Data Wrangling (3)

1. Data Wrangling: The Foundation of Data Science A comprehensive guide to turning messy data into business value

2. THE REALITY The 80/20 Truth of Data Science 80% Data Preparation 20% WhyDatais Messy Generated by error-prone systems and humans Collected from dozens of disconnected sources Analysis&Modeling No standardization across databases Timespentwranglingand cleaningdata Timespentonactualinsights Sensors drift, users make typos, APIs change formats "The world is messy, and data reflects that mess with perfect fidelity" Key Insight: Great algorithms can't fix bad data. Data wrangling is the bridge between chaos and insight.

3. What is Data Wrangling? DataWrangling The comprehensive end-to-end process of making data usable for analysis transforming raw chaos into structured insights Data Munging The "grimy" technical work of transforming formats, rooted in hacker culture the hands-on cleaning and reshaping ETL (Extract, Transform, Load) Traditional IT approach with rigid, scheduled pipelines is now evolving to ELT, where data is loaded raw and transformed on-demand Modern Reality: Data is now loaded raw into data lakes, where analysts wrangle it on-demand for specific needs more agile than traditional ETL approaches

4. THE JOURNEY The Six-Stage Wrangling Process in Data Science 01 02 Discovery Structuring Locate data across systems, profile structure and quality, assess scope of cleanup needed Parse unstructured strings, reshape data (pivot/melt) for analysis, join disparate datasets 04 03 Cleaning Standardize formats and values, remove duplicates, handle missing data and outliers Enriching Add external data sources, combine transactional + contextual data for deeper insights 06 05 Validating Publishing Check format compliance, verify logical consistency, and confirm statistical distributions Deliver to target systems, document transformations, and create data dictionaries

5. The Cost of Bad Data $15M Annual Cost Real-WorldFailures Healthcare Algorithm Bias Used "healthcare spending" as proxy for "health needs," assigning lower risk scores to Black patients due to systemic access barriers. Amazon's AI Recruiter (2018) It was trained on historical data that was male dominant so the system learned to penalize resumes that mentioned "women." The project had to be scrapped entirely. Average per organization according to Gartner The Impact Lost marketingspend Supply chain errors Bad strategic decisions Destroyed stakeholder trust Target's Canada Expansion Unit-of-measure errors (inches vs. centimeters) led to wrong products shipped, billions in losses, and eventual market exit. The lesson is that "Garbage In, Garbage Out". None of the algorithms are capable of compensating for incomplete input data.

6. Critical Challenge: Missing Data Why Data GoesMissing MCAR MAR Sensorfailures Missing Completely at Random Missing at Random Optional form fields No relationship to any data. Safe to delete rows, but rare in practice. Related to observed data (e.g., women less likely to report age). Complex to handle. Software updates breaking schemas Users choosing not to provide information MNAR Missing Not at Random The value itself causes missingness (e.g., high earners hiding income). Most dangerous type. Solution Approaches Deletion 1 Remove incomplete rows (loses valuable data) Mean/MedianImputation 2 Fill with average(distorts distribution) Forward Fill 3 Use last known value(time-series only) ML Imputation 4 Predictbasedonothervariables MultipleImputation 5 Gold standard- creates uncertainty estimates Critical Insight: How you fill a blank cell is a modeling decision that fundamentally changes your analytical reality

7. Outliers: Signal or Noise? TheFundamentalQuestion Treatment Strategies Is this data point an error to remove or a crucial pattern to investigate? Delete If confirmed as a data entry error Winsorize Cap values at percentile (e.g., 99th) Keep Use robust methods (median vs. mean) Detection Methods Context is Everything: In fraud detection, the outlier IS the goal. In sales forecasting, it's often noise. Always investigate before deciding. VisualInspection Box plots, scatter plots never underestimate visualization Z-Score Analysis Z-score analysis utilizes the standard score to quantify the distance of a specific data point from the mean in terms of standard deviations. Interquartile Range This method works when your data isn't distributed. You can spot outliers by checking if values fall below Q1 - 1.5×IQR or above Q3 + 1.5×IQR Machine Learning Isolation Forests anomalies are easier to isolate from normal points

8. The Tooling Landscape Excel OpenRefine Python / R Alteryx / Trifacta Powerful, flexible, free, reproducible great pattern matching Specialized for cleaning, Visualworkflows,AI suggestions, democratizes access Visual, immediate, easy No reproducibility, crashes with big data  Hard to automate, not scalable  Requires programming skills Expensive licenses, vendor lock-in Cloud-Native Massive scale,cloud integration Ecosystem lock-in Tool Selection Criteria Team Skills Data Volume Budget Reproducibility Automation Bottom Line: Use what works for your context tool dogmatism is pointless. The best tool is the one your team can & use actually effectively use.

9. Wrangling vs Feature Engineering Twodifferentactivitieswithdistinctgoals understandingthedifferenceiscriticalforsuccess Aspect Feature Engineering Data Wrangling Goal Predictive Power Fidelity & Truth Mindset Domain Expert & Inventor Detective & Janitor Activity Create new variables Fix errors, standardize Question "What predicts the outcome?" "Is this accurate?" Timing Second First Example: Date of Birth Field Wrangling Convert text strings to date objects Standardize format (YYYY-MM-DD) Handle invalid dates Fix timezone issues Feature Engineering Calculate current age Create "Generation" category Add "Is_Birthday_This_Month" flag Compute "Days_Since_Last_Birthday" The Critical Sequence Wrangle First Can't build good features on dirty data Engineer Second Clean data alone won't reveal complex patterns Both are essential. Both require different skills. Master both to excel in data science.

10. THE FUTURE AI is Transforming Data Wrangling LLM-PoweredTools TraditionalTools Rigid rule-based transformations Semantic understanding of context Manual mapping tables required Knows "Code Ninja" = "Software Engineer" Can't handle semantic ambiguity Detects contextually wrong data ($1 car) Example: "Convert CA, Calif, California ³ CA" Example: "Standardize job titles" (no mapping needed) Emerging Capabilities Semantic Cleaning Agentic AI Human - on -th e -Lo op Fuzzy normalization without lookup tables, context-aware error detection, leverages world knowledge Goal-driven workflows like "Prepare sales data for Q4 analysis"4 autonomous planning, execution, and self-correction AI handles tedious execution while humans provide judgment and domain expertise4supervision instead of manual labor Critical Requirements: Data lineage to track every AI decision, auto-generated documentation (verified by humans), and governance for explainability in audits 20% Wrangling 80% Insight The vision for time spent The future of data work Reality Check: We're getting closer to this vision, but human judgment remains essential for complex decisions and domain context.