This repository showcases the certifications I have earned in Data Analytics, SQL, and Programming.
Each certification highlights my commitment to continuous learning and developing strong analytical skills.
Issued by: IBM (via Coursera)
Year: 2025
Credential: View Certificate
A comprehensive program covering data analysis fundamentals using Python, SQL, Excel, and Power BI.
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Python for Data Analysis (Pandas, NumPy, Matplotlib, Seaborn)
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SQL for querying and optimizing databases
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Excel for advanced analytics & reporting
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Data Visualization with Power BI and Python
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Data Wrangling, Cleaning, and Transformation
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Exploratory Data Analysis and Statistical Techniques
✅Exploratory Data Analysis on real-world datasets
✅Interactive dashboards in Power BI and Excel
✅SQL queries for business insights
✅Data storytelling reports
Issued by: HackerRank
Year: 2025
Credential: View Certificate
Validates strong proficiency in solving SQL problems, ranging from simple queries to complex analytics.
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Joins (INNER, OUTER, CROSS, SELF)
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Subqueries and Nested Queries
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Window Functions (RANK, DENSE_RANK, ROW_NUMBER)
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Aggregation & Grouping (GROUP BY, HAVING, CUBE, ROLLUP)
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Query Optimization Techniques
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Working with Dates, Strings, and Case Expressions
✅150+ SQL challenges solved (Easy → Hard)
✅Real-world datasets from e-commerce, banking, and HR domains
✅Wrote optimized queries for insights
Issued by: Udemy
Year: 2025
Credential: View Certificate
Power BI DAX Mastery: Advanced Formulas and Data Analysis is an in-depth course focused on mastering Data Analysis Expressions (DAX) to perform advanced data modeling and analytics in Power BI. The course covers practical business scenarios that teach how to design robust data models, create dynamic measures, and extract actionable insights using advanced DAX functions. It bridges the gap between intermediate and expert-level Power BI users by emphasizing real-world data problem-solving and performance optimization.
-Advanced DAX Formulas: Proficiency in creating complex measures and calculated columns using functions like CALCULATE(), FILTER(), RANKX(), and DATEADD().
-Data Modeling Expertise: Ability to design optimized star schemas, manage relationships, and handle many-to-many relationships.
-Time Intelligence Analysis: Mastery of YoY, MoM, rolling averages, and cumulative totals for business trend tracking.
-Dynamic KPI Creation: Building metrics such as Customer Lifetime Value (CLV), Customer Acquisition Cost (CAC), and Promotion Effectiveness.
-Performance Optimization: Improving DAX efficiency through context understanding and query tuning.
-Analytical Problem Solving: Applying DAX to scenarios like inventory analysis, cohort tracking, and basket analysis.
Completing this course significantly deepened my understanding of Power BI’s analytical layer — particularly how DAX operates within row, filter, and evaluation contexts. I can now:
✅Build smarter, more dynamic dashboards that adapt to user selections.
✅Create advanced business metrics directly in Power BI without relying solely on SQL.
✅Perform complex time-based and scenario analyses, enhancing my data storytelling ability.
✅Apply best practices in data modeling for faster, cleaner, and more scalable reports.
✅This certification strengthened my ability to deliver data-driven insights and improved my confidence in solving real-world analytics challenges using DAX.
Issued by: Forage
Year: 2025
Credential: View Certificate
The Tata Data Analytics Job Simulation is a self-paced virtual experience in which participants step into the role of a data analyst at Tata Group. You work through realistic business scenarios — exploring data, identifying insights, and making strategic recommendations — to help the company turn raw data into actionable business decisions.
-Data exploration and cleaning: inspecting, transforming, and preparing data for analysis.
-Business-driven analytics: identifying key questions and framing insights in a business context.
-Use of analytics tools and techniques: performing quantitative analysis, visualizing results, and communicating findings to stakeholders.
-Insight communication: translating data insights into recommendations for leadership (CEOs/CMOs) and using storytelling to drive decisions.
-Exposure to enterprise analytics culture: working through tasks representative of what Tata Group data professionals do.
✅Completing this simulation enhanced my analytical toolkit by reinforcing how to think like a business data analyst — not just crunch numbers, but ask the right questions, analyse data with purpose, and communicate clearly.
✅Asking strategic business questions before diving into data.
✅Structuring analyses that deliver actionable insights rather than just descriptive reports.
✅Visualising and presenting data in a way that resonates with decision-makers.
✅Operating under a real-world corporate style workflow — from data preparation to stakeholder presentation.
✅This experience strengthened both my technical analysis skills and my ability to deliver insights that drive business impact.
Issued by: Oracle
Year: 2025
Credential: View Certificate
The Oracle Cloud Infrastructure (OCI) AI Foundations course is a beginner-friendly, self-paced program designed to introduce learners to the fundamentals of artificial intelligence (AI), machine learning (ML), deep learning, and generative AI — with a special focus on how these technologies are applied within the Oracle Cloud environment. It covers core AI concepts, OCI’s AI stack and services (vision, language, anomaly detection, forecasting, large language models), and the architecture required for AI workloads on the cloud.
-Understanding of AI, ML and Deep Learning fundamentals (supervised vs unsupervised, neural networks, etc.)
-Knowledge of how to use OCI’s AI-first services (such as Vision, Language, Speech, Anomaly Detection, Forecasting) and working with large language models (LLMs) in a cloud environment.
-Familiarity with cloud architecture for AI workloads: compute, storage, networking, data science services, AI deployment & monitoring on OCI.
-Awareness of responsible AI practices (governance, explainability, bias mitigation) and performance/cost optimization in AI deployments.
✅Completing this course significantly enhanced my ability to think and act as an AI-enabled cloud practitioner.
✅Confidently interpret and apply AI/ML concepts in real business and cloud contexts — not just theoretically.
✅Leverage cloud-native AI services (OCI) to design and implement intelligent solutions such as image/text classification, anomaly detection, forecasting, and generative tasks.
✅Understand the full lifecycle of AI solutions on the cloud: from data ingestion/storage, model building/deployment, to monitoring & governance — giving me an end-to-end view.
✅Integrate AI-driven insights into analytics and decision-making workflows (which complements my previous experience in data analytics and DAX modelling).
✅Advocate for and apply responsible, optimized AI designs — considering cost, performance and ethics.
This repository will be continuously updated as I complete new certifications and achievements.