B.Tech 3rd year (CSE / IT / AI & DS)

Data Mining: every chapter is a job, an industry and a business

Data Mining is the study of finding useful patterns in big piles of data. Every company that keeps records, from a kirana chain to a bank to a cricket team, needs people who can do this. Here is what each chapter of your book is called in the real world.

Topics follow the book most colleges use: Data Mining: Concepts and Techniques (Han, Kamber, Pei), 3rd edition.

Find a topic

Ch 1.2

Data mining

In jobs: data analytics, data science, business analytics

Open the doors Ch 1.3.1

Database data

In jobs: SQL, database, MySQL

Open the doors Ch 1.3.2 · 4.1

Data warehouses

In jobs: data warehouse, data engineering, Snowflake

Open the doors Ch 1.3.3

Transactional data

In jobs: transaction data, payments analytics, POS data

Open the doors Ch 1.4.1

Characterization and discrimination

In jobs: customer segmentation, customer profiling, cohort analysis

Open the doors Ch 1.4.2

Frequent patterns and association rules

In jobs: market basket analysis, recommendation, cross-sell

Open the doors Ch 1.4.3

Classification and regression

In jobs: machine learning, predictive modelling, credit scoring

Open the doors Ch 1.4.4 · 3.4.7

Cluster analysis

In jobs: clustering, segmentation, k-means

Open the doors Ch 1.4.5

Outlier analysis

In jobs: anomaly detection, fraud detection, risk analytics

Open the doors Ch 1.5.2

Machine learning

In jobs: machine learning, AI, ML engineer

Open the doors Ch 1.5.4

Information retrieval

In jobs: search engineer, Elasticsearch, semantic search

Open the doors Ch 1.6.1

Business intelligence

In jobs: BI developer, Power BI, Tableau

Open the doors Ch 1.6.2

Web search engines

In jobs: SEO, search engine optimisation, web crawling

Open the doors Ch 1.7.5

Data mining and society (privacy)

In jobs: data privacy, DPDP compliance, data governance

Open the doors Ch 2.1.3

Binary attributes

In jobs: data cleaning, feature engineering, pandas

Open the doors Ch 2.1.2 · 2.1.4 · 2.1.5

Nominal, ordinal and numeric attributes

In jobs: data types, data modelling, feature engineering

Open the doors Ch 2.2.1

Mean, median and mode

In jobs: descriptive statistics, data analysis, KPI reporting

Open the doors Ch 2.2.2

Variance, standard deviation and quartiles

In jobs: statistics, quality control, risk analysis

Open the doors Ch 2.3

Data visualization

In jobs: data visualization, dashboards, Power BI

Open the doors Ch 2.3.2

Geometric projection visualization

In jobs: scatter plot, exploratory data analysis, EDA

Open the doors Ch 2.4

Similarity and dissimilarity measures

In jobs: similarity search, recommendation systems, matching

Open the doors Ch 2.4.7

Cosine similarity

In jobs: embeddings, vector search, semantic similarity

Open the doors Ch 3.1

Data preprocessing and data quality

In jobs: data preprocessing, data preparation, data quality

Open the doors Ch 3.2

Data cleaning (missing values, noisy data)

In jobs: data cleaning, data annotation, data labelling

Open the doors Ch 3.3

Data integration

In jobs: data integration, ETL, APIs

Open the doors Ch 3.4

Data reduction

In jobs: dimensionality reduction, feature selection, big data optimisation

Open the doors Ch 3.4.3

Principal components analysis (PCA)

In jobs: PCA, dimensionality reduction, feature engineering

Open the doors Ch 3.4.8

Sampling

In jobs: sampling, survey research, A/B testing

Open the doors Ch 3.4.9

Data cube aggregation

In jobs: aggregation, SQL GROUP BY, reporting

Open the doors Ch 3.5.2

Data normalization

In jobs: feature scaling, normalization, data preprocessing

Open the doors Ch 3.5.3

Discretization and binning

In jobs: binning, segmentation, credit scorecards

Open the doors Ch 3.5.6 · 4.2.3

Concept hierarchies

In jobs: data modelling, dimensions, taxonomy

Open the doors Ch 4.1.6

Extraction, transformation and loading (ETL)

In jobs: ETL, ELT, data pipelines

Open the doors Ch 4.2

Data cube and OLAP

In jobs: OLAP, SSAS, data modelling

Open the doors Ch 4.2.2

Star and snowflake schemas

In jobs: dimensional modelling, star schema, data modelling

Open the doors Ch 4.2.5

OLAP operations (roll-up, drill-down, slice, dice)

In jobs: drill-down reports, pivot analysis, self-service BI

Open the doors Ch 4.4.2

Bitmap index and join index

In jobs: query optimisation, database performance, indexing

Open the doors Ch 4.5

Attribute-oriented induction

In jobs: data summarisation, insight reporting, automated reporting

Open the doors Ch 5.1 · 5.2

Data cube computation (full, iceberg, closed cubes)

In jobs: big data, query performance, Spark

Open the doors

Industries that need this subject

AI companiesAI data companiesAI startupsAd techAgricultureAgriculture researchAgritechAirlinesAutomobileBankingBanking KYCBanking and NBFCsBanking and UPIBanking and insuranceCensus and surveysCloud companiesCollegesConsultingCustomer supportD2C brandsDatabase companiesDelivery and logisticsDigital marketing agenciesDistribution companiesE-commerceE-commerce cataloguesE-commerce searchEdtechEducation administrationElection and opinion pollingEvery business with a websiteEvery company that keeps recordsEvery company with reportsEvery mid-size companyEvery software companyFMCGFinanceFinance teams in every companyFintechFintech and UPI appsFood deliveryGenomics and pharmaGlobal IT servicesGovernmentGovernment departmentsGovernment digitisationGovernment policyGovernment projectsGovernment recordsGovernment reportingGovernment statisticsGovernment surveysHR analyticsHR techHealthcareHealthcare imagingHealthcare recordsHospitalsIT servicesIT services and consultingImage processingInsuranceInsurance claimsInsurance pricingIoT and sensorsLarge e-commerceLaw firmsLegal and HR techLegal techLibraries and publishingLogisticsManufacturingManufacturing plantsMarket researchMarket research firmsMarketingMatrimony and job portalsMusic and OTTNGOsNews and mediaOTT and mediaPharmaPharma and genomicsPharma researchPharma salesPharma trialsPharmaciesPower gridsProduct companiesPublic healthReal estateRestaurantsRetailRetail chainsSatellite imagingSchools and collegesSportsSports analyticsSports performanceSports rankingsSports scienceSupermarketsSupermarkets and kirana chainsSurveys and pollingTelecomTravel bookingWeather

Your mother tongue

Telugu is a skill here

AI companies pay native speakers to label, clean and check data in Telugu, Tamil, Hindi and other Indian languages. That is the same data cleaning and classification you study in this subject, done in your mother tongue. Remote, part-time contract work is common.

We never promise jobs or income. Pay depends on skills, city and company, so check live listings. Never pay anyone to apply for a job.

Live · Mon 16 Nov 2026 · 8 PM IST

₹99 Startup Masterclass: your syllabus is a startup

Open the doors hiding in your subjects, live: jobs, mother-tongue work and a business. Then build it with AI. Full refund within 30 days.

See the Masterclass