LinGrow

Founder

Manuel Veronesi

No Code Solutions SRL

Innovation Consultant

Be Team

Unified Communications Specialist with a strong technical foundation in network infrastructures & Linux systems. Skilled in implementing and optimizing 3CX solutions to streamline business communications and enhance productivity.

UC & Network Specialist

Sonitus S.r.l

I design and implement AI-driven solutions to enhance customer engagement for Italy's automotive and wine & food sectors. My expertise includes IT/ICT infrastructure, project management, and digital signage optimization. Collaborated with renowned brands like Hyundai and Eataly.

AI & ICT Digital Strategist

Alisystems srl

I solve IT, security, & communication challenges for high-profile brands through strategic infrastructure management (networks, UC, phone systems), and project leadership.

Pegasus: IT Value Acceleration Services

Professional Services - VoIP & UC Technical Account Manager for Italy, EU and UK.

Portfolio:

APTOS RETAIL (Milan, Italy) - Ring Central online training sessions to users

MSX International Holding Italia srl (Rome, Italy) - Ring Central go-live onsite support & professional services

GUESS? Europe Sagl (Boggio, Lugano, Switzerland) - Ring Central go-live deployment and user’s training 

NEWS UK & Ireland Ltd (The Times & The Sunday Times) (The News Building, London Bridge street, London, UK) - Ring Central go-live assistance for accounts activation and user’s support

Avery Dennison (Cadorago, Italy) - Ring Central Survey on Cadorago Plant

Global Switch (Amsterdam, Netherlands)
Ring Central go-live onsite support & professional services

CP Kelco (Køge, Denmark)
Ring Central go-live onsite support & professional services

UC Engineer | Italy

aylook

Keynote Speaker about IT Networks, IP protocol and Cybersecurity at Aylook EBI events dedicated to video surveillance integrators who want to certify and train themselves to advance in their career.

IT Networks and Cybersecurity Keynote Speaker

Xenialab 

Professional Services for international customers and partners about Xcally (Contact Center multichannel software solution)
Previously HelpDesk for worldwide customers and partners. 
Email Tickets management and remote support 
Products Test and troubleshooting. 
Technical documentation revision and drafting

---
Ended unceremoniously

Professional Services Consultant

retilocali srl

In my role as Founder and CEO of retilocali, I led a company deeply committed to technological innovation in the field of IT and telecommunications. We specialized in a wide array of services, from copper UTP/FTP and fiber optics cabling to advanced networking and telecommunication systems. My responsibilities extended to overseeing the planning, development, testing, and troubleshooting across various domains, including contact center software solutions, wireless technologies, and comprehensive security systems like video surveillance and anti-burglar alarms. As a technical manager and IT business consultant, I guided our team to deliver solutions that combined technical excellence with practical business insights, fostering a culture of quality and client satisfaction.

Founder & CEO

I Help CEOs & Founders Deliver 5-Star Customer Experiences with AI Chatbots & Voice Responders — Bridging the Gap between Remote Teams & Global Customers in Italy, the EU, the US, and Brazil

If you're a solopreneur, coach, CEO, or Founder in the tech or marketing world who's...

→ Struggling to get noticed on LinkedIn
→ Finding AI content tools overwhelming
→ Frustrated with technology that's slowing you down

...you're in the right place.

I'll share a quick story with you. 

A few years ago, I became friends with one of the most successful people on LinkedIn— 

He showed me how powerful the right content can be on this platform.

I realized that my tech skills — combined with this new knowledge — could help others achieve amazing results. 

So I dedicated myself to becoming an expert in both using AI to create content and building a strong presence on LinkedIn.

Now, I empower leaders like you to turn their ideas into content that gets attention — and drives business.

Using the latest AI tools and proven LinkedIn strategies, I create content that gets you noticed, builds your reputation, and helps you turn your online presence into real results.

My background is in technology — I started out working with network cables and phone systems. 

Over the years, I've helped businesses worldwide optimize their technology, especially phone systems. 

For local businesses in Italy, I've set up everything from internet connections to entire IT systems.

My deep understanding of technology — especially in areas like IT, cybersecurity, and communication systems — allows me to help businesses like yours stand out on LinkedIn.

Here's how I can help you:

→ Use AI tools to create content that resonates with your audience.
→ Create effective posts that build your reputation and connections.
→ Set up phone and communication systems that meet your business needs.
→ Leverage no-code tools to enhance team effectiveness and business growth.

I'm a native Italian speaker and fluent in English and Portuguese — and I approach every project with care, creativity, and a focus on getting things done right.

If you're ready to:

→ Use AI to reach your goals faster
→ Fix communication system problems
→ Elevate your content and turn your online presence into a profit
→ Discover how no-code solutions can empower your team and drive innovation

Send me a message with "Ciao Manuel!" — and let's talk!

Eli Lilly and Company

Driving product strategy across HCP & Patient analytics, focused on scalable design, faster turnaround, and adoption across brands and teams.

Analytical Products for HCP Analytics
Built products to generate HCP lists critical to Dynamic Targeting and promotional initiatives—standardizing processes, adding analyst-friendly functionality, and delivering meaningful time savings for downstream teams.

Product Strategy & Lifecycle (SAFe Agile)
Owned the end-to-end lifecycle: discovery, prioritization, iteration, and delivery. Ran sprints, retros, and roadmaps in Jira/GitHub, translating requirements into automated outputs and measurable product increments.

User-Centric Design & Adoption
Co-developed with end users through continuous touchpoints and feedback loops to address pain points, improve usability, and drive sustained adoption.

Cross-Functional Alignment & Scale
Partnered with medical, commercial, engineering, and analytics stakeholders to align on needs, standardize approaches, and scale capabilities as business priorities evolved.

Tools, Automation & Scalability
Built user interfaces with Python, Flask, HTML/JavaScript, Dash, HTMX. Curbed turnaround time using generative AI, modular/standardized frameworks, and parameterized components for long-term reuse. Participated in Databricks/GenAI exploration and shared use cases across BUs.

Associate Consultant - BI&A POP Product

Drove strategic analytics delivery across Immunology brands (Taltz, Olumiant, Ebglyss, Omvoh) with a focus on targeting, dynamic strategy enablement, and process innovation.

HCP & HCO Targeting Leadership
Led multi-semester targeting for launch and in-line brands, guiding 3+ team members in planning, execution, and stakeholder collaboration. Standardized rules and workflows, cutting delivery time from 8 to 4 weeks.

Dynamic Targeting – Impact Measurement & Implementation
Directed a high-visibility study on opportunity HCP targeting’s effect on share growth, involved building methodology design & identifying performance KPIs. Partnered with multiple teams to implement dynamic targeting streams for launch brands by building scalable rule frameworks that enabled integration into field execution and drove a 4% revenue uplift for Immunology.

Patient Journey & Global Leakage Insights
Built Power BI OTGS dashboards to track patient journeys, leakage trends, and key drivers. Delivered US launch brand insights and partnered with International BU to provide trends across 10+ geographies, helping teams reduce drop-offs and improve retention.

Tools, Automation & Optimisation
Leveraged Tableau, Power BI, and SQL across analytics delivery. Automated forecasting reports by redesigning backend SQL and adding parameterized stored procedures, cutting turnaround from 2 weeks to 1 and stabilizing large-scale refreshes.

Standardization & Mentorship
Onboarded and mentored 3 new members across NBA, targeting, and source-of-business projects, upskilling them in Python, Tableau, and Power BI.

Associate Consultant - BI&A Immunology

Delivered analytics solutions for HCP/HCO targeting, market performance tracking, and launch brand enablement across Immunology brands (Taltz, Olumiant, Ebglyss, Omvoh). Partnered with cross-functional teams to turn data into actionable strategies, improve processes, and build team capabilities.

Strategic Analytics & Market Insights
Leveraged IQVIA LAAD & OneKey datasets to lead market tracking and competitive analysis, including GI Market Landscape and NILEX JIA studies — combining patient journey mapping, competitive share analysis, and prescribing trends to guide investment priorities and market positioning.

Evolved HCP Targeting
Led targeting for launch and in-line brands. Built a Tableau-based dynamic targeting tool for segmentation, cutting manual workflows, reducing list refresh time, and improving communication — recognized at Quarterly Townhalls for innovation and impact.

Innovation & Process Improvement
Standardized analytical methodologies and tools across 4+ therapeutic teams, saving 1,000+ hours over 18 months. Contributed to enable Dynamic Targeting by engineering new metrics and HCP universes for ML model development.

Launch Excellence & Campaign Optimization
Led the Omvoh HCO Profiling Study, integrating IQVIA OneKey data with desk research to segment accounts — awarded 2023 BI&A Distinguished Insight Award. Owned the Next Best Action (NBA) program for launch brands, creating scalable rule frameworks and KPIs to ensure timely execution.

Collaboration & Capability Building
Developed and maintained Tableau dashboards for savings card, payer performance, specialty referrals, and source-of-business tracking. Upskilled peers on GitHub collaboration and version control

Associate - BI&A (Business Insights & Analytics)

As part of US Immunology under HCP Analytics unit - delivered analytics solutions for high-priority brands including Taltz and Olumiant, with a focus on Next Best Action (NBA) initiatives.

- Designed and developed high-value dashboards in Tableau for Savings Card Utilization Tracking and Payer-Wise Performance Monitoring, enabling faster, data-driven brand performance reviews.

- Automated report refresh processes, reducing refresh time from 3 days to 1 day and freeing up analyst bandwidth for strategic analysis.

- Re-engineered backend workflows for the Source of Business project, cutting data refresh time from 1 day to 1–2 hours and reducing database storage use by ~95% (36 intermediate tables reduced to 2).

- Leveraged tools such as Postgres SQL, Python, Excel 7 Tableau; gained exposure to cloud-based analytics environments.

- Recognized for driving efficiency improvements and delivering actionable insights in partnership with cross-functional US commercial teams.

Intern - Commercial Analytics

iNeuron.ai

Developed an end-to-end machine learning solution to predict potential backorders for e-commerce entities—enabling optimized logistics, reduced bad-debt risk, and improved customer satisfaction.

- Built predictive models in Python, deployed via Flask, with front-end in HTML/CSS.

- Utilized AWS SageMaker and EC2 for model training and hosting; deployed final solution on Heroku for easy access.

- Optimized for reusability, compatibility, and reduced latency, with comprehensive project documentation for transparency.

- Delivered a fully functional UI + backend pipeline, integrating analytics into actionable operational workflows.

Data Science (Machine Learning) Project Intern

Kantar

Gained hands-on exposure to market research methodologies, survey operations, and data quality checks.

- Conducted on-field interviews for client projects in credit cards and insurance.

- Led an academic research project, “Consumer Behavior in Selecting Subscription-Based Video Streaming Platforms (Chennai)”, applying SPSS techniques including Factor Analysis, Chi-Square, and One-way ANOVA to identify key decision drivers for platforms like Netflix and Amazon Prime.

Freelance Market Research - Internship Trainee

Timelinks

Supported data-driven decision-making for a leading Titan Company Limited retail franchisee (brands: Tanishq, Titan Eye Plus, Helios).

- Performed data cleaning, preparation, and reporting to track customer purchase patterns, enabling targeted tele-calling lists and personalized offers.

- Analyzed sales data to identify opportunities for cross-sell, up-sell, and customer retention.

- Maintained customer and sales data across multiple stores using Microsoft Excel; conducted initial visualization work in Tableau.

Data Analyst

I Help Freshers & Professionals from Non-IT & Engineering backgrounds break into the Data Analytics | Podcaster - The Starlight Podium | BI&A Associate @ Eli Lilly and Company

With nearly 4 years in healthcare and pharmaceutical analytics, I design data-driven strategies, scalable products, and actionable insights that drive business impact. As an Associate Consultant at Eli Lilly in Product Analytics, I deliver HCP and patient analytics solutions that boost efficiency and deliver measurable outcomes across multiple brands.

My career has spanned HCP/HCO targeting, next-best-action (NBA) strategy, patient journey and leakage analysis, global market insights, and analytics product development. I combine technical expertise with product thinking and cross-functional collaboration to create solutions that are both impactful and scalable.

Highlights of my journey include:

- Led and evolved multi-semester HCP targeting and NBA programs for launch and in-line brands, cutting delivery timelines by 50% and enabling consistent stakeholder alignment.
- Designed and implemented dynamic targeting frameworks that drove a 4% revenue uplift for immunology brands.
- Built and deployed analytical products in Python, Flask, SQL, and Tableau/Power BI, standardizing processes and saving 1,000+ hours across teams.
- Developed OTGS dashboards tracking patient journeys and leakage trends across US and 10+ global markets, guiding retention strategies and reducing drop-offs.
- Mentored and upskilled peers on analytics tools and methodologies, embedding best practices across teams.

Currently pursuing a Master’s in Data Science (AI/ML specialization) from Deakin University, with the goal of transitioning into a data science role by mid-2026 and applying advanced machine learning to solve high-impact problems in healthcare.

My professional philosophy centers on building scalable solutions that merge analytics with business strategy, enabling teams to make faster, clearer, and more confident decisions.

Varun Sagar Theegala

When start working with real-world data, you quickly learn that single models rarely perform robustly on their own as the real data is messy - This is where 𝗥𝗮𝗻𝗱𝗼𝗺 𝗙𝗼𝗿𝗲𝘀𝘁 stands out.

It’s one of the few algorithms that consistently delivers strong, stable performance across industries — finance, healthcare, retail, telecom, risk modeling, and more.

And the reason is simple: Random Forest is built on the fundamentals of Decision Trees but overcomes their biggest limitations.

 • Decision Trees are intuitive but unstable.
 • A small change in data can alter the entire tree.
 • They tend to overfit unless heavily regularized.

Random Forest solves this by building many diverse trees and letting them vote.

𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁:
A model that is 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁, 𝗹𝗲𝘀𝘀 𝗯𝗶𝗮𝘀𝗲𝗱, 𝗺𝗼𝗿𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝗹𝗶𝘇𝗮𝗯𝗹𝗲, and surprisingly easy to deploy in production.

Most advanced models used today — including XGBoost, CatBoost, and LightGBM — expand on principles first introduced through 

Random Forest: 𝗯𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽 𝘀𝗮𝗺𝗽𝗹𝗶𝗻𝗴, 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗿𝗮𝗻𝗱𝗼𝗺𝗻𝗲𝘀𝘀, 𝗮𝗻𝗱 𝗲𝗻𝘀𝗲𝗺𝗯𝗹𝗲 𝗮𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻.

Understanding Random Forest is not just a chapter in ML… It’s the foundation for understanding how modern ML systems achieve stability at scale.

To help learners build that foundation clearly, I’ve created a structured guide that walks through both Decision Trees and Random Forests—from intuition to practical application.

𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗴𝘂𝗶𝗱𝗲 𝗵𝗲𝗹𝗽𝘀 𝘆𝗼𝘂 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱:

• Why single trees struggle — and how ensembles fix it
• A clear, thorough recap of Decision Trees and the logic behind splitting
• How Random Forest actually builds and aggregates its trees
• What bootstrap sampling and feature randomness really mean
• Key parameters that influence model performance and stability
• Bias–variance intuition behind ensemble modeling
• Strengths, limitations, and business applications
• 25 essential Random Forest interview questions

🎯 𝗧𝗵𝗲 𝗴𝗼𝗮𝗹:
A practical, easy-to-reference resource that helps you understand, visualize, and apply Random Forest while grounding everything in strong Decision Tree fundamentals.

♻️ If you find this helpful, a like or share would be appreciated.

I'm Varun Sagar Theegala - follow along as I share my daily learnings, reflections, and experiences from my 3 years+ journey in analytics, data science, and AI.

Tagging individuals who help many learners with Data Science/ML upskilling & preparation. Hope this adds value to your community! - Venkata Naga Sai Kumar Bysani Tajamul Khan Andres Vourakis Timur Bikmukhametov, PhD Karun Thankachan Nirmal Budhathoki

#DataScience #MachineLearning #DecisionTree #RandomForest #Analytics #InterviewPreparation #MLAlgorithms

ML Overview - Random Forest

Every Aspiring Data Scientist Should Understand It Early - 
Most ML models don’t fail because they’re poorly built.
They fail because they’re poorly deployed.

A model that works beautifully in a Jupyter notebook often collapses the moment it enters production — not because the math is wrong, but because the environment around it isn’t ready.

Here are a few reasons why this happens:

𝗗𝗮𝘁𝗮 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗹𝗼𝗼𝗸 𝗹𝗶𝗸𝗲 𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗱𝗮𝘁𝗮
(drift, missing fields, new patterns)

𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗶𝘀𝗻’𝘁 𝗯𝘂𝗶𝗹𝘁 𝗳𝗼𝗿 𝗠𝗟 𝘄𝗼𝗿𝗸𝗹𝗼𝗮𝗱𝘀
(GPU shortages, slow pipelines, inconsistent environments)

𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝗿𝗲𝗻’𝘁 𝗺𝗼𝗻𝗶𝘁𝗼𝗿𝗲𝗱 𝗮𝗳𝘁𝗲𝗿 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁
(no alerts when performance drops or inputs change)

𝗧𝗲𝗮𝗺𝘀 𝘄𝗼𝗿𝗸 𝗶𝗻 𝘀𝗶𝗹𝗼𝘀
Data scientists build models → engineers deploy them → no shared workflow

For beginners, this often comes as a surprise:
doing the modelling is only ~20% of the ML lifecycle.
The remaining 80% is everything around it.

𝗧𝗵𝗶𝘀 𝗶𝘀 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝘄𝗵𝗲𝗿𝗲 𝗠𝗟𝗢𝗽𝘀 𝗰𝗼𝗺𝗲𝘀 𝗶𝗻.

MLOps brings structure to the messy reality of real-world machine learning.
It helps teams:

 • version data and models
 • automate training & deployment
 • manage compute efficiently
 • monitor models continuously
 • retrain when data changes
 • collaborate without friction

In simple terms:
If Machine Learning is building the model, MLOps is everything needed to keep that model alive, stable, and useful.

If you want a deeper understanding of the full MLOps lifecycle, workflows, and best practices, this guide is a great starting point.

It breaks down the concepts in a clear, structured way suitable for both beginners and working professionals.

If you found this helpful, do like or repost ♻️

I'm Varun Sagar Theegala - follow along as I share my daily learnings, reflections, and experiences from my 3 years+ journey in analytics, data science, and AI.

Tagging individuals who help many learners with Data Science/ML upskilling & preparation. Hope this adds value to your community! - Venkata Naga Sai Kumar Bysani Tajamul Khan Andres Vourakis Timur Bikmukhametov, PhD Karun Thankachan Nirmal Budhathoki

Guide To MLOps

Following my last post on 𝘄𝗵𝘆 𝗟𝗶𝗻𝗲𝗮𝗿 𝗥𝗲𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗮𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝗳𝗼𝗿 𝗶𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, I wanted to extend the conversation to models where those assumptions don’t hold — but explainability is still critical.

Because in real projects, you can’t trade off accuracy for interpretability.

That’s where 𝗦𝗛𝗔𝗣 𝘃𝗮𝗹𝘂𝗲𝘀 become invaluable.

In a recent ML project, I needed both:

 • Higher accuracy (so I moved to Random Forest / XGBoost)
 • Clear explanations for stakeholders (why a prediction changed)

Traditional `feature_importance()` only ranks features.
But business users need 𝗱𝗶𝗿𝗲𝗰𝘁𝗶𝗼𝗻 + 𝗺𝗮𝗴𝗻𝗶𝘁𝘂𝗱𝗲 — the “how” behind every prediction.

𝗦𝗛𝗔𝗣 (𝗦𝗵𝗮𝗽𝗹𝗲𝘆 𝗔𝗱𝗱𝗶𝘁𝗶𝘃𝗲 𝗘𝘅𝗽𝗹𝗮𝗻𝗮𝘁𝗶𝗼𝗻𝘀) breaks down each prediction to show:

* How much each feature contributed
* Whether it pushed the prediction up or down
* Insights at both global and individual levels
* Consistent explanations for complex models

In short: 𝗮𝗰𝗰𝘂𝗿𝗮𝗰𝘆 + 𝘁𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆, together.

What my guide covers

• What SHAP is (and isn’t)
• Base value concept
• Why SHAP > feature_importance
• Step-by-step Jupyter demo
• Summary, dependence, and decision plots
• When SHAP is most useful in real-world ML

Model interpretability shouldn’t be an afterthought.
SHAP helps you explain advanced models with the same clarity as simpler ones.

If you found this helpful, do like or repost ♻️

I'm Varun Sagar Theegala - follow along as I share my daily learnings, reflections, and experiences from my 3 years+ journey in analytics, data science, and AI.

Tagging individuals who help many learners with Data Science/ML upskilling & preparation. Hope this adds value to your community! - Venkata Naga Sai Kumar Bysani Tajamul Khan Andres Vourakis Timur Bikmukhametov, PhD Karun Thankachan Nirmal Budhathoki

#DataScience #MachineLearning #ModelInterpretation #InterviewPreparation #MLAlgorithms #SHAP

SHAP Values - Model Interpretability

Your Linear Regression model is only as good as the assumptions behind it.

Linear Regression looks simple on the surface - straightforward coefficients, clean equations, easy explanations.

But the moment its assumptions break, your interpretation breaks with it.

Here’s what every analyst and data scientist should keep in mind:

𝗪𝗵𝘆 𝗮𝘀𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻𝘀 𝗺𝗮𝘁𝘁𝗲𝗿

If assumptions fail, you may:

 • Misread coefficients
 • Trust misleading confidence intervals
 • Mark variables as significant when they aren’t
 • Miss underlying structure in your data
 • Build a model that looks perfect on paper but performs poorly in reality

Interpretability isn’t about explaining outputs.
It’s about ensuring those outputs 𝗱𝗲𝘀𝗲𝗿𝘃𝗲 𝘁𝗼 𝗯𝗲 𝗲𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱.

Leaders rely on regression models to:

 • Understand what drives outcomes
 • Allocate budgets
 • Prioritise product features
 • Set performance targets
 • Make directional bets

If the underlying assumptions are broken, every decision above becomes weaker.

Strong interpretability starts with strong foundations.

Validate your assumptions. 
Re-check them as data evolves. 
Never rely on intuition alone.

Better assumptions → Better models → Better decisions.

♻️ Do like & repost you find this helpful.

I'm Varun Sagar Theegala - follow along as I share my daily learnings, reflections, and experiences from my 3 years+ journey in analytics, data science, and AI.

Tagging individuals who help many learners with Data Science/ML upskilling & preparation. Hope this adds value to your community! - Venkata Naga Sai Kumar Bysani Tajamul Khan Andres Vourakis Timur Bikmukhametov, PhD Karun Thankachan Nirmal Budhathoki

#DataScience #MachineLearning #LinearRegression #InterviewPreparation #MLAlgorithms #ModelInterpretability

Model Interpretability - Linear Regression

If you’re learning Python for Data Analytics & Data Science, NumPy is one of the first packages you should build confidence in.


It’s the numerical engine behind almost everything we do and it’s also the foundation on which 𝗣𝗮𝗻𝗱𝗮𝘀 is built.

Whether you’re manipulating DataFrames, running machine learning models, or preparing datasets — you're essentially working with NumPy under the hood.

Here’s why strengthening NumPy skills pays off immediately in analytics and DS:


𝟭. 𝗡𝘂𝗺𝗣𝘆 𝗺𝗮𝗸𝗲𝘀 𝗣𝗮𝗻𝗱𝗮𝘀 𝗳𝗲𝗲𝗹 𝗶𝗻𝘁𝘂𝗶𝘁𝗶𝘃𝗲

Pandas Series & DataFrames are built on top of NumPy arrays.
Concepts like vectorization, indexing, slicing, boolean masks, broadcasting — all originate in NumPy and directly influence how Pandas behaves.

If you understand how `ndarray` works, Pandas suddenly becomes far more predictable.

𝟮. 𝗙𝗮𝘀𝘁 𝗻𝘂𝗺𝗲𝗿𝗶𝗰𝗮𝗹 𝗰𝗼𝗺𝗽𝘂𝘁𝗮𝘁𝗶𝗼𝗻

Vectorized math, comparisons, and aggregations run significantly faster than native Python loops.

Your Pandas operations (`df.sum()`, `df.mean()`, `df.apply()`) rely on this speed behind the scenes.

𝟯. 𝗖𝗹𝗲𝗮𝗻 & 𝗳𝗹𝗲𝘅𝗶𝗯𝗹𝗲 𝗱𝗮𝘁𝗮 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻

Slicing, boolean filtering, stacking, reshaping — skills every analyst uses daily.
These operations map directly to Pandas workflows:

* NumPy: `arr[arr > 10]`
* Pandas: `df[df["col"] > 10]`

𝟰. 𝗥𝗲𝗹𝗶𝗮𝗯𝗹𝗲 𝗱𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝘃𝗲 𝘀𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀

NumPy’s statistical functions power much of Pandas’ `.describe()` and statistical methods.

𝟱. 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗠𝗟 & 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴

Scikit-learn expects NumPy arrays. TensorFlow/PyTorch operate on NumPy-like tensors. Almost every model pipeline begins with:

X = df.values

Strong NumPy → smoother transition to ML model training.

𝟲. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘂𝘁𝗶𝗹𝗶𝘁𝗶𝗲𝘀 𝗳𝗼𝗿 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀

NumPy helps with everyday tasks like:

 • Creating test arrays (`np.zeros`, `np.random.random`)
 • Handling types (`astype`, `dtype`)
 • Saving/loading intermediate datasets (`np.save`, `np.savetxt`)

Practicing small NumPy programs goes a long way in making everything else easier.

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Tagging individuals who help many learners with Data Science/ML upskilling & preparation. Hope this adds value to your community! - Venkata Naga Sai Kumar Bysani Tajamul Khan Andres Vourakis Timur Bikmukhametov, PhD Karun Thankachan Nirmal Budhathoki

#NumPy #Pandas #DataScience #DataAnalytics

Python - NumPy

Essential interview preparation notes for Data Analyst roles!

Acing your data interview takes more than technical skills.

You need to be able to:

1) Communicate clearly your projects
2) Being able to solve open-ended questions
3) Come up with the right metrics to measure
4) Be great at SQL (the most asked technical skill)

Being able to do these 4 things well, will put you in the best spot to land your next Data Role.

In the next year, I will expect AI being part of the interview process for Data Analysts.

Here are few resources to get you started:
📗 The Interview Book: https://lnkd.in/ei8Sp6Qw
📕 Interview Questions: https://lnkd.in/eHEb4u3P

———

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A very valuable post for individuals preparing for data analyst interviews. Behavioural interviews are as important as the technical ones

View Venkata Naga Sai Kumar Bysani’s profile

Most candidates ace the technical questions and then stumble on the behavioral ones that actually decide who gets hired.

Behavioral questions reveal how a candidate thinks, communicates, and drives outcomes. They show whether someone can take data and turn it into decisions.

Here’s one of the most common questions and exactly how to answer it:
“𝐓𝐞𝐥𝐥 𝐦𝐞 𝐚𝐛𝐨𝐮𝐭 𝐚 𝐭𝐢𝐦𝐞 𝐲𝐨𝐮𝐫 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐢𝐧𝐟𝐥𝐮𝐞𝐧𝐜𝐞𝐝 𝐚 𝐤𝐞𝐲 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧.

Many candidates talk about SQL queries or dashboards.

Hiring managers want business impact.

𝐇𝐞𝐫𝐞'𝐬 𝐚 𝐒𝐓𝐀𝐑 𝐚𝐧𝐬𝐰𝐞𝐫 𝐭𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐰𝐨𝐫𝐤𝐬:

𝐒𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧: Our e-commerce company was losing money on shipping. The CFO wanted to cut free shipping entirely, which would've hurt customer acquisition.

𝐓𝐚𝐬𝐤: I needed to find a data-driven solution that balanced costs with customer experience before the board meeting in 5 days.

𝐀𝐜𝐭𝐢𝐨𝐧: I analyzed 6 months of order data and discovered:
• 78% of losses came from orders under $35
• Customers with 3+ orders had 85% higher lifetime value
• Competitors offered free shipping above $50

I built a cohort analysis showing how a $40 minimum would impact different customer segments. Then created a simple visual showing projected savings vs. customer retention rates."

𝐑𝐞𝐬𝐮𝐥𝐭: We implemented free shipping above $40. Result:
• Saved $1.8M annually in shipping costs
• Average order value increased 23%
• Customer retention stayed flat

𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐚𝐧𝐬𝐰𝐞𝐫 𝐰𝐨𝐫𝐤𝐬:

 - Shows business thinking
 - Quantifies impact clearly
 - Demonstrates stakeholder alignment
 - Connects analysis directly to strategy
 - Stays concise and structured

𝐓𝐡𝐞 𝐬𝐞𝐜𝐫𝐞𝐭: Keep it under 90 seconds. Practice until it feels natural. Get mock interviews

𝐑𝐞𝐦𝐞𝐦𝐛𝐞𝐫: They're not testing your SQL skills here. They're testing if you can translate data into decisions that move the business forward.

𝐖𝐡𝐚𝐭'𝐬 𝐲𝐨𝐮𝐫 𝐒𝐓𝐀𝐑 𝐬𝐭𝐨𝐫𝐲?

𝐐𝐮𝐢𝐜𝐤 𝐝𝐢𝐬𝐜𝐥𝐚𝐢𝐦𝐞𝐫: this is just a sample STAR answer (not my own story 😄).

Drop it below - let's refine it together 👇

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If you're building data solutions for your organisation, don’t think small. Don’t optimise only for today’s questions. Design with tomorrow’s business needs in mind.

In my first year in product analytics, I learned some core principles about building scalable, future-ready solutions:

𝗪𝗵𝗮𝘁 𝘂𝘀𝘂𝗮𝗹𝗹𝘆 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 - 

 • We look at the current state of the business and prioritise solutions that answer today’s questions.
 • Under pressure, teams push for quick wins — fast dashboards, rapid data pipelines, immediate outputs.
 • This works in the short term, but creates long-term friction when business strategies evolve.

However, 𝘁𝗵𝗲 𝗿𝗶𝘀𝗸 𝗼𝗳 𝘀𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝗯𝘂𝗶𝗹𝗱𝘀 - 

 • Solutions that are not modular or robust become difficult to adapt.
 • Even strong early work loses credibility if it can’t evolve.
 • Teams end up spending more time fixing, reworking, or rebuilding instead of enabling faster decisions.

𝗪𝗵𝗮𝘁 𝘁𝗼 𝘁𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝘄𝗵𝗶𝗹𝗲 𝗱𝗲𝘀𝗶𝗴𝗻𝗶𝗻𝗴, 𝗮𝘀𝗸 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 - 

 • What problems can this solution solve today?
 • How can we design it to answer 𝗻𝗲𝘄 𝗼𝗿 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 questions tomorrow?
 • Can the backend or frontend absorb changes quickly without major rewrites?
 • Can the solution be part of a reusable ecosystem — not a one-off dashboard, tool, or dataset?

𝗧𝗵𝗲 𝗺𝗶𝗻𝗱𝘀𝗲𝘁 𝘁𝗵𝗮𝘁 𝗵𝗲𝗹𝗽𝘀 - 

 • Build with flexibility, modularity, and scalability in mind.
 • Be willing to invest some extra effort upfront to save months of rework later.
 • Think of your solution not as a final product, but as an evolving capability for the business.

Future-ready design isn’t about doing more — it’s about thinking better.

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Model drift is one of the biggest reasons machine learning models lose accuracy after deployment.

It happens when the relationship between input features and the target variable changes over time, while the model stays the same.

In real-world systems, this decline is usually gradual and goes unnoticed until it becomes a business problem.

𝗔𝘀 𝗮𝗻 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 - Suppose you deploy a model to predict 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝘄𝗶𝗹𝗹 𝗮𝗰𝗰𝗲𝗽𝘁 𝗮 𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗹𝗼𝗮𝗻.

It starts strong — around 88% accuracy.

But over the next few months:

 • spending behaviour changes
 • Income patterns shift
 • new customer segments appear
 • market conditions evolve

Yet the model continues making predictions based on old patterns.

𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁?
High-quality customers are wrongly rejected, and unsuitable applicants may slip through.

You start seeing a slow decline:

Month 1: 87%
Month 2: 85%
Month 3: 89%
Month 4: 86%
Month 5: 71%
Month 6: 68%

By the time metrics drop sharply, the business has already taken the hit.

𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗛𝗮𝗽𝗽𝗲𝗻𝘀 ?
The data distribution in production no longer resembles the data the model was trained on. The model is fixed; the environment isn’t.

This is where Monitoring - a crucial step in MLOps becomes essential and guide next steps to control such performance dips

𝗛𝗼𝘄 𝘁𝗼 𝗿𝗲𝗱𝘂𝗰𝗲 𝗼𝗿 𝗰𝗼𝗻𝘁𝗿𝗼𝗹 𝗺𝗼𝗱𝗲𝗹 𝗱𝗿𝗶𝗳𝘁 (𝗶𝗻 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲):

 • Monitor data and model performance continuously
 • Set thresholds/alerts for changes in input patterns or prediction accuracy
 • Retrain models periodically using fresh, recent data
 • Use incremental or rolling training pipelines where possible
 • Re-validate assumptions as business conditions evolve
 • Keep tight feedback loops with business teams who notice behavioural shifts early

These steps don’t eliminate drift, but they help detect it early and keep the model aligned with real-world behaviour instead of letting performance quietly degrade.

In a loan context, it helps ensure the model continues approving the right customers and doesn’t start harming conversions or increasing risk.

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If you’re building any data solution — a dashboard, dataset, model, or internal tool — there’s one principle that always holds true:

𝗕𝗲𝗴𝗶𝗻 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗲𝗻𝗱 𝗶𝗻 𝗺𝗶𝗻𝗱.
(Thank you, Stephen Covey.)

In analytics, the “end” isn’t the code or the chart.
The “end” is the person 𝘄𝗵𝗼 𝘄𝗶𝗹𝗹 𝘂𝘀𝗲 𝘆𝗼𝘂𝗿 𝘄𝗼𝗿𝗸 𝘁𝗼 𝗺𝗮𝗸𝗲 𝗮 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 — whether that’s an analyst, a product manager, or a senior leader.

Most teams build for today’s question.
Great teams build for the 𝗻𝗲𝘅𝘁 𝟭𝟬 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀.

Because business priorities evolve.
Stakeholder needs change.
And the follow-ups always come.

That’s why thoughtful design matters.

Here's what to keep in mind when building:

 • Focus on the user: Build so they can understand, trust, and act on it.
 • Make it reusable: Clean structure, clear logic, consistent naming.
 • Track what matters: Only include what drives real decisions.
 • Design for change: Keep it flexible as questions evolve.
 • Think beyond one asset: Build ecosystems, not one-off outputs.

Data solutions create the most impact when they’re 𝘂𝘀𝗲𝗳𝘂𝗹, 𝘂𝘀𝗮𝗯𝗹𝗲, 𝗮𝗻𝗱 𝗮𝗱𝗮𝗽𝘁𝗮𝗯𝗹𝗲.
Build for the user.
Build for the future.

Begin with the end in mind.

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