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Hazardous9hub/README.md
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Portfolio Resume LinkedIn Tableau HackerRank LeetCode Email



⛽ Enterprise Operations
350+ Retail Outlets
Monitored daily sales & stock at BPCL 		🔍 Cloud Relational SQL
99K+ Rows Analyzed
Target BigQuery multi-table case study 		🏆 Database Competency
5-Star Gold Badge
HackerRank verified SQL ranking 		🎓 Academic Merit
8.67 CGPA
B.E. Mechanical Engineering (VTU)

👨‍💻 About Me & My Journey

I am a Data Analyst with a degree in Mechanical Engineering (8.67 CGPA) and a year of on-ground enterprise operations experience at Bharat Petroleum Corporation Limited (BPCL).

  • ⚙️ From Physical Systems to Data: My engineering background trained me to think in numbers, constraints, and root causes. At BPCL, I saw how physical operations rely on day-to-day data accuracy: monitoring fuel throughput, underground tank inventories, and dealer credit risk across 350+ retail stations.
  • 🛠️ Practical Process Automation: Working with SAP ECC, Advanced Excel, and VBA, I rebuilt manual daily reporting routines, cutting MIS compilation time from 2.5 hours down to 25 minutes (70% reduction) and evaluated bank credit eligibility under the e-DFS facility for 50+ retail dealers.
  • 📈 Rigorous Upskilling with Scaler DSML: I committed to deep analytical training through Scaler Academy's Data Science & Machine Learning program, mastering SQL (BigQuery, PostgreSQL, MySQL), Python (Pandas, NumPy, Scipy, Seaborn), Applied Probability, Hypothesis Testing, and Tableau Public.
  • 🎯 Current Focus: Open to full-time Data Analyst / Business Analyst roles where I can combine operational intuition with clean SQL queries, statistical rigor, and executive dashboards.

🛠️ Technical Arsenal

Languages & Querying Python SQL BigQuery MySQL VBA
Analytics & Statistics Pandas NumPy SciPy Probability Hypothesis Testing
BI & Data Visualization Tableau Power BI Excel Seaborn
Enterprise & Workflow SAP ECC Git Jupyter Colab

📂 Featured Case Studies & Live Dashboards

1. 🛒 Target E-Commerce: Logistics Disparity & Revenue Scaling (Google BigQuery & SQL)
  • Business Problem: Evaluate delivery lead-times across Brazilian states, detect logistical bottlenecks, and analyze customer repeat purchasing trends.
  • Methodology: Queried 99,000+ customer records across 6 relational tables using Google BigQuery; wrote modular Common Table Expressions (CTEs), multi-table joins, and ranking window functions.
  • Key Finding: Identified a 3.5x regional delivery lead-time disparity between northern and southeastern states, and demonstrated how expanding regional fulfillment centers would protect a 137% YoY revenue surge.
  • Key SQL Pattern: 
    WITH delivery_metrics AS (
  SELECT customer_state, AVG(DATE_DIFF(order_delivered_customer_date, order_purchase_timestamp, DAY)) as avg_days
  FROM `target.orders` JOIN `target.customers` USING(customer_id)
  GROUP BY customer_state
)
SELECT customer_state, avg_days, DENSE_RANK() OVER(ORDER BY avg_days DESC) as rank
FROM delivery_metrics;
  • Direct Project Links:
    🔗 GitHub Repository • 📄 Master SQL Script (all_target_queries.sql) • 📁 SQL Queries Directory (7 Scripts) • 📊 Case Study PDF Report
2. 🛒 Walmart: Consumer Purchase Behavior & Central Limit Theorem (Python, Statistics & CLT)
  • Business Problem: Test demographic spending assumptions across 550,000+ Black Friday transactions to guide seasonal stock planning and marketing budget allocation.
  • Methodology: Applied Central Limit Theorem (CLT) sampling distributions ($n \in {30, 100, 500, 1000}$), calculated standard error contraction, and built 90%, 95%, and 99% Confidence Intervals across demographics.
  • Key Finding: Men spend +$702.96 (+8.05%) more per transaction overall ($9,437 vs. $8,734) with zero CI overlap at 99% confidence (gap >$649), yet within specific categories (such as Product_Category 1), spending between men and women is virtually identical ($11 difference). Marital status showed no spending variance ($4.74 difference, completely overlapping CIs).
  • Key Python Snippet: 
    # Computing sample mean standard error and 95% confidence interval
std_err = df['Purchase'].std() / np.sqrt(len(df))
margin_error = stats.norm.ppf(0.975) * std_err
ci_95 = (df['Purchase'].mean() - margin_error, df['Purchase'].mean() + margin_error)
  • Direct Project Links:
    🔗 GitHub Repository • 📓 Jupyter Notebook • 📊 Case Study PDF Report • 📱 LinkedIn Carousel Slides
3. 🚲 Yulu Micro-Mobility: Statistical Hypothesis Testing & Demand Drivers (Python, ANOVA, Chi-Square & T-Tests)
  • Business Problem: Diagnose electric cycle revenue slumps and evaluate which environmental and temporal factors govern hourly rental demand in urban transit networks.
  • Methodology: Formulated strict 6-step hypothesis testing frameworks across 10,886 hourly records: 2-Sample Independent T-Test (Workingday effect), One-Way & Two-Way ANOVA (Seasonal and Weather variation using Pingouin), and Chi-Square Test of Independence (Weather vs Season with Cochran's rule check on $n=1$ outlier).
  • Key Finding: Total demand appears identical on working vs non-working days ($p = 0.226$), but rider segmentation completely shifts: casual riders surge by +136% on weekends, while registered commuters dominate weekdays (twin peaks at 8 AM and 5 PM). Spring experiences an acute 50% demand collapse ($116.3$ vs $234.4$ in Fall, $p < 10^{-100}$), and adverse weather reduces demand by 42%.
  • Key Python Snippet: 
    # Two-Way ANOVA testing Season, Weather, and interaction effects
model = pg.anova(dv='count', between=['weather', 'season'], data=yulu[yulu['weather'] != 4], ss_type=2)
  • Direct Project Links:
    🔗 GitHub Repository • 📓 Jupyter Notebook • 📊 Case Study PDF Report
4. 🏃 AeroFit: Customer Segmentation & Treadmill Purchasing Drivers (Python, EDA, Probability)
  • Business Problem: Define distinct customer profiles across entry-level (KP281), mid-tier (KP481), and commercial (KP781) treadmills to target sales recommendations.
  • Methodology: Constructed two-way contingency tables, evaluated conditional probabilities $P(\text{Product} \mid \text{Fitness Rating})$ vs. demographic indicators, and conducted distribution analysis.
  • Key Finding: Self-rated fitness (level 4–5) was the single strongest indicator for commercial model purchases, showing an 80%+ conditional probability for the premium KP781 model, proving that fitness habits matter far more than age or income alone.
  • Key Python Snippet: 
    # Conditional probability matrix
pd.crosstab(index=df['Fitness'], columns=df['Product'], normalize='index') * 100
  • Direct Project Links:
    🔗 GitHub Repository • 📓 Jupyter Notebook • 📊 Case Study PDF Report • 📈 Tableau Packaged Dashboard (.twbx)
5. 📊 Superstore Executive Sales & Profitability Dashboard (Tableau Public)
  • Business Problem: Leadership needed immediate visibility into loss-making product categories, regional discount sensitivity, and customer margin contributions.
  • Methodology: Built a dynamic parameter-driven Tableau Public dashboard with interactive filters for Region, Segment, and Category; engineered Level of Detail (LOD) expressions and custom profit margin calculations.
  • Key Finding: Tracked $733K+ in total sales and discovered that aggressive discounting on Tables in the Central region accounted for over 60% of total regional operating losses, providing the justification for discount limits.
  • Direct Project Links:
    🌐 Live Interactive Dashboard (Tableau Public) • 📊 Executive Dashboard PDF Summary • 👤 Tableau Public Profile
6. ⛽ BPCL Operations MIS & Dealer Credit Evaluation (Advanced Excel, VBA, SAP ECC)
  • Operational Reality: Daily monitoring of sales volumes, physical tank stock levels, and dealer credit exposure across 350+ petroleum retail outlets.
  • Methodology: Designed structured Excel reporting templates connecting SAP ECC ERP extracts, dynamic Pivot Tables, and automated VBA macros; assessed 2-year sales trends to calculate bank credit eligibility under the e-DFS facility for 50+ retail dealers.
  • Quantified Impact: Reduced daily MIS compilation from 2.5 hours down to 25 minutes (70% reporting time saved) and coordinated documentation and operational compliance for the commissioning of 37 new retail outlets.
7. 🚖 Rapido Ride-Sharing Analytics (15 Production-Grade SQL Queries)
  • Business Problem: Analyze driver allocation patterns, morning peak-hour ride cancellations, and customer retention drop-offs across urban pickup zones.
  • Methodology: Solved 15 distinct business scenarios using window functions (ROW_NUMBER, RANK), multi-condition aggregations, and distance-based user segmentation.
  • Direct Project Links:
    🔗 GitHub Repository • 📁 15 SQL Queries Directory
8. 👥 HR Workforce Analytics & Equity Analysis (9 Structured SQL Queries)
  • Business Problem: Analyze department-level turnover risks, managerial spans of control, and salary equity across regional offices.
  • Methodology: Wrote 9 structured queries utilizing correlated subqueries, window percentiles, and group-level benchmarking against company-wide averages.
  • Direct Project Links:
    🔗 GitHub Repository • 📁 9 SQL Queries Directory
9. 🏭 Deloitte Australia: Daikibo Factory IoT Telemetry & Downtime Analysis (IoT & Tableau)
10. 🎬 Netflix Content Strategy & Catalog Evolution (Python, EDA, Seaborn)
  • Business Problem: Examine catalog shifts between movies and television series over a 15-year period, international content growth, and director networks.
  • Methodology: Handled nested genre strings, performed temporal trend analysis, and visualized release patterns using Pandas, Matplotlib, and Seaborn.
  • Direct Project Links:
    🔗 GitHub Repository • 📓 Jupyter Notebook • 📊 Case Study PDF Report

📜 Verified Certifications & Credentials

  • 🏆 HackerRank SQL - 5-Star Gold Badge (View Verified Profile)
  • 🇦🇺 Data Analytics Job Simulation — Deloitte Australia (via Forage) (Certificate)
  • 🎓 SQL Skill Mastery Certification — Scaler Academy & InterviewBit
  • 📊 Data Analytics & Visualisation: Probability & Statistics — Scaler DSML
  • 🐍 Data Analytics & Visualisation: Python Libraries (Pandas, NumPy, Seaborn) — Scaler DSML
  • 📈 Tableau & Excel Specialization — Scaler DSML
  • 🎓 Bachelor of Engineering (B.E.) in Mechanical Engineering — VTU Belagavi (CGPA: 8.67)

🐍 GitHub Contribution Snake

GitHub Contribution Snake

💬 Analytical Mindset

"Numbers have an important story to tell. They rely on you to give them a clear and convincing voice."
— Stephen Few

Whether it's auditing daily sales across 350 petroleum stations, diagnosing regional logistics delays in BigQuery, or finding purchase drivers with conditional probability, I enjoy the craft of making data clean, clear, and actionable.

Designed & Maintained with curiosity by Shivaling Battarki • Open for Data & Business Analyst Roles

Pinned Loading

  1. Aerofit-Treadmill-Business-Case-Study Aerofit-Treadmill-Business-Case-Study Public

    Customer Segmentation & Business Analytics for Aerofit Fitness Equipment

    Jupyter Notebook 1

  2. HR-ANALYTICS-SQL-CASE-STUDY HR-ANALYTICS-SQL-CASE-STUDY Public

    SQL-based HR analytics case study analyzing workforce structure, compensation, and employee behavior using BigQuery.

    1

  3. RAPIDO-MINI-CASE-STUDY RAPIDO-MINI-CASE-STUDY Public

    SQL-based analytical case study on a Rapido ride-hailing dataset, covering user behavior, ride patterns, vehicle performance, and cohort analysis using BigQuery SQL.

    3

  4. Target-SQL-Business-Case-Study Target-SQL-Business-Case-Study Public

    Target Brazil E-Commerce SQL Business Case Study analyzing 99K+ orders across 27 states using Google BigQuery (CTEs, Window Functions, Logistics & Revenue Analysis).

    1

  5. Walmart-Purchase-Behaviour-Business-Case-Study Walmart-Purchase-Behaviour-Business-Case-Study Public

    Analyzing 550K+ Walmart retail transactions using Python, Central Limit Theorem (CLT), and Confidence Intervals (90%, 95%, 99%) to debunk demographic spending myths and optimize retail merchandising.

    Jupyter Notebook 1