Accenture

Deliver end-to-end analytics solutions for global clients, leveraging Power BI, SQL, Snowflake, Python, and GenAI to optimize Accounts Receivable (AR), Accounts Payable (AP), and Order-to-Cash (O2C) processes. Skilled in requirement gathering, stakeholder communication, and turning complex datasets into actionable insights.

Key Highlights:

AR Dashboard (Flagship Project):

Designed an executive summary tracking reserves (≥3.1%, rising to 3.2% in 6 months).

Reduced past-due % in a key region from 11.5% to 6.7% within six months.

Flagged high-risk accounts and identified billing process gaps despite a 76% rise in 90+ aging.

Enhanced forecasting accuracy through GenAI-powered anomaly detection and predictive modeling.

Dispute Management Dashboard: Tracked $2.69B+ across 57K+ cases, increasing resolution efficiency by 76% and improving visibility for leadership.

Automated financial reporting with Python, reducing manual effort and enabling near real-time insights.

Leveraged Snowflake for data modeling, query optimization, and seamless integration of large datasets into interactive dashboards.

Impact: Achieved a 42% reduction in past-due accounts, improved reserve accuracy, and enabled proactive financial decision-making through data-driven strategies.

Sr Decision Science Analyst

Unstop

Mentor

topmate.io

Data Analytics Mentor

Emergi Mentors PTY LTD

With over 5 years of industry experience across Energy, Retail, and Supply Chain domains, I’ve contributed to data-driven success stories at global companies like Schneider Electric and Polaris Inc. My technical expertise includes SQL, Power BI, Snowflake, Python, and Alteryx—skills I’ve applied to solve real-world business problems, improve operational efficiency, and deliver meaningful insights to stakeholders.

As a Mentor, my mission is to guide aspiring analysts through every stage of their career journey—from mastering tools to landing their first or next role.

What I Offer:

✅ Resume Review & Writing Services – Get professionally crafted resumes tailored to data roles, plus one-on-one reviews to showcase your strengths and align with recruiter expectations.

✅ LinkedIn Optimization & Personal Branding – Build a compelling online presence that attracts recruiters and showcases your data expertise.

✅ Targeted Job Application Strategy – Learn how to apply smart, not just hard, by focusing on roles that match your skill set and growth goals.

✅ Hands-On Training with Real-World Projects – Learn SQL, Power BI, and more through practical scenarios from real business cases.

✅ Industry-Ready Portfolio Development – Craft an impactful portfolio with dashboards, reports, and case studies that reflect your capabilities.

✅ Mock Interviews & Networking Support – Personalized mock interviews, recruiter connections, and strategies to confidently enter the job market.

✅ Live & Recorded Webinars – Sessions covering tools, technical concepts, stakeholder communication, and interview readiness to keep you ahead of the curve.

Whether you’re just starting or aiming for your next big leap, I’ll help you build the skills, strategy, and confidence to succeed in the data world.

Polaris Inc.

Collaborated with cross-functional teams to identify business challenges, formulate data-driven solutions, and develop or modify ad hoc queries using advanced data analytics tools. Provided actionable insights that addressed dynamic business needs and enabled informed decision-making.

➡️ Analyzed and optimized SQL queries within Snowflake to assess historical data trends and demand patterns, devising strategies that improved inventory management across APAC, resulting in optimized stock levels and reduced excess inventory.

➡️ Utilized Power BI dashboards to drive sales and marketing strategies, leading to a 15% increase in invoice revenue through data-driven decision-making and enhanced reporting.

➡️ Played a pivotal role in the HIVE transition of D365 from DAX 09, successfully migrating over 1 million records using Snowflake, ensuring seamless system optimization and reducing data retrieval times by 30%.

➡️ Developed and deployed Power BI dashboards to monitor and visualize key business metrics, track invoice statuses, approvals, and payment schedules, providing real-time insights for improved financial visibility and operational efficiency.

➡️ Achieved a 75% reduction in invoice processing time through automation expertise in Advanced Excel, integrated APIs, and process optimization techniques. Focused on data integration, KPI development, financial reporting, and workflow automation, ensuring data quality and integrity throughout.

➡️ Designed and automated Alteryx workflows for the Sales Pricing Analysis in the EMEA region, automating daily and weekly data updates using the Alteryx Scheduler and Alteryx Server, significantly enhancing the speed and accuracy of pricing analysis.

• Hands-on tech: Power BI, Snowflake,  Alteryx,Dynamic-D365,MS - Excel

Data Analyst

Schneider Electric

Collaborated with internal stakeholders to define key performance indicators related to energy use, and 
emissions, and support supply chain management operations.

➡️ Specialized in data visualization and reporting tools like Power BI, showcasing strong analytical skills and managing diverse data sources to deliver actionable insights in the energy and supply chain domains.
➡️ Conducted exploratory data analysis (EDA) and statistical analysis using Python and Excel, uncovering trends and patterns that supported data-driven decisions for energy optimization.

➡️ Managed databases using SQL Server, focusing on energy data within the Global Risk Analytics team and contributing to sustainability initiatives, aligning with corporate goals in the energy and sustainability domain.

➡️ Created and presented dynamic reports using Power BI to highlight energy consumption trends and pinpoint areas for improvement, leading to a 15% increase in client satisfaction.

➡️ Developed and maintained real-time dashboards in Power BI to track key performance indicators (KPIs) for energy usage, increasing reporting efficiency by 30%.

➡️ Collaborated with cross-functional teams to design and implement smart energy meter solutions, resulting in a 25% improvement in data accuracy and a comprehensive energy management strategy.

➡️ Managed and queried large datasets (over 2 million records) using SQL Server, ensuring data integrity, and conducted advanced Excel analysis to enhance decision-making, boosting operational efficiency by 20%.

➡️ Supported the deployment of smart energy meter initiatives with data-driven insights, leading to the installation of meters in 100 new locations and contributing to supply chain optimization.

➡️ Leveraged business intelligence tools to enhance reporting capabilities, streamline processes, and provide strategic insights in both energy management and the supply chain sector.

• Hands-on tech: Python, Tableau, SQL Server, MS - Excel, APIs

Celebal Technologies

• Collaborate with business teams to understand their data needs and ensure that the data 
transformation processes meet their requirements.
• Generated data visualization of hidden insights for business communication for the Retail and 
Marketing sector. 
• Create interactive dashboards, reports, and visualizations using Power  BI tools.
• Write complex SQL queries to clean, transform, and aggregate data.

Associate(Business Intelligence)

Senior Analyst @ Accenture | Helping Students & Beginners Break into Data Careers | 1:1 Coaching, Projects, & Career Roadmaps | 350+ placed | Open to collaborate| Youtuber

350+ 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐒𝐭𝐨𝐫𝐢𝐞𝐬 & 𝐂𝐨𝐮𝐧𝐭𝐢𝐧𝐠

My journey started exactly where you might be now…
In 2015, I graduated as a Mechanical Engineer and worked for 2 years as a Maintenance Engineer.

From 2017 to 2019, I pursued the UPSC journey, which sharpened my analytical and problem-solving mindset.

In 2019, I made a move into the world of Data Analytics, without a technical background, connections, or a clear roadmap.
Back then, I had no clear roadmap, just trial and error.

You don’t have to go through that alone.
Through years of mentoring and refining my process, I’ve now helped 2000+ students successfully break into data analytics careers, and counting.

What I Offer:
●	Personalized career roadmaps for non-technical professionals seeking analytics roles.
●	Resume revamps that highlight your transferable skills.
●	1:1 job search coaching with practical, buzzword-free advice.
●	Interview preparation tailored to analytics positions.
●	Community support from fellow career-changers.
●	Available for collaborations, workshops, speaking events, and career guidance.

Who Benefits From My Mentorship:
●	Career switchers uncertain about first steps into data.
●	Students feeling overlooked in the job market.
●	Working professionals seeking growth in analytics.
●	Anyone without a traditional tech background who wants to break into data.


Let's Connect:
●	Email: mk24sablani@gmail.com
●	For Australian students: https://emergimentors.com.au/dashboard

→ 𝐓𝐡𝐞 𝐬𝐞𝐜𝐫𝐞𝐭 𝐰𝐞𝐚𝐩𝐨𝐧𝐬 𝐞𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐧𝐞𝐞𝐝𝐬?
𝐏𝐲𝐭𝐡𝐨𝐧’𝐬 𝐏𝐚𝐧𝐝𝐚𝐬 & 𝐍𝐮𝐦𝐏𝐲.


What if I told you mastering just two Python libraries could transform your data analysis forever?

Every great data story starts with clean, structured data.
Pandas & NumPy unlock that power.

• 𝐏𝐚𝐧𝐝𝐚𝐬 → 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚 𝐏𝐨𝐰𝐞𝐫𝐡𝐨𝐮𝐬𝐞
Clean messy datasets into structured tables

Filter, group, and summarize with ease

Handle missing values like a pro

Merge multiple datasets seamlessly

• 𝐍𝐮𝐦𝐏𝐲: 𝐓𝐡𝐞 𝐛𝐚𝐜𝐤𝐛𝐨𝐧𝐞 𝐨𝐟 𝐧𝐮𝐦𝐞𝐫𝐢𝐜𝐚𝐥 𝐜𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠.
Lightning-fast operations on large datasets

Work effortlessly with arrays & matrices

Speed up calculations for advanced analysis

Why struggle with spreadsheets when these tools can automate and optimize your workflow?

𝐖𝐢𝐭𝐡 𝐏𝐚𝐧𝐝𝐚𝐬 𝐚𝐧𝐝 𝐍𝐮𝐦𝐏𝐲:
• Load data from CSV, Excel, SQL, and more.
• Use intuitive commands like .groupby(), .pivot_table(), .fillna().
• Utilize vectorized operations for lightning-fast performance.
• Prepare data for machine learning and visualization in minutes.

➡️Ready to land your dream Data Analyst & AI role? 

DM me for resume & LinkedIn optimization, hands-on portfolio building, targeted job strategies, mock interviews, and personalized mentorship - all designed to fast-track your data & AI career success

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Mukesh Sablani

Helping Students & Beginners Break into Data Careers | 1:1 Coaching, Interview Strategist, &Career Transition Specialist for Tech Professionals ,Freelance Data Analyst| 350+ placed | Open to collaborate| Youtuber

𝐒𝐐𝐋 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 

Lost in the maze of SQL commands?
You’re not alone.

The secret isn’t memorising endless syntax.
It’s mastering the core concepts that power every data-driven decision.

Here’s your 𝐒𝐐𝐋 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 for Data Analysts

• SQL Database: Your data’s home base. Know how to create, manage, and optimize databases.

• SQL Constraints: Enforce rules for data accuracy and integrity. Primary keys, unique, NOT NULL - these aren’t just buzzwords; they protect your data.

• SQL Operators: The building blocks. Learn logical AND, OR, IN, NOT to craft precise queries.

• SQL Views: Virtual tables that simplify complex data. They save time and reduce errors.

• SQL Tables: The real data holders. Understand their design to improve query performance.

• SQL Joins: The heart of relational data. Master INNER, LEFT, RIGHT, and FULL joins to connect your datasets seamlessly.

• SQL Functions: Aggregate, scalar, window functions – use them wisely for advanced analytics.

• SQL Clauses: WHERE, GROUP BY, HAVING – filter and group data efficiently.

• SQL Indexes: Speed up retrieval. Knowing when and how to index is a game changer.

• SQL Stored Procedures: Automate repetitive tasks and encapsulate logic for reuse.

You don’t need every SQL trick.
You need the right ones applied with precision.

➡️𝐑𝐞𝐚𝐝𝐲 𝐭𝐨 𝐥𝐚𝐧𝐝 𝐲𝐨𝐮𝐫 𝐝𝐫𝐞𝐚𝐦 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 & 𝐀𝐈 𝐫𝐨𝐥𝐞? 

𝐃𝐌 me for resume & LinkedIn optimization, hands-on portfolio building, targeted job strategies, mock interviews, and personalized mentorship - all designed to 𝐟𝐚𝐬𝐭-𝐭𝐫𝐚𝐜𝐤 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚 & 𝐀𝐈 𝐜𝐚𝐫𝐞𝐞𝐫 𝐬𝐮𝐜𝐜𝐞𝐬𝐬

Follow Mukesh Sablani for more insights on Data Analysis

→ 𝐓𝐡𝐞 𝐒𝐢𝐥𝐞𝐧𝐭 𝐁𝐚𝐭𝐭𝐥𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐆𝐢𝐚𝐧𝐭𝐬: 𝐏𝐚𝐧𝐝𝐚𝐬 𝐯𝐬 𝐏𝐲𝐒𝐩𝐚𝐫𝐤
• Ever wondered which data tool truly rules in today's data-driven world?
• Both Pandas and PySpark are powerful - but their strengths lie in different realms.
• Choosing the right one can make or break your data project.

→ 𝐏𝐚𝐧𝐝𝐚𝐬: 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭’𝐬 𝐒𝐰𝐢𝐬𝐬 𝐀𝐫𝐦𝐲 𝐊𝐧𝐢𝐟𝐞
• Perfect for small to medium datasets.
• Easy to learn with intuitive syntax.
• Great for quick data cleaning, analysis, and visualization.
• Runs on a single machine - making it fast but limited by memory.

→ 𝐏𝐲𝐒𝐩𝐚𝐫𝐤: 𝐓𝐡𝐞 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐝 𝐏𝐨𝐰𝐞𝐫𝐡𝐨𝐮𝐬𝐞
• Designed for big data and massive scalability.
• Executes across clusters in parallel - handles terabytes with ease.
• Has a steeper learning curve but rewards with speed on large datasets.
• Integrates deeply with Apache Spark ecosystem and cloud platforms.

→ 𝐓𝐡𝐞 𝐑𝐞𝐚𝐥 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧: 𝐖𝐡𝐚𝐭’𝐬 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐒𝐢𝐳𝐞 𝐚𝐧𝐝 𝐒𝐩𝐞𝐞𝐝 𝐍𝐞𝐞𝐝?
• If your dataset fits comfortably in memory and you want speed and simplicity → Pandas.
• If you're dealing with data that requires distributed computing and fault tolerance → PySpark.
• Sometimes, combining both is the smartest strategy.

Don't let tool choice slow down your data journey!

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→ 𝐌𝐚𝐬𝐭𝐞𝐫𝐢𝐧𝐠 𝐒𝐐𝐋: 𝐓𝐡𝐞 𝐂𝐨𝐫𝐞 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐘𝐨𝐮 𝐒𝐡𝐨𝐮𝐥𝐝 𝐊𝐧𝐨𝐰

SQL is the standard language for managing and manipulating relational databases efficiently. It helps perform queries, updates, and schema modifications seamlessly across different database systems.

→ 𝐊𝐞𝐲 𝐒𝐐𝐋 𝐂𝐨𝐧𝐜𝐞𝐩𝐭𝐬 𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐢𝐞𝐝

• SQL Command Types –
 DDL (CREATE, ALTER, DROP) defines schemas.
 DML (SELECT, INSERT, UPDATE, DELETE) manipulates data.
 DCL and TCL manage permissions and transactions.

• Keys –
 Primary Key uniquely identifies each record.
 Foreign Key connects tables and maintains referential integrity.

• Joins –
 Combine data from multiple tables. Common types: INNER, LEFT, RIGHT, FULL.

• WHERE vs HAVING –
 WHERE filters before grouping; HAVING filters after aggregation.

• DELETE vs TRUNCATE vs DROP –
 DELETE removes rows selectively, TRUNCATE clears all data, DROP removes the entire table.

• NULL Values –
 Represent missing data, different from zero or empty strings.

• DISTINCT –
 Retrieves unique records, removing duplicates.

• Subquery –
 A query within another query for complex filtering or selection.

• Aggregate Functions –
 COUNT, SUM, AVG, MIN, MAX summarize data, often with GROUP BY.

• ORDER BY –
 Sorts data in ascending or descending order.

• Indexing –
 Boosts data retrieval speed, especially in large datasets.

• CHAR vs VARCHAR –
 CHAR is fixed-length; VARCHAR is variable-length and storage-efficient.

• COUNT Function –
 SELECT COUNT(*) counts all rows in a table.

→ SQL is not just a language - it’s the foundation of every data-driven decision.
 Mastering these fundamentals is the first step toward becoming data fluent.

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 SQL interview Questions

→ 𝐓𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐀𝐈 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬

Data is the new currency, but its true value emerges only when it’s analyzed intelligently. That’s where AI transforms the game-turning raw information into powerful insights with speed, precision, and purpose.

• Automated Data Collection – AI captures data from multiple sources instantly, reducing manual effort and human error.
 • Data Cleaning and Integration – It refines messy datasets and merges them into a unified structure for accuracy.
 • Pattern Recognition – Machine learning models uncover hidden trends, relationships, and anomalies.
 • Natural Language Processing (NLP) – It interprets sentiments, topics, and text data, converting words into measurable insights.
 • Predictive Analytics – AI forecasts future trends and customer behavior to guide strategic decisions.
 • Customer Segmentation – It groups audiences by behavior, enabling personalized engagement and marketing.
 • Real-time Analysis – Businesses gain instant visibility, empowering faster, data-driven decisions.
 • Visualization and Reporting – AI auto-generates interactive dashboards and summary reports.
 • Decision Support – Smart systems recommend the best actions and alert teams to critical issues.

→ 𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭: faster insights, smarter strategies, and stronger business outcomes.

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→ 𝐓𝐡𝐞 𝐌𝐨𝐬𝐭 𝐀𝐬𝐤𝐞𝐝 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬: 𝐔𝐧𝐯𝐞𝐢𝐥𝐢𝐧𝐠 𝐖𝐡𝐚𝐭 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 𝐌𝐨𝐬𝐭

• What is data analysis? It’s the art of inspecting, cleaning, transforming, and modeling data to discover meaningful insights. Organizations rely on it to make smarter decisions and optimize strategies.

• Types of data matter. Qualitative data describes categories (like colors or gender). Quantitative data deals with numbers - either discrete counts or continuous measurements.

• Know your data formats. Structured data fits neatly into tables; unstructured data includes text, images, and videos, requiring specialized tools.

• Data cleaning is critical. Fix errors, handle missing data, remove duplicates, and format consistently to ensure reliable analysis.

• Master your tools: Excel for basics, SQL for querying databases, and Python libraries like pandas and matplotlib for advanced work.

• Missing data? Options include removal, imputation, or algorithms designed to handle gaps gracefully.

• Data analyst vs data scientist? Analysts clean and visualize data; scientists build predictive models with ML.

• Visualize wisely: bar charts, pie charts, line graphs, and scatter plots reveal different insights.

• Accuracy is king: validate, audit, and automate to maintain data integrity.

• Correlation ≠ causation: they measure relationships differently - always look deeper.

• Pivot tables simplify complex datasets by summarizing and exploring patterns dynamically.

• Outliers require careful handling - either investigate, transform, or use robust methods.

• SQL skills let you retrieve and manipulate data efficiently. Example: SELECT * FROM sales WHERE sale_date >= '2023-01-01';
• Descriptive stats like mean, median, and mode simplify data understanding and support further analysis.

Mastering these fundamentals can set you apart in data analysis interviews.

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Most Asked Interview questions for Data Analysis

𝐅𝐫𝐨𝐦 𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐦𝐞𝐧𝐭 𝐒𝐜𝐡𝐨𝐨𝐥 𝐭𝐨 𝐚 𝐆𝐥𝐨𝐛𝐚𝐥 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 : 𝐌𝐲 𝐒𝐭𝐨𝐫𝐲 𝐨𝐟 𝐒𝐞𝐥𝐟-𝐁𝐞𝐥𝐢𝐞𝐟

I studied in a small government school where dreams rarely went beyond finding a stable job.

English wasn't my first language. 
Opportunities were few. 
Confidence? Almost none.

But one thing changed everything - 𝐈 𝐝𝐞𝐜𝐢𝐝𝐞𝐝 𝐭𝐨 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐢𝐧 𝐦𝐲𝐬𝐞𝐥𝐟.

→ I stopped comparing my background to others. → I started focusing on small, consistent progress. → I learned to speak up, even when my voice shook.

Step by step, I built skills, confidence, and a new vision for myself.

Today, as a 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭, I work with global teams, turn raw data into actionable insights, and mentor others who once felt "not enough."

Self-belief turned my limits into launchpads.

Your background does not define your ceiling - your mindset does.

If you're reading this from a small town, a humble school, or a place where big dreams feel out of reach - 𝐤𝐞𝐞𝐩 𝐠𝐨𝐢𝐧𝐠.

The world is bigger than where you started.

What's one belief that held you back, and how did you overcome it?
Let's inspire each other below! 

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→ 𝐓𝐡𝐞 𝐏𝐲𝐒𝐩𝐚𝐫𝐤 𝐂𝐡𝐞𝐚𝐭𝐬𝐡𝐞𝐞𝐭: 𝐌𝐚𝐬𝐭𝐞𝐫 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐰𝐢𝐭𝐡 𝐄𝐚𝐬𝐞

Have you ever felt stuck juggling large datasets beyond the reach of your usual tools? The secret weapon could be PySpark.

PySpark is the Python API for Apache Spark. It’s designed for big data processing, distributing tasks seamlessly across computers. Think of it as pandas on steroids - built for speed, scale, and efficiency.

• 𝐑𝐞𝐚𝐝𝐢𝐧𝐠 & 𝐖𝐫𝐢𝐭𝐢𝐧𝐠 𝐃𝐚𝐭𝐚
- Load CSV, JSON (including multiline), Parquet, text
- Import data via JDBC connections

• 𝐃𝐚𝐭𝐚 𝐄𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐢𝐨𝐧
- Preview with show() and select rows without truncation
- Print schema, summary stats, and count rows
- Understand your data types easily

• 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠
- Drop null rows globally or selectively
- Fill null values with custom values
- Remove duplicates effortlessly

• 𝐅𝐢𝐥𝐭𝐞𝐫𝐢𝐧𝐠 & 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬
- Filter by numeric or string conditions
- Combine multiple filters
- Keep only non-null rows
- Categorize data dynamically

• 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧 & 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠
- Count, sum, average per group
- Group by multiple columns
- Use rollup and cube for hierarchical summaries

• 𝐒𝐨𝐫𝐭𝐢𝐧𝐠 & 𝐃𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐬
- Sort ascending/descending globally or per partition
- Remove duplicates and get unique rows
-  Limit returned rows smartly

• 𝐉𝐨𝐢𝐧𝐬 & 𝐌𝐞𝐫𝐠𝐞
- Inner, left, right, full outer joins
- Append data with union
- Union by matching columns

PySpark unlocks powerful data manipulation at scale, yet keeps your code intuitive.

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𝐈 𝐃𝐢𝐝𝐧'𝐭 𝐂𝐨𝐦𝐞 𝐟𝐫𝐨𝐦 𝐚 𝐓𝐞𝐜𝐡 𝐁𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝 - 𝐁𝐮𝐭 𝐈'𝐦 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐓𝐞𝐜𝐡 𝐅𝐮𝐭𝐮𝐫𝐞

A few years ago, I couldn't have imagined myself in tech. 
No computer science degree. 
No coding bootcamp. 
Just curiosity and a willingness to learn.

Here's what I've discovered along the way:

You don't need to know everything to start. 
I began with small steps - watching tutorials, building tiny projects, breaking things (a lot), and Googling my way through errors. Each small win built my confidence.
Your "non-tech" background is actually your strength. 

My previous experience taught me problem-solving, communication, and understanding what users actually need. 

These skills matter just as much as technical knowledge.

The tech community is more welcoming than you think. 

I was nervous about asking "basic" questions, but I found that most people remember being beginners too. They're genuinely happy to help.

Imposter syndrome doesn't disappear - you just get better at working through it. 

Some days I still feel like I'm figuring things out. 
But that's the whole point. 
Tech is always evolving, and we're all learning together.

To anyone considering a move into tech: your background isn't a barrier.
It's what makes your perspective valuable.

The tech industry needs diverse thinkers, career changers, and people who bring fresh eyes to old problems.

You belong here too.

What's one thing you wish you knew before starting your tech journey? Drop it in the comments - let's help the next wave of career changers.

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