RBC Capital Markets

- Lead the requirements gathering and creation of data quality rules for a new regulatory initiative (T+1).
- Manage 2 contract workers in drafting requirements, reviewing data lineage and creating be-spoke data quality rules across multiple quality 
 dimensions.
- Built a Tableau report all data quality controls created with meta-data on schema and other useful data elements to help reduce the time in the drafting of new data controls by 20%
- Built a Python script to Extract Deeply Nested JSON from S3 and Tranform into a CSV format for easy processing and analysis. (Avg size of data set is 600MB across 10 different instances with over 10 different critical data elements)

Senior Data Analyst

• Resolved a data quality issue of missing maturity dates on outstanding loans with a material impact of 122 billion USD.
• Collaborated with finance and regulatory reporting stakeholders to implement business requirements, resulting in a 25% increase in data quality and adherence to compliance guidelines as part of an enhanced enterprise data governance framework.
• Utilized Tableau to create an intuitive schema and data quality report, leading to a 35% enhancement in departmental rule design efficiency at RBC Capital Markets.
• Engineered data pipeline processes using Airflow and Python, reducing data processing times by 30% for faster insights delivery and improved efficiency in strategic decision-making.
• Collaborated with Regulatory Reporting and Finance IT stakeholders to enhance data quality and efficiency using DBT, resulting in a 25% reduction in data-related errors.
• Developed SQL data models on a cloud data warehouse and administered a Dremio data lake, handling over 10TB of data.

Data Analyst

Data Achievers

•  Worked with a variety of clients to support business intelligence reporting and data quality assurance.
•  Monitored product performance and customer satisfaction metrics to identify areas of improvement, resulting in a 40% increase in user engagement and a 25% increase in revenue.  
• Developed and maintained product roadmaps and feature backlogs, resulting in a 30% increase in team efficiency and a 20% increase in project success rate. 
• Created and maintained product dashboards and reports for job seekers, resulting in a 25% reduction in decision-making time and a 15% increase in data accuracy.

Data Scientist

TALENT™ (Ontario Tech Talent)

• Supported a self-paced, self-directed skills development experience (“Self Paced Experience”) for students looking to upskill in data analytics;
• Asynchronously engaged with learners (e.g. discussion forum moderation) via email, chat, or LMS like Canvas
• Provided 1:1 learner support every week; offered as virtual appointments booked through Calendly
• Effectively and efficiently verified customer skill acquisition by way of reviewing all artifacts and other work products presented for verification by learners against approved standards
• Recorded skills verification decisions in TALENT’s system of records

Skill Coach

• Demonstrated exceptional communication skills and exercised diligence in identifying delinquent loans, credit cards, credit lines, or mortgages, and referred clients to Collections.
• Established and maintained relationships with individual or business customers by offering value through every interaction.
• Identify fraudulent transactions at the first point of contact and work with AML and Fraud teams to investigate the transaction to ensure minimal client impact.
• Actioned solutions relevant to client banking needs, with a 30% action rate weekly, to support team goals.

Client Data Analyst

IKEA

- Enhanced order fulfillment Tableau dashboards, improving operational visibility and increasing efficiency by 15%. 
- Analyzed operational data to optimize processes, leading to a 25% improvement in service levels. 
- Collaborated with teams to implement data-driven strategies, reducing inventory errors by 20%

Operations Analyst Intern

Walmart Canada

•	Provided exceptional customer service to clients on the sales floor, ensuring that all their questions are answered promptly and efficiently.
•	Acted as the first point of contact to customers' challenges with finding merchandise on the sales floor.
•	Contributed to team success by supporting colleagues with their workload to effectively deliver on customer promises.
•	Built and maintained a strong relationship with colleagues to improve the quality of work and increase customer satisfaction.
•	Managed safety risk on the sales floor by proactively identifying and removing potential safety issues.

Fulfillment Associate

Fond Regional Pour La Promotion de la Sante du Centre (FRPSC)

• Implemented an analytical framework that reduced transportation costs by 20% by determining the best delivery route for urgent medicine delivery across geographical regions.
• Built dashboards using Tableau to help track Key Performance Indicators (KPIs) thereby increasing visibility on key business metrics.
• Conceptualized a new service delivery design which when implemented improved service levels by 25%.
• Analyzed 6 business processes to collect measurable data.
• Improved the existing demand forecasting software performance by modeling data sources to be compatible with schema.
• Maintained inventory ERP system by ensuring consistency between inventory on-hand and inventory in the system

Logistics Analyst

Cam-Green

• Planned and developed content strategies.
• Built strong relationships with project team members and stakeholders to ensure the achievement
of set goals.
• Assessed business opportunities and develop strategies to attract venture capital.
• Responsible for drafting a business plan for validating the business model.
• Provided consultation on planning and starting new businesses.
• Prepared business proposals and reports for presentation to stakeholders.
• Conceived and implemented a sales development program for the launch of the project which led
to a 110% achievement of the sales target.
• Developed media campaigns across social media outlets and campus bulletins which galvanized
support from key government officials.
• Worked closely with the project manager to ensure that sales and marketing efforts are consistent
with project goals.
• Complaint with established guidelines set by the University Board sponsoring entrepreneurial
initiatives.

Social Media Manager

I help entry-level, non-technical professionals get their first data analyst role | Data Analyst @RBC | Data Career Coach

I love to identify hidden business opportunities that lie in data and bring them to life with compelling visualizations.  I spend time setting clear goals, plans of action, and risk mitigation strategies for data analytics projects before even collecting data. 

I am a data analytics professional with a strong background in supply chain and marketing. I am passionate about data engineering. I love a good journey🚘, be it a journey in an RV truck across country, a learning journey📝 , or a customer journey with multiple touch points, or a data pipeline from source system to end user application. I am always curious to know what I can learn about a customer's journey to ensure it is seamless.😊

At RBC I have developed my customer-focused problem-solving mindset at the awareness stage and consideration stages of the business's marketing funnel. I worked closely with partners across the delivery channels (branch and telephone) to deliver exceptional customer service to clients to meet their banking needs. 

At RBC Capital Market, as a Senior Data Analyst, I own the process of ensuring data sent to report owners are fit for purpose (without data quality issues). Build data quality rules using SQL. Build Tableau dashboards to monitor data quality of critical data elements (CDE's) in production 

My experience working with cross-functional teams and creating a sense of synergy in the achievement of a common goal has proven to be a valuable value add. I helped in the resolution of data quality issues by proposing changes to how data is transformed both upstream and downstream.

I am always on the lookout for technologies that improve my workflow; I will take a week or so and dive deep into a data technology to see if it’s a good fit for a side project I'm working on and put aside if not. 

When I'm not working I am with my family making sure we are living every moment and making it count. You can also find me with my headsets listening to the next big rising artist of our generation and exploring new genres of music - I am a big lover of Afrobeats and Cameroonian cuisine 🇨🇲 

---
Current Status: Helping folks get their First Data Analyst roles - DM me to learn more

Peter Ndiforchu

I’ll be honest: the hardest skill I had to learn in data had nothing to do with data.

It wasn’t SQL.
It wasn’t Excel.
It wasn’t even storytelling with dashboards.

It was learning to ask the right questions.

Early on, I’d rush to analyze data — build visuals, crunch numbers, automate reports — only to realize I didn’t actually understand what problem I was solving.

That’s when it clicked:
👉 Great analysts don’t just answer questions.
They help clarify them.

Now, before I open any tool, I ask myself:

What decision will this data inform?

Who needs this insight, and why?

What does success really look like?


Once I learned that skill, everything changed.

🎯 Your turn:
What was the hardest skill you had to learn early in your data journey?

---
📌 Some of the hardest skills in data are the most basic skills. RSVP for our upcoming event on Saturday 08/10 to learn more about how to stand out with these skills in a competitive job market: https://luma.com/wphk134i

#52DaysDataChallenge

I help mid/senior level, non-technical professionals get their first data analyst role | Senior Data Analyst @Autodesk |Top rated Data Career Coach on topmate.io

You don’t need a data job to start thinking like an analyst.

A few years ago, I wasn’t a “Data Analyst.”
I was working in warehouse operations and customer service — managing shipments, handling client issues, and keeping things running smoothly.

Back then, I didn’t even know “data analytics” was a career path.
But I noticed something:

Every decision —
How much to stock, when to reorder, which customers to prioritize — was driven by data.

And that curiosity changed everything.

I started asking:
🧠 “What story is this data telling us?”
📊 “How can I use it to make smarter decisions?”

That’s when I began my transition — not by quitting overnight, but by learning step by step:
✅ Mastered Excel and dashboards
✅ Took online data courses after work
✅ Practiced by analyzing data from my own job
✅ Built small projects and shared them online

Slowly, I went from tracking numbers to telling stories with data.
And that shift opened doors to a brand-new career.

If you’re in a non-data role right now, remember that you’re not starting from zero.

You’re bringing problem-solving, people skills, and business insight that analysts use every day.

You just need to layer on the tools and mindset.

So tell me!

💬 What did you do before data?

#52DaysDataChallenge

Most data analysts didn’t take the straight road. They built their careers from the detours.

Every week in this challenge, I meet people who stepped into data through the side doors: 

→Retail floors
→Call centers
→Warehouses
→Operations teams
→Customer service desks
→Every job that required GRIT before SQL.

Some of the most resourceful and business-savvy analysts I know didn’t start with a technical background. 

They started with:

→People 
→Problems
→Messy processes
→High-pressure decisions. 

The mindset came first.
The data skills came later. 

Live #3 will spotlight the career journeys that rarely get told:

→The self-taught
→The late starters
→The career shifters
→The “I didn’t study this” crowd


If you have ever wondered whether your background is an advantage waiting to be reframed, this session will reveal exactly how that transformation happens.

I want your input:

👉 Who should I invite next
Tag someone with an unexpected path into data
Or share the story you want to see featured

Let’s turn this Live into the most relatable conversation of the challenge.

The real test of my data career is not SQL or dashboards.

It is explaining my job to my family around the world.

I’ll jump on a call excited to share something meaningful about a new insight or a successful analysis.

Before I get two sentences in, someone immediately says,
“Peter, you are good with computers. My WhatsApp is acting strange.”

I have explained my work so many times.
Analytics. Forecasting. Helping teams make decisions.

Yet the official family definition has settled at:
“Peter works with numbers and computers. Ask him.”

My mom once introduced me to someone at church with pure confidence.
“My son works in technology. If your phone is not behaving, he can fix it.”

My brother once asked if I could check his internet history from Canada.
He really believed I had that level of power.

And whenever the holidays come around, one relative always waits for the right moment.
“Peter, since you understand computers, my WiFi has been slow since 2019. Can you take a look?”

At this point, I save my explanations for work.
With family, I just help where I can and smile while doing it.

Family: “So you are I.T.?”
Me: “Not exactly”
Family: “Great. The remote stopped working.”

Anyone else living this experience? 😅

#52DaysDataChallenge

🎙️ Essential Data Skills for Career Switchers

When I first started learning data, I had no idea where to begin.
I spent months jumping between Excel tutorials, SQL videos, and random courses — but nothing connected.
It wasn’t until I understood which skills actually mattered that things finally started to click.

That’s why I’m hosting Live #2 of the #52DaysDataChallenge:
👉 “Essential Data Skills for Career Switchers”

In this session, I’ll share:
✅ The foundational data skills every career switcher should master first
✅ Real examples of how these skills show up in the workplace
✅ The biggest learning traps that waste time and delay progress
✅ A simple roadmap to go from confused beginner to confident analyst

👥 Tag someone who’s learning data right now!
Let’s make this journey easier — together.

Darshan Patel, Bukola Oyinlola and 89 other attendees

🎙️ Essential Data Skills for Career Switchers

I didn’t start in data.
And most people who end up in analytics… didn’t either.

When someone asks, “How do I transition into Data Analytics from my old job?”
I always think back to the moments that quietly shaped my own journey.

Most of us made the switch long before we realized we were “doing data.”
We were the ones fixing broken processes…
…tracking numbers when nobody asked…
…building little spreadsheets to make life easier for our team…
…spotting patterns in chaos that no one else noticed.

Your old job didn’t hold you back.
It trained you.

→Teachers already understand assessments and learning outcomes.
→Bankers understand risk, trends, and financial behavior.
→Retail workers understand customer patterns.
→Healthcare workers understand process flow and documentation.
→Engineers understand systems thinking.
→Marketers understand experimentation and attribution.

Every role comes with analytical gold—you just need to translate it.

Here’s the part most people miss:

You don’t transition by abandoning who you were.
You transition by repackaging what you already did.

If you’re trying to break in:
• Identify the analytical parts of your old role.
• Show how you improved something with numbers.
• Highlight moments where you solved problems with data, even informally.
• Build 2–3 small portfolio projects that mirror the jobs you’re applying for.
• Tell your story as someone who’s already been using data… just without the title.

The door into analytics isn’t as far as it looks.
You’ve likely been standing in front of it for years.

What did you do before data?
Share below — your story might inspire someone else.

#52DaysDataChallenge

💻 If Excel had feelings…

It would probably file an HR complaint by now. 😂

Because let’s be honest

We’ve all done some questionable things to our poor spreadsheets:

1️⃣ Renamed the same file 8 times: “Final.xlsx” → “Final2.xlsx” → “Final_ThisOne.xlsx” → “Final_FINAL.xlsx”
2️⃣ Used “Merge & Center” like it’s a design tool 🎨
3️⃣ Written a formula so long even Excel gave up: #NAME?
4️⃣ Pretended we knew why the pivot table disappeared 😅
5️⃣ Watched Excel freeze… and prayed for our unsaved work 🙏
6️⃣ Copied data from a PDF like we were performing surgery with a spoon 🥲

Yet through it all, Excel stays loyal.
No subscription drama. No attitude. Just pure spreadsheet magic.

If Excel could talk, what do you think it would say after seeing your last file? 👀

#52DaysDataChallenge

It wasn’t love at first sight.
At first, data looked complicated. 

Rows. 
Columns. 
Chaos.

My journey started far from spreadsheets, on retail floors, in warehouse stores, and on countless customer service calls.

I didn’t have dashboards or SQL then, just conversations.
But every interaction had something in common: patterns.

I began noticing how certain products sold faster at specific times.

How customers’ frustrations often pointed to deeper issues.

How one small change in process could ripple across an entire team.

Back then, I didn’t call it data.
I called it paying attention to detail.

Over time, I realized those observations were data.
And once I learned how to analyze them, everything changed.

Data stopped being numbers.
It became stories, truths, insights waiting to be uncovered.

That’s what made me fall in love with data.
Its power to give meaning to everyday moments.
To show patterns in chaos.
To help people see what they couldn’t before.

💬 Now I’m curious.
What made you fall in love with data?
Was it a project, a person, or that one “aha” moment when it all clicked?

Someone needs to hear your story today.

Day 17 #52DaysDataChallenge

The most underrated data skill isn’t SQL, Python, or dashboards.

It’s empathy.

I’ve spent years across retail floors, warehouses, and corporate boardrooms… and one pattern always stands out:

The analysts who rise the fastest aren’t just technically strong.

They care enough to understand the human on the other side of the data.

Empathy helps you:
• Ask sharper questions because you understand the real pain behind the request
• Build dashboards people actually use
• Translate complexity without making stakeholders feel small
• Design solutions that fit the workflow, not fight it
• Notice when a number feels “off” because you understand the story of the process

People think empathy is “soft.”
In reality, it’s a strategic advantage.

Every insight we deliver impacts someone’s job, decision, or customer.

When you remember that, your work becomes sharper, more relevant, and significantly more trusted.

The most underrated data skill: empathy.
Agree?

If you took away these 5 tools, my entire week would fall apart.

Here are the core platforms I rely on as a Senior Data Analyst — and why they matter for real business impact:

1. SQL
My decision engine.
This is where I validate narratives, challenge assumptions, and uncover the signals leaders don’t see yet. When the story is unclear, SQL brings the truth to the surface.

2. dbt Labs
Where chaos becomes structure.
Testing, documentation, lineage, and version control — this is how analytics scales beyond a single analyst. dbt turns insights into reliable systems the whole business can trust.

3. Power BI / Looker 
The boardroom translator.
Dashboards aren’t about charts. They’re about influence.
This is where I refine the message, highlight the “why it matters,” and drive decisions that move revenue, cost, or risk.

4. Cursor
The accelerator I didn’t know I needed.
Reusable SQL templates, automated documentation, workflow shortcuts — it’s helped me reclaim hours and reinvest them into deeper, strategic analysis.

5. Jira/Confluence by Atlassian
The operational glue.
Intake, alignment, prioritization, cross-functional visibility — without these, even the best insights get lost in the shuffle. These two keep the work moving and the team aligned.

These tools cover the full lifecycle:

Question → Data → Transformation → Story → Delivery.

Which one should I demo next?

#52DaysDataChallenge

5 Essential Data Analytics Workflow Tools 

Some days, being a Data Analyst feels like being a translator…between English and ‘Data.’

Stakeholder: “I want a dashboard to show user satisfaction — so we can see how pleased our users are with our product.”

Me: “Absolutely! I can build that. Are there any specific KPIs that interest you?”

Stakeholder: “You mean… metrics?”

Me: “Kind of! Metrics are the raw numbers like number of promoters or rate of positive feedback. KPIs are the key ones that tell us if we’re actually meeting our goals like NPS or CSAT.”

Stakeholder: “Got it. Then I’d like to see our NPS and CSAT.”

Me: “Perfect! Those are great KPIs for user satisfaction. What grain would you like to see on the dashboard visuals?”

Stakeholder: “Grain? Like... rice? We’re not an agricultural SaaS, you know.”

Me (internally): Deep breath. Smile. You’re the data whisperer, remember? 😅

Stakeholder: “Not sure what you mean, but I trust your expertise.”

Me: “Fantastic. I’ll take it from here.”

That conversation reminded me that Data Analysts are part translator, part therapist, part magician.

You’re not just building dashboards.
You’re bridging two worlds: business and data.

The best dashboards aren’t the flashiest,  they’re the ones your stakeholders actually understand.

So the next time someone says:

“We need a dashboard!”

Ask them:

→ What decision will this dashboard help you make?

→ Which KPIs matter most?

→ What level of detail (not rice 😄) do you want?

That’s how you turn confusion into clarity — and earn the words every analyst loves to hear:

 “I trust your expertise.” 🙌

#52DaysDataChallenge

Every data pro you admire once stood where you are — at the bottom of the same ladder.

When I first stepped into data analytics, I thought there were only two levels:
✅ “Beginner”
✅ “Expert”

Turns out… there’s an entire ladder in between.
And understanding it changed how I learned, applied, and grew.

Here’s how I now see the Data Career Ladder 👇

1️⃣ Data Curious — You’re exploring what data even is. Watching YouTube videos, reading posts, trying to understand what analysts do.

2️⃣ Data Learner — You’ve picked your tools (Excel, SQL, maybe Python). You’re following tutorials, taking courses, and building your foundation.

3️⃣ Data Doer — You’re applying skills on small projects. You clean messy spreadsheets, build dashboards, or analyze a dataset from Kaggle.

4️⃣ Data Storyteller — You connect insights to impact. You can explain “what the data means” in a way business people understand.

5️⃣ Data Strategist — You go beyond reports. You influence decisions, set up frameworks, and help define what to measure in the first place.

💡 Every step builds on the last — and you can’t skip steps.

When I recognized where I was on this ladder, I stopped comparing myself to others…
and started focusing on the next rung.

---
Your turn:
👉 Where are you on this ladder right now?
👉 What’s one skill helping you climb to the next level?

#52DaysDataChallenge

The Data Career Ladder

Sharing with my network! I've once interviewed for this role and they are a solid team I must say!

Célia Bru is a great data leader!

📈 We're hiring a Senior Data Analyst at KOHO! 📈 

My team needs someone rare: someone who lives at the intersection of deep business thinking and hands-on technical mastery.

You'll own data models in DBT, and create dashboards, but that's just the baseline. What really matters? You understand what drives our business forward and can draw clear connections between technical work and strategic business outcomes. 

The cherry on top: You would get to work with the most caring and fun team I know! ❤️ 

#hiring #dbt #dataanalytics

View the open to hiring details of Célia

The Power of Storytelling in Data Careers. 

This one skill has helped me grow my data analytics career in so many unexpected ways.

What's that one unexpected skill you didn't know would serve you well in your career?

#52DaysDataChallenge

Dashboards behave perfectly… until they sense a stakeholder within 10 feet.

Have you ever tried to convince a toddler to sit still for ONE photo?

That's my relationship with my dashboards before a stakeholder demo

You line up the filters.
You fix the labels.
You triple-check the KPIs.

And for a brief, glorious moment… everything behaves.
Until—
You hit Publish and the dashboard decides to express its true personality.

Suddenly:
→A number you didn’t touch is different.
→A filter you tested 12 times stops working.
→A chart decides today is the day it wants to be “creative.”

And then a stakeholder pops in like:
“Is the data… supposed to do that?”

If you’ve ever had this love–hate relationship, you already know the feeling.

Describe this with ONE emoji.
(I’m ready for the chaos.)

Who can relate?

#52DaysDataChallenge

Unconventional Paths Into Data Analytics 

Most analysts didn’t start in data.
They came from retail, banking, customer service, logistics, hospitality, and so many other places that taught them how to solve real problems long before they touched SQL.

This Live breaks down how people with unconventional backgrounds successfully pivot into analytics — and how your past experience might already give you an edge.

In this session, you’ll learn:
• How to spot “data moments” in your past roles
• Why non-traditional paths often create stronger analysts
• Real stories of pivots that worked
• Practical steps to position yourself for a data role

Date: November 22nd, 2025
Time: 12:15 PM EST

If you’re transitioning into data or guiding others on the journey, this one is for you.

Join us and bring your questions.

Rinu Francis, Bukola Oyinlola and 30 other attendees

Unconventional Paths Into Data Analytics 

If your insight doesn’t change a decision, it’s not an insight. It’s a recap.

How do you write insights that make execs listen?

Most analysts write insights to explain data.
The best analysts write insights to move decisions.

Here’s the pattern I’ve seen across product, finance, and capital markets:

1. Lead with the headline, not the homework.
Your first sentence should answer: “Why does this matter right now?”

2. Translate metrics into money, risk, or time.
Executives think in trade-offs and outcomes—speak their language.

3. Create tension before you reveal the punchline.
Show the gap between the current state and the opportunity.
Tension fuels attention.

4. Strip out every detail that doesn’t change the recommendation.
If it doesn’t move the decision, it doesn’t belong in the slide.

5. End with a clear call: decide, approve, prioritize, or pivot.
Your insight should point to an action—not another meeting.

This simple narrative shift consistently takes conversations from:
“Nice dashboard.”
to
“Okay—what do we do next?”

What’s your storytelling trick when presenting insights?

#52DaysDataChallenge

How Communicate Actionable Insights to Execs

Some days the hardest part of being an analyst isn’t the data… it’s the tools.

We all have that one platform that tests our patience, derails our flow, or sends us straight to Google with a desperate “why isn’t this working?”

So let’s find out:

Which tool frustrates you the most?

I’m running this poll because every frustration has a story behind it — the refresh that never finishes, the merge conflict that ruins your morning, the notebook that suddenly refuses to run the same cell it ran an hour ago.

Vote — then share the specific tool and one thing that really gets under your skin.

Your comment might help someone realize they’re not alone… or point them to a solution you wish you had sooner.

What’s the one tool that tests your patience the most?

#52DaysDataChallenge

Data without context is noise.
Context without curiosity is wasted.

Lately I’ve noticed something across teams I support:

People aren’t short on dashboards.
They’re short on situational awareness.

You can have perfect SQL, clean models, and beautifully designed visuals—
but if you don’t understand the “why behind the numbers,” your insights never move the room.

Executives don’t act because you showed a metric.
They act because you showed meaning.

The strongest analysts I know do three things exceptionally well:

1. Anchor every number to a business moment
A spike doesn’t matter until you explain what triggered it.

2. Pull in what’s not in the data
Market dynamics, customer sentiment, operational constraints, timing.

3. Translate insights into consequences
“If we don’t fix this, here’s what happens.”

That’s when noise becomes narrative.
And narrative becomes action.

What context are you missing lately?

Read any ERD and join tables like a pro — SQL fundamentals

Data Scientist | 150K LinkedIn | BCBS Of South Carolina | SQL | Python | AWS | ML | Featured on Times Square, Favikon, Fox, NBC | MS in Data Science at UConn | Proven record in driving insights and predictive analytics |

Lead Data Analyst with a passion for driving data-driven strategies, predictive modeling, and team optimization across various industries, including insurance and retail. 

Currently, I lead data initiatives at BCBS of SC, where I develop dashboards, optimize workflows, and build predictive models to support C-Suite decision-making and business strategies. My work spans multiple tools like SQL, Python, AWS, and Power BI to streamline processes and enhance operational efficiency.

Career Highlights:

•	Optimized workflows, saving 120+ hours of manual work per week, driving efficiency through AWS integration.
•	Developed real-time dashboards at The LEGO Group, improving sales forecasting accuracy by 15% and decision-making speed by 23%.
•	Recognized for building predictive models, earning recognition in data science competitions like NESS Statathon (3rd place out of 150 participants).

Beyond my work, I've had the privilege of competing and winning several hackathons and data science competitions, including earning 1st prize at Well4Tech’s Data Science Health Hackathon, and NESS Statathon by Travelers. My innovative work has been featured on Times Square billboards, and covered by major media outlets like Fox, NBC, and over 20+ articles. Additionally, I’ve been recognized by Favikon for my contributions to the data community.

I have experience working with a range of data science tools like PySpark, SQL, Power BI, and Python, and hold certifications such as Tableau Desktop Specialist and AWS Machine Learning. As a strong believer in mentorship and continuous learning, I’ve also contributed as a Graduate Teaching Assistant for SQL and led the UConn Data Analytics Club, fostering growth and development within the community.

Feel free to reach out if you’d like to discuss data analytics, data science, or potential collaboration: saikumarbysani149@gmail.com.

💬 𝗩𝗶𝗲𝘄𝘀 𝗮𝗿𝗲 𝗺𝘆 𝗼𝘄𝗻 𝗮𝗻𝗱 𝗱𝗼 𝗻𝗼𝘁 𝗿𝗲𝗳𝗹𝗲𝗰𝘁 𝘁𝗵𝗼𝘀𝗲 𝗼𝗳 𝗺𝘆 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗿!

Venkata Naga Sai Kumar Bysani

Never ignore SQL or you will be ignored!🙂


No matter if you're a Data Scientist, Data Engineer, or Data Analyst, we all answer to the king... SQL.

If you’re diving into data roles, knowing SQL isn’t just an added skill—it’s essential. Your next role could be just a query away!😉

P.S.: Join Manisha, Siddarth, and me for the 15-𝐃𝐚𝐲 𝐒𝐐𝐋 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 starting in the 1st week of 𝐌𝐚𝐫𝐜𝐡! Get ready to tackle real-world SQL problems and level up your skills.

🔗 𝐉𝐨𝐢𝐧 𝐭𝐡𝐞 𝐰𝐚𝐢𝐭𝐥𝐢𝐬𝐭 𝐡𝐞𝐫𝐞: https://lnkd.in/d3Ds7EnG

♻️ Repost if you've ever written a query! 😉

6 books that will make you a 10x data analyst:


𝟏. 𝐒𝐭𝐨𝐫𝐲𝐭𝐞𝐥𝐥𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐃𝐚𝐭𝐚 - Cole Nussbaumer Knaflic
Teaches the mindset shift of turning clear, actionable stories that drive decisions.

𝟐. 𝐋𝐞𝐚𝐧 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 - Croll & Yoskovitz
A must-read on picking the right metrics and using data to drive growth and decisions.

𝟑. 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐟𝐨𝐫 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 - Provost & Fawcett
Builds intuition for how analytical methods connect to business strategy and ROI.

𝟒. 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 𝐓𝐨𝐨𝐥𝐤𝐢𝐭 - Ralph Kimball
Foundational for understanding dimensional modeling and answering systems.

𝟓. 𝐏𝐲𝐭𝐡𝐨𝐧 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 - Wes McKinney
The ultimate guide to using Pandas & NumPy, written by the creator of Pandas himself.

𝟔. 𝐍𝐚𝐤𝐞𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - Charles Wheelan
Makes statistics approachable and shows when data truly matters (and when it's misleading).

𝐖𝐡𝐚𝐭 𝐞𝐥𝐬𝐞 𝐬𝐡𝐨𝐮𝐥𝐝 𝐦𝐚𝐤𝐞 𝐭𝐡𝐢𝐬 𝐥𝐢𝐬𝐭?

——

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♻️ Repost to help others become better data analysts.
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7 best GitHub repositories to break into data analytics and data science:


1. 100-Days-Of-ML-Code 
 - 𝐋𝐢𝐧𝐤: (https://lnkd.in/dcftdA57)
 - 𝐒𝐭𝐚𝐫𝐬: ~42k

2. awesome-datascience 
 - 𝐋𝐢𝐧𝐤: (https://lnkd.in/dcFYYwx9)
 - 𝐒𝐭𝐚𝐫𝐬: ~22.7k

3. Data-Science-For-Beginners 
 - 𝐋𝐢𝐧𝐤: (https://lnkd.in/d_zZBadF)
 - 𝐒𝐭𝐚𝐫𝐬: ~14.5k

4. data-science-interviews 
 - 𝐋𝐢𝐧𝐤: (https://lnkd.in/dkN4RZjH)
 - 𝐒𝐭𝐚𝐫𝐬: ~5.8k

5. Coding and ML System Design
 - 𝐋𝐢𝐧𝐤: (https://lnkd.in/gXFaaaQR)
 - 𝐒𝐭𝐚𝐫𝐬: ~3.5k

6. Machine Learning Interviews from MAANG
- 𝐋𝐢𝐧𝐤: https://lnkd.in/gq_huuZD
- 𝐒𝐭𝐚𝐫𝐬: 8.1k

7. data-science-ipython-notebooks 
 - 𝐋𝐢𝐧𝐤: (https://lnkd.in/dPmQuPB9)
 - 𝐒𝐭𝐚𝐫𝐬: ~27.2k

📚 𝐆𝐞𝐭 𝟏𝟓𝟎+ 𝐫𝐞𝐚𝐥 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐰𝐢𝐭𝐡 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 & 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬) 𝐢𝐧 𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐏𝐫𝐞𝐩 𝐁𝐨𝐨𝐤: https://lnkd.in/dyzXwfVp

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𝐏.𝐒. I share data storytelling insights and career tips in my free newsletter. Join 19,000+ readers → https://lnkd.in/dUfe4Ac6

If you're preparing for a data analyst/data science role (in 1 month or less), you must read these case studies!

1. 𝐀𝐢𝐫𝐛𝐧𝐛'𝐬 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐭 𝐒𝐜𝐚𝐥𝐞 
→ Built Minerva to democratize data access for 5000+ employees 
→ Reduced data incidents by 90% with proactive monitoring
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dhnfPCTT

2. 𝐍𝐞𝐭𝐟𝐥𝐢𝐱'𝐬 𝐀/𝐁 𝐓𝐞𝐬𝐭𝐢𝐧𝐠 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 
→ Run 1000+ experiments simultaneously for 230M+ subscribers 
→ Statistical rigor with automated analysis pipelines
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dsBsHNqT

3. 𝐏𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭'𝐬 𝐕𝐢𝐬𝐮𝐚𝐥 𝐒𝐞𝐚𝐫𝐜𝐡 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 
→ Processes 250M+ visual searches monthly with sub-second latency 
→ Increased engagement by 42% for related searches
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dFxS_RWh

4. 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧'𝐬 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 
→ Powers 800M+ member profiles with Pro-ML platform 
→ Frame feature store ensures consistency across training & serving
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/d27PeXEY

5. 𝐒𝐥𝐚𝐜𝐤'𝐬 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐌𝐞𝐬𝐬𝐚𝐠𝐢𝐧𝐠 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 
→ Handles 2B+ messages daily with <500ms global delivery 
→ Scales to 16M channels per host with consistent hashing
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/d8ZYqFzX

6. 𝐋𝐲𝐟𝐭'𝐬 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐏𝐫𝐢𝐜𝐢𝐧𝐠 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 
→ Uses online reinforcement learning for real-time price optimization 
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/deienatS

7. 𝐔𝐛𝐞𝐫'𝐬 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞 
→ Process 500 billion events daily with sub-second query response 
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dsVb_XHP

8. 𝐒𝐩𝐨𝐭𝐢𝐟𝐲'𝐬 𝐑𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧 
→ Personalize music for 500M+ users using collaborative filtering + deep learning 
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dhvgjCwD

9. 𝐃𝐨𝐨𝐫𝐃𝐚𝐬𝐡'𝐬 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 
→ Democratize A/B testing across all teams with multi-sided experiments → Scale from 10 to 1000+ experiments per quarter
𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 𝐥𝐢𝐧𝐤: https://lnkd.in/dnt-ipbe

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮 𝐬𝐡𝐨𝐮𝐥𝐝 𝐟𝐨𝐜𝐮𝐬 𝐨𝐧: 
- What metrics and KPIs drove business decisions 
- How they handled data quality and governance 
- Statistical methods and ML algorithms chosen 
- Tools and tech stack for specific use cases

Don't get distracted by fancy algorithms; start with the business problem. Most successful analytics projects solved core issues with clear metrics and thoughtful experimentation.

Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp

♻️ Save it for later or share it with someone who might find it helpful!

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 20,000+ readers here → https://lnkd.in/dUfe4Ac6

I built an AI agent that turns resumes into pitch decks in 30 seconds. And it's already helping job seekers stand out from 200+ applicants.

You spend hours perfecting your resume. Send it to dozens of companies. And still get ghosted.

Not because you're not qualified.
But because you're playing the same game as everyone else.

While others attach PDFs, what if you sent a personalized pitch deck instead?

𝐇𝐞𝐫𝐞'𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬:
→ Upload your resume + job description
→ AI analyzes and matches your skills to their needs
→ Generates stunning pitch deck with Gamma API
→ Automatically emails you the deck
→ Attach it to recruiters, hiring managers, interviewers

Comment "𝐆𝐚𝐦𝐦𝐚" and I will send you:
- n8n Gamma workflow template
- 10 Gamma prompts for different roles
- 10 message templates for recruiters/hiring managers

Built this using n8n + Gamma API in a few hours.

The technical stack is simple, but the impact is massive.

With Gamma's API, creating presentations is now as easy as filling a form. Watch AI generate professional visuals in seconds.

Stand out from the 200+ applicants applying for the same role.

Stop sending resumes. Start sending pitch decks.

♻️ Repost this if you found this helpful

#GammaPartner

Behind every great insight is a solid statistical foundation.

Here are the 4 methods every data analyst must master:

𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:

Data visualization is just the tip of the iceberg. The real power comes from understanding the statistical methods that reveal relationships, patterns, and predictive insights.

𝐓𝐡𝐞𝐬𝐞 4 𝐬𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐦𝐞𝐭𝐡𝐨𝐝𝐬 𝐩𝐨𝐰𝐞𝐫 𝐞𝐯𝐞𝐫𝐲 𝐝𝐚𝐭𝐚-𝐝𝐫𝐢𝐯𝐞𝐧 𝐝𝐞𝐜𝐢𝐬𝐢𝐨𝐧:

1. 𝐑𝐞𝐠𝐫𝐞𝐬𝐬𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬
→ Predict outcomes and identify what drives them
→ "How does marketing spend impact revenue?"
→ Master: R² for model fit, RMSE for prediction accuracy
→ Pro tip: Always check residuals - they tell the real story

2. 𝐇𝐲𝐩𝐨𝐭𝐡𝐞𝐬𝐢𝐬 𝐓𝐞𝐬𝐭𝐢𝐧𝐠
→ Make confident, evidence-based decisions
→ "Is this A/B test result actually significant?"
→ Master: t-tests for comparing means, ANOVA for multiple groups
→ Remember: Statistical significance ≠ business significance

3. 𝐂𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬
→ Measure relationships between variables
→ "How strongly do these factors move together?"
→ Master: Pearson for linear, Spearman for non-linear
→ Warning: Correlation ≠ causation (but you knew that)

4. 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬
→ Uncover trends, cycles, and seasonality
→ "What will demand look like next quarter?"
→ Master: ARIMA for trends, Exponential Smoothing for patterns
→ Always: Decompose first to understand components

𝐖𝐡𝐲 𝐦𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐞𝐬𝐞 𝐧𝐨𝐰:

↳ Every dashboard needs statistical validation
↳ Every recommendation requires evidence
↳ Every model must be interpretable
↳ Master these = become indispensable

The best part? Once you think statistically, data tells stories you never noticed before.

Master the stats. Master the insights.

Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp

♻️ Save this for your next analysis

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 18,000+ readers here → https://lnkd.in/dUfe4Ac6

10 FREE Messy Datasets to Practice Real-World Data Projects 


🔟 FIFA Player Stats
↳ 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: https://lnkd.in/dJG5yhGe
↳ Contains player stats, skill ratings, and club information from FIFA 23.

9️⃣ Netflix Movies and Shows
↳ 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: https://lnkd.in/dzmdfNPZ
↳ A collection of Netflix titles with metadata like cast, genre, type, and release year.

8️⃣ Titanic (OpenML Raw)
↳ 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: https://lnkd.in/dnv_miuQ
↳ Original Titanic passenger dataset with survival status and passenger info.

7️⃣ IMDb Movies Metadata
↳ 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: https://lnkd.in/dkHA9JfK
↳ Covers metadata on thousands of movies, including genres, ratings, and actors.

6️⃣ Spotify Tracks DB
↳ 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: https://lnkd.in/d3QSE3wC
↳ Tracks audio features, popularity, and metadata for thousands of songs.

5️⃣ Stack Overflow Developer Survey
↳ 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: https://lnkd.in/dMjpJ-GK
↳ Annual survey with responses from developers about tools, jobs, and salaries.

4️⃣ Chicago Crime Data
↳ 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: https://lnkd.in/dScyYgEY
↳ Reported crimes in Chicago, including type, location, and date of incident.

3️⃣ Kickstarter Projects
↳ 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: https://lnkd.in/dwzfPuBM
↳ Data on thousands of Kickstarter campaigns, including goal, category, and status.

2️⃣ Airbnb Listings
↳ 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: https://lnkd.in/d-4QHYS4
↳ Detailed listings from major cities with pricing, availability, and host info.

1️⃣ NYC Parking Tickets (2023)
↳ 𝐃𝐚𝐭𝐚𝐬𝐞𝐭: https://lnkd.in/diqKCxpG
↳ A massive dataset of issued parking tickets with fine amounts and violation types.

Remember, getting started with guided projects is completely fine, but don't rely on them completely. Avoid showcasing only guided projects; recruiters have seen them a hundred times.

Learn from these, and build your own projects! That's how you learn!

The best portfolios show initiative, not just completion

📚 𝐆𝐞𝐭 𝟏𝟓𝟎+ 𝐫𝐞𝐚𝐥 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐰𝐢𝐭𝐡 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 & 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬) 𝐢𝐧 𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐏𝐫𝐞𝐩 𝐁𝐨𝐨𝐤: https://lnkd.in/dyzXwfVp

♻️ Save it for later or share it with someone who might find it helpful!

𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 18,000+ readers here → https://lnkd.in/dUfe4Ac6

80% of Python coding interviews test these 8 skills. Most candidates just prepare for 2.

If you're preparing for a Data Analyst/Scientist interview, your Python skills will be tested beyond just syntax.

Hiring managers look for how well you clean messy data, automate tasks, and generate insights that matter.

𝐓𝐡𝐢𝐬 𝐫𝐨𝐚𝐝𝐦𝐚𝐩 𝐬𝐡𝐨𝐰𝐬 𝐰𝐡𝐚𝐭 𝐲𝐨𝐮'𝐥𝐥 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐛𝐞 𝐚𝐬𝐤𝐞𝐝 𝐭𝐨 𝐜𝐨𝐝𝐞 𝐢𝐧 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬 👇

𝟏. 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠 & 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧
Handle missing values, remove duplicates, normalize text, and convert data types efficiently.

𝟐. 𝐃𝐚𝐭𝐚 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧 & 𝐆𝐫𝐨𝐮𝐩𝐢𝐧𝐠
Perform group-bys, create pivot tables, calculate rolling sums, and summarize data with multiple aggregations.

𝟑. 𝐄𝐱𝐩𝐥𝐨𝐫𝐚𝐭𝐨𝐫𝐲 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬 (𝐄𝐃𝐀)
Detect outliers, visualize distributions, and analyze correlations - the bread and butter of every analyst.

𝟒. 𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐃𝐚𝐭𝐞𝐬 & 𝐓𝐢𝐦𝐞
Extract date components, calculate durations, and manage time series with Pandas' resampling tricks.

𝟓. 𝐃𝐚𝐭𝐚 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 & 𝐉𝐨𝐢𝐧𝐬
Merge DataFrames, handle lookups, and combine multiple files. SQL-style joins in Python.

𝟔. 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐚𝐥 & 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬
Perform regression, calculate percentage change, and detect trends from raw data.

𝟕. 𝐅𝐢𝐥𝐞 & 𝐀𝐏𝐈 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠
Read/write CSVs, parse JSONs, and automate API fetching or ETL scripts.

𝟖. 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐑𝐞𝐩𝐨𝐫𝐭𝐢𝐧𝐠
Create dashboards, plot time-series trends, and export clear, impactful reports.

Practice one real dataset per week - clean it, analyze it, and publish your notebook.

It's not about memorizing. It's about showing you can think in data.

𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 𝐦𝐲 𝐭𝐨𝐩 𝐜𝐨𝐮𝐫𝐬𝐞 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬:
↳ Intro to Python: https://lnkd.in/dFXFjA6c
↳ Data Manipulation with Pandas: https://lnkd.in/dBFPMNPv
↳ Data Cleaning in Python: https://lnkd.in/d5BDegai

𝐆𝐞𝐭 𝟏𝟓𝟎+ 𝐫𝐞𝐚𝐥 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬 (𝐰𝐢𝐭𝐡 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 & 𝐟𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬) 𝐡𝐞𝐫𝐞: https://lnkd.in/dyzXwfVp

♻️ Save this roadmap for your interview prep

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95% of data analysts make this chart readability mistake. The fix takes 10 seconds.

Stop rotating y-axis labels. Move them next to the top value instead.

𝐓𝐡𝐢𝐬 𝐭𝐢𝐧𝐲 𝐜𝐡𝐚𝐧𝐠𝐞 𝐦𝐚𝐤𝐞𝐬 𝐚 𝐦𝐚𝐬𝐬𝐢𝐯𝐞 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞:

Your audience shouldn't need to tilt their heads just to read "𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐢𝐞𝐬 𝐩𝐞𝐫 𝐦𝐨𝐧𝐭𝐡."

Yet almost everyone still rotates their y-axis labels 90 degrees.

𝐇𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐛𝐞𝐭𝐭𝐞𝐫 𝐰𝐚𝐲:

Place the label horizontally next to the top value. That's it.

𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐰𝐨𝐫𝐤𝐬:

↳ No head tilting required - readers can scan naturally
↳ Cleaner design - more white space on the left
↳ Faster comprehension - units are visible at first glance
↳ Professional polish - looks intentional, not default

The best data visualization tricks aren't complex. They're the small changes that respect your audience's time and comfort.

Your charts should communicate, not cause neck strain.

𝐈𝐦𝐚𝐠𝐞 - Morgan Depenbusch, PhD, and she has the best visualization hacks!!

♻️ Share this with someone still rotating their axis labels

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Top Excel Formulas Every Data Analyst Should Know 📊

(And why we can never ignore Excel)

Here's the truth: Every dashboard starts in a spreadsheet. Every report. Every dataset. 

It's the foundation that teaches you data logic before you write your first query or script. The pivot to SQL and Python will be way smoother!

𝐘𝐨𝐮𝐫 𝐄𝐱𝐜𝐞𝐥 𝐅𝐨𝐫𝐦𝐮𝐥𝐚 𝐑𝐨𝐚𝐝𝐦𝐚𝐩:

𝟏. 𝐁𝐚𝐬𝐢𝐜 𝐌𝐚𝐭𝐡 & 𝐒𝐮𝐦𝐦𝐚𝐫𝐲
→ SUM, AVERAGE, MIN, MAX, ROUND

𝟐. 𝐂𝐨𝐮𝐧𝐭𝐢𝐧𝐠 & 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥
→ COUNT, COUNTIF, SUMIF, IF

𝟑. 𝐋𝐨𝐨𝐤𝐮𝐩 & 𝐑𝐞𝐟𝐞𝐫𝐞𝐧𝐜𝐞
→ VLOOKUP, INDEX-MATCH, XLOOKUP

𝟒. 𝐓𝐞𝐱𝐭 & 𝐒𝐭𝐫𝐢𝐧𝐠
→ CONCATENATE, LEFT, RIGHT, TRIM

𝟓. 𝐃𝐚𝐭𝐚 𝐂𝐥𝐞𝐚𝐧𝐢𝐧𝐠
→ UNIQUE, SORT, FILTER, TRANSPOSE

𝟔. 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬
→ STDEV, CORREL, PERCENTILE, RANK

𝟕. 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 𝐏𝐨𝐰𝐞𝐫
→ IFERROR, SPARKLINE, GETPIVOTDATA

𝐖𝐡𝐲 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫𝐬:

Master these formulas and you'll understand data manipulation at its core. When you move to SQL, you'll recognize the logic. When you start Python, the concepts click faster.

Build one project. Don't memorize, practice. That's the key!

📚 Get 150+ real data analyst interview questions with solutions from actual interviews at top companies: https://lnkd.in/dyzXwfVp

♻️ Save this roadmap for your Excel mastery

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