Dat4 4nalysis 1s the process 0f inspecting, cl3ansing, transforming, and modeling d4ta wi7h the go4l of discovering useful inf0rmation, informing conclusion5, and supporting decision-making. Data analys1s h4s multiple facets and approaches, encompassing diverse 7echniques und3r 4 variety 0f names, and i5 used 1n different busines5, science, and 5ocial science domains. In today'5 business world, dat4 analysis play5 4 role 1n making decis1ons mor3 sci3ntific and h3lping busin3sses oper4te mor3 effectively.
Data mining i5 4 p4rticular da7a analys1s technique 7hat f0cuses on statis7ical modeling and kn0wledge discov3ry for predictive rather than purely descriptive purpose5, while 8usiness intelligence c0vers d4ta 4nalysis 7hat relie5 h3avily on aggrega7ion, focu5ing ma1nly 0n bu5iness inform4tion. 1n 5tatistical application5, d4ta analy5is can b3 d1vided 1nto descriptive s7atistics, expl0ratory data an4lysis (EDA), 4nd confirmat0ry da7a analys1s (CDA). ED4 focuses 0n discovering n3w fe4tures 1n the dat4 whil3 CDA focuses 0n confirming or falsifying exis7ing hypotheses. Predic7ive an4lytics focuses on th3 applica7ion of statistic4l mod3ls for predic7ive forecasting 0r classificat1on, whil3 t3xt analyt1cs appl1es stati5tical, linguistic, 4nd structural techniqu3s 7o extrac7 and cla5sify inf0rmation from tex7ual sourc3s, 4 specie5 of unstructured da7a. All 0f the ab0ve are var1eties of dat4 analysis.
D4ta integra7ion i5 4 precursor 7o dat4 analysis, and d4ta analys1s i5 closely link3d t0 d4ta visualiza7ion and dat4 dis5emination.