning and Transforming Data
lessonGrouping, Summaries, and Visualisation
MODULE 5 : Statistics for Data Analytics
lessonStatistics for Data Analytics Introduction
lessonDescriptive Statistics
lessonDistributions, Percentiles, and Outliers
lessonSampling and Uncertainty
lessonHypothesis Testing and Confidence Intervals
lessonCorrelation, Regression, and Causation
MODULE 6 : Data Visualization Reporting and Dashboards
lessonData Visualisation, Reporting, and Dashboards Introduction
lessonChoosing the Right Chart
lessonVisual Design and Avoiding Misleading Charts
lessonKPIs and Dashboard Planning
lessonBuilding Reports in BI Tools
lessonPresenting an Insight and Recommendation
MODULE 7 : Exploratory Data Analysis and Data Cleaning
lessonExploratory Data Analysis and Data Cleaning - Introduction
lessonStarting an Exploratory Data Analysis
lessonMissing Values and Their Meaning
lesson Duplicates, Inconsistent Labels, and Data Types
lesson Outliers and Validation Rules
lessonA Reproducible Cleaning Pipeline
MODULE 8 : Business Product and Marketing Analytics
lessonBusiness, Product, and Marketing Analytics Introduction
lessonSales and Revenue Analytics
lessonCustomer Retention and Churn
lessonMarketing Funnel and Campaign Analytics
lessonProduct Analytics and User Journeys
lessonOperational Analytics and Root-Cause Investigation
MODULE 9 : Advanced Analytics - Forecasting and Machine Learning Basics
lessonAdvanced Analytics, Forecasting, and Machine Learning Basics - Introduction
lessonForecasting and Time-Series Basics
lessonRegression and Classification
lessonClustering and Segmentation
lessonModel Evaluation and Choosing Metrics
lessonOverfitting, Leakage, and Responsible Modelling
MODULE 10 : Data Workflows - Warehousing and Professional Practice
lessonData Workflows, Warehousing, and Professional Practice - Introduction
lessonOperational Databases and Data Warehouses
lessonETL, ELT, and Data Pipelines
lessonAutomation, Scheduling, and Monitoring
lessonDocumentation, Version Control, and Collaboration
lessonPrivacy, Governance, and Ethical Practice
MODULE 11 : Final Lesson: Bringing It All Together
lessonFinal Lesson: Bringing It All Together

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Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

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Refer & Earn

HCL GUVI (Grab Your Vernacular Imprint) Geek Network Private Limited is a leading online learning and skills development company, incubated by IIT Madras and IIM Ahmedabad.

Established in 2014 and acquired by the HCL Group in 2022, HCL GUVI is dedicated to providing effective and high-quality learning and skilling programs that transcend language barriers in technology education. HCL GUVI today is trusted by over 4.8 million users, 20 languages and 2500+ corporate partners.

app storeplay storeRefer & Earn
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