Foundations of Exploratory Data Analysis

1. Exploratory Data Analysis

2. WHAT IS EDA Exploratory Da ta Ana ly sis is the st age where we tr y to u n derstand the data befo re building any mod el or dr awing conclusions . It focuse s on explo ring pat tern s within the da ta , identifying errors and inc onsist enci es, and validati ng or questionin g initial assumptions. By doing this early, EDA ens ures that decisi ons are based on r e liable insigh ts r ather than guesswork and for ms the foundation for all further anal ysis. In a practic al da ta sc ience course in K erala, this step is essen tial f or helping learners work confidently wit h real- wor ld dat a i nst ead of jus t theoreti cal examples.

3. WHY EDA IS IMPORTANT Real- world data is rarely clean or reli able, and EDA helps uncov er pro b lems early. Data often c on t ains missing or inco rrec t v alues Hi dden errors can lead to wr ong conclusions EDA reduce s the risk of misle ading resul ts Better EDA leads to bett er decisions and models

4. UNDERSTANDING THE DATA STRUCTURE Before analysis, you must know wh a t the dataset actually contains and how it is organized. Number of re cords a nd features Meaning and purpose of each co lumn Data ty pes such as numer ic, categorical, da te , o r text Units and scale of measurement Without underst anding the s tructure, any analys is becomes guesswork .

5. DATA QUALITY CHECKS EDA helps determine wheth er the data can b e trusted fo r analysis. Ide ntify missi ng values D ete ct dupli cate reco rd s Find inva l id or inconsiste n t en tries Spo t un expected or incorrect catego ri es Poor data quality always le ads to poor re sults.

6. UNVARIATE ANALYSIS This s tep focuses o n understan di n g each variable on its own. Di stribution of values Mea n, median, and ra n ge S p read and s kewnes s Frequency of categ orie s It helps confirm whe ther individual fe atures behave as expected .

7. BIVARIATE ANALYSIS Bivariate analysis examines how two va r iables are related to e ach other . Correlation between numerical variab les Com pariso n across diff erent categories Tre nds or patterns over time This step helps identify factors that influenc e outco m es.

8. MULTIVARIATE ANALYSIS Mo s t real -world problems involve multi ple factors working t og ether. Study comb ined effects of multiple f e atures Ident if y hidden or in direct relati onships Detect mu lt ico llinear ity b etween variables Si ng le-variab le thinki ng rarely e x pla ins re al behavi or.

9. OUTLIER DETECTION Outliers are extr eme values that can st rongly affect analysis results. Identify u nusual or ex t reme observatio ns Decide whether outliers a re erro rs or valid cases R educe distortion i n analysis and modeling Not ev ery outlier should be removed blindly.

10. ASSUMPTION CHECKING AND HYPOTHESIS FORMATION EDA hel ps prepar e the data and mindset for m odeling. C heck assumptions like norm ality or lineari ty Fo rm hypothes es based on observed patt erns Decide whe th er modeli ng is suita ble or necessary EDA gu ides the di re ction of the entire projec t.

11. THANK YOU