Analyst Builder

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Founder & CEO

Alex Analytics LLC

Consulting focused on Product Design, Marketing Strategy, and Analytics for Startups and Tech Companies.

Founder

AmerisourceBergen

Manager of Operational Analytics and Intelligence

Data Analyst II

Junior Data Analyst

KPN Advisors

Data Analyst

The Family Place

Data Collection Specialist and Analyst

Helping people around the world get jobs in Analytics and Tech!

Check out my YouTube channel Alex The Analyst for free resources and Analyst Builder for my full learning platform.

Alex Freberg

My wife and I got married over 8 years ago - since then she’s only made my life better! 

When we met I was renting a room out of a house, eating hot dogs and eggs for most meals, I was super broke, and had very little sense of style.

Unfortauntely, my style hasn’t changed much.

But she’s pushed me in so many ways over the years - ways in which I know I wouldn’t have if I was by myself. When I was by myself I was content with just surviving. 

I only had myself to take care of so I didn’t care much about my career, looks, or other things.

But she believed in me and pushed me to be more than I could have been by myself.

A good spouse will raise you up and lift you to be better. I wouldn’t be where I am today without her. Behind the scenes she’s my biggest supporter.

Founder of AnalystBuilder.com | YouTube "Alex The Analyst" | Not LinkedIn Top Voice

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If my life has taught me anything up to this point - it’s that you need to do things you don’t feel qualified for.

I didn’t feel qualified to be a Data Analyst.

I didn’t feel qualified to be a Manager.

I wasn’t qualified to teach people on YouTube.

I wasn’t qualified or have enough experience to start consulting.

Genuinely, I wasn’t qualified for any of it - I just did it and did my best. It’s taken me places and opened doors I never even knew about.

Heading out to LinkedIn Learning to record a course!

Wish me luck - they said I had to wear makeup so hopefully I’m recognizable!

This week we wrapped up our Databricks series!

We covered:
1. Importing Data from flat files and connecting to Data Sources
2. Analyzing data using SQL Editor and Notebooks
3. Visualizing and Creating Dashboards
4. Using AI to code and talk with our Databases

You can learn all of this for Free in this series using Databricks Free Edition! 

I plan on adding to the Databricks Series in the future! Check out the playlist below!

Link: https://lnkd.in/eF3XV744

Databricks.

One of the best platforms on the planet for AI and Data - and in this video we are building a full project using it!

The best part - it's completely free to use. Practice with the SQL Editor, Notebooks, AI, and Data Visualization!

https://lnkd.in/e35AB7xs

Open video: Building an End-to-End Data Project in Databricks (Free Edition) on youtube.com

Building an End-to-End Data Project in Databricks (Free Edition)

Remote Jobs are 100x harder to land than local ones.

With all the talk about the Tough Job Market - it's great to know that there are still jobs out there!

Jobs aren't impossible to find - even ones that use Excel, SQL, and Power BI.

This is just one message, but I've gotten hundreds this year saying the same thing. Jobs are out there!

One of the biggest things I've heard from people is that the jobs aren't remote. Remote jobs are 100x harder to get than local ones since almost everyone wants to work from home. But companies want people in person!

Many people who got jobs were relocating to where their new role was. so if you're only look for remote jobs, think about looking local or see if you're willing to move for work.

Put me on LinkedIn Lunatics. Attack my character and who I am as a man and a father.

I don’t care.

I feel better when I workout and no amount of shaming me is going to stop me from going to the hotel gym and doing a workout in the morning.

Yes, I forgot workout shoes and went in my socks. 

No amount of public humiliation can stop me. 

I’ve become ungovernable. 

Why did I post this on LinkedIn when it’s completely unnecessary? Because I’m not here to make friends. 

I want enemies. It makes me want to workout even more.

I don't know when or how this started, but it's become a running theme for my kids to make me look as insane and crazy in drawings as they can.

Everyone else can look perfectly normal - or even drawn great - but not me.

I hold onto as many of these drawings as I can. They make me smile every time I see one :D

At some point I just started putting :) in my emails and I haven't quit.

Is this unprofessional?

Because I can tell you right now I've easily written 1000s of emails with smileys in them and I have no plan on stopping.

Snuck into LinkedIns employee-only party to take some pictures and eat some crepes.

Having a great time in Carpinteria!

Wrapping up recording today and heading back to the east coast tomorrow 🙌

In2IT Technologies

- As a Business Analyst, conducted research, analyzed business operations, and solved the business 
  problems
- Collaborating with cross-functional teams, including product managers,  and other 
   stakeholders, to gather requirements, analyze data, and define the product roadmap
- Preparing various functional specification documents, facilitating review of the same by development 
   team
- Created detailed user stories, workflows, and acceptance criteria to ensure clear communication of business needs to development teams.
- Currently working on a Dashboard Product for a client.

Business Analyst

Octaculus

- Supported seniors in gathering and documenting requirements for product development initiatives.
- Developed process flow diagrams and use case documentation, streamlining internal processes and 
   reducing redundancies.
- Analyzed data trends and user feedback, providing actionable insights that improved product usability 
   and customer satisfaction.
- Assisted in sprint planning and backlog grooming sessions, maintaining alignment with Agile principles.
- Analyzing data for generating marketing strategies

Associate Business Analyst

DataDice Technologies

- Collect, clean, and preprocess structured and unstructured datasets.
- Conduct exploratory data analysis (EDA) to uncover trends, patterns, and insights.
- Assist in building dashboards and visualizations using tools like Power BI.
- Collaborate with cross-functional teams to understand business requirements and translate them into 
    analytical solutions.
- Support the team in automating data workflows and creating reports.
- Document findings and prepare presentations for stakeholders.

Coaching Institute

- Provided one-on-one & group tutoring sessions in Social Studies(SST) & Mathematics, helping students improve their understanding & performance

-Assisted students in exam preparation, homework, & conceptual learning, leading to improved grades & confidence

- Focused on interactive learning methods, making subjects more engaging & easier to grasp

SST Tutor

• 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞𝐬: SQL
• 𝐃𝐚𝐭𝐚 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Power BI, Seaborn, Plotly
• 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 & 𝐋𝐢𝐛𝐫𝐚𝐫𝐢𝐞𝐬: Streamlit, Pandas, NumPy, Matplotlib, Plotly, Scikit-learn.

Sachin Kumar

Most SQL beginners think DELETE, DROP, and TRUNCATE mean the same… until they break a production table.

1) DELETE:
is a Data Manipulation Language (DML) command used to remove records from a table.
It deletes specific rows based on conditions specified in WHERE clause.
DELETE does not alter the table structure; it only affects the data.
Use Case: Use DELETE when you want to selectively remove specific rows while keeping the table intact.


2) TRUNCATE:
is also a DDL command, but it focuses on removing data while preserving table structure.
It quickly removes all rows from the table without logging individual deletions.
Use Case: Use TRUNCATE when you want to clear out data efficiently without dropping the table.

3) DROP:
is a Data Definition Language (DDL) command that removes an entire table from the database.
It deletes both the table structure and all the data within it.
Irreversible: Once you DROP a table, it cannot be rolled back.
Use Case: Use DROP when you need to completely eliminate a table.


Day 68 : https://lnkd.in/gSUgzey9

#sql #database #dataanalyst #businessanalyst #productanalyst

𝐄𝐯𝐞𝐧𝐭 𝐥𝐨𝐠𝐬 𝐚𝐫𝐞 𝐧𝐨𝐢𝐬𝐲. 𝐒𝐐𝐋 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐞𝐦 𝐦𝐞𝐚𝐧𝐢𝐧𝐠𝐟𝐮𝐥. 𝐇𝐞𝐫𝐞’𝐬 𝐡𝐨𝐰 𝐲𝐨𝐮 𝐜𝐨𝐧𝐯𝐞𝐫𝐭 𝐥𝐨𝐠𝐬 → 𝐬𝐞𝐬𝐬𝐢𝐨𝐧𝐬 → 𝐰𝐨𝐫𝐤𝐢𝐧𝐠 𝐡𝐨𝐮𝐫𝐬

◾ Problem:
Employees may log in and out multiple times a day.
We must pair each login with its next logout, and then compute total working hours per day.

SQL Logic Explained (In Simple Words)

1️⃣ — Pair each login with its next logout
Using LEAD() window function, we fetch the upcoming event_time

2️⃣ — Filter only login rows
Because the logout time is only meaningful for login events.

3️⃣ — Calculate working hours
We subtract login_time from logout_time:
Converts seconds → hours
Handles multiple login-logout pairs in same day

4️⃣ — Summing total working hours per employee per day
This gives us a daily rolled-up summary.


This query is crucial for:
Payroll systems — exact payable hours
Timesheet automation — actual vs expected productivity
Workforce analytics — time-on-platform, shift performance


Day 70 of SQL: https://lnkd.in/gFQ6UgyB


#SQL #Dataanalyst #productanalyst #businessanalyst #sqlqueries

📕Day 72 of SQL Revision
New vs Repeat Customers — A Classic SQL Pattern Every Analyst Should Know

Understanding customer behavior over time is one of most common (& most important) problems in analytics.

One simple but powerful question businesses often ask is 👇
“On each day, how many customers are new and how many are repeat?”

SQL query:

Step 1: Find each customer’s first-ever order
Step 2: Get one order per customer per day --We care about daily active customers, not order volume.
Step 3: Classify customers as New or Repeat
Step 4: Daily aggregation (final output)



WITH first_order AS (
  SELECT
    cust_id,
    MIN(DATE(order_dt)) AS first_order_date
  FROM Input
  GROUP BY cust_id
),
daily_orders AS (
  SELECT DISTINCT
    cust_id,
    DATE(order_dt) AS order_date
  FROM Input
),
customer_status AS (
  SELECT
    d.cust_id,
    d.order_date,
    CASE
      WHEN d.order_date = f.first_order_date THEN 'New'
      ELSE 'Repeat'
    END AS status
  FROM daily_orders d
  JOIN first_order f
    ON d.cust_id = f.cust_id
)
SELECT
  order_date,
  COUNT(CASE WHEN status = 'New' THEN 1 END) AS new_customers,
  COUNT(CASE WHEN status = 'Repeat' THEN 1 END) AS repeat_customers
FROM customer_status
GROUP BY order_date
ORDER BY order_date;



Day 71 of SQL: https://lnkd.in/gj8SwrHk



#SQL #Database #SQLQueries #Dataanalyst #SQLQuery

𝐃𝐮𝐫𝐢𝐧𝐠 𝐣𝐨𝐛 𝐬𝐞𝐚𝐫𝐜𝐡, 𝐭𝐡𝐞𝐫𝐞 𝐚𝐫𝐞 𝐩𝐡𝐚𝐬𝐞𝐬 𝐰𝐡𝐞𝐧 𝐲𝐨𝐮 𝐚𝐩𝐩𝐥𝐲 𝐝𝐚𝐢𝐥𝐲, 𝐩𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐝𝐚𝐢𝐥𝐲, 𝐮𝐩𝐬𝐤𝐢𝐥𝐥 𝐝𝐚𝐢𝐥𝐲… 𝐛𝐮𝐭 𝐬𝐭𝐢𝐥𝐥 𝐧𝐨 𝐜𝐚𝐥𝐥𝐛𝐚𝐜𝐤𝐬, 𝐧𝐨 𝐢𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬, 𝐧𝐨 𝐫𝐞𝐬𝐮𝐥𝐭𝐬.

It’s frustrating.
It makes you doubt yourself.
It makes you feel incompatible or not good enough.

But here’s the truth most people won’t tell you:

◾ This phase is normal.
- Almost everyone who made it through faced this exact silence.

Job search is not a straight line.
 It’s effort → waiting → rejection → learning → repeat.

Sometimes it’s not about talent.
Sometimes it’s timing.
Sometimes it’s just probability.

What is in your control?
1) Showing up every day
2) Improving one skill at a time
3) Writing better queries, cleaner logic, sharper explanations
4)  Staying consistent even when motivation drops

Hard work doesn’t always give instant results — but it always compounds.
If you keep learning, keep building, keep trying — the result WILL come.
Maybe not today.
Maybe not this week.
But it will come.


So don’t quit just because the journey feels quiet.
Quiet phases are where growth actually happens.
Give your 100% effort.
Future you will thank you for it.


#Jobsearch #Jobs #Dataanalystsjobs

Day 73 of SQL Revision Series

generate_series() is a set-returning function in PostgreSQL.
This means:
It does not return a single value
It returns multiple rows
Those rows behave like a temporary / virtual table

You can use it in:
SELECT
JOIN
WHERE
GROUP BY
Subqueries

Why is it called a “virtual table”?
Because:
You don’t create a table
You don’t store data
PostgreSQL generates rows on the fly
So it’s fast and very useful for analysis.

Syntax (Very Important):
generate_series(start, stop, step)


| `start`  | First value of the series  |
| `stop`  | Last value (inclusive)   |
| `step`  | Increment / Decrement value |

Analysts Use generate_series():
1. Create Data Without Tables (Practice SQL)
2. Finding Missing Dates in Data
3. Daily / Monthly Reports

generate_series() is used to create a sequence of rows (numbers or dates) without creating a physical table, mainly for analysis and reporting.


Day 72 of SQL: https://lnkd.in/gXZEUhay


#SQL #dataanalysts #sqlqueries #database

Sometimes, It's Not Just Hard Work — Luck Also Plays a Role (And That's Okay!)

We often hear:
“Success = Hard Work + Skills.”
But rarely do people talk about the 3rd variable that silently influences every 
interview:

Luck. Yes, luck matters too.
Let me explain this with a small data-story


📊 Imagine an Interview Like a Random Dataset

You prepare SQL, Python, Product Sense, Case Studies, Stats…
 But the interviewer pulls 3 random questions from a pool of 200+ topics.

Sometimes that sample is perfectly in your favor.
 You get questions that you’ve practiced 100 times.
 You feel confident. You solve everything.
 The interviewer smiles. You smile.
 Result: You get selected.

 Other times…
 The random sample goes against you.
 You get topics you barely touched.
 A trick question comes.
 Your brain freezes for a moment.
 Even though you know you're capable, that day doesn’t reflect it.
Result: Rejection.


But the truth is:
 Your skill didn’t change.
 Only the sample of questions changed.
 Just like randomness in a dataset.



So What Should You Learn From This:
✔ Hard work still matters the most
✔ Don’t judge yourself based on one interview
✔ Rejections don’t mean you’re not good



Day 67: https://lnkd.in/gyADUWw7


#Motivation #CareerMotivation #JobSearch #TechInterview #SuccessMindset #LearningEveryday

Expanding Rows Using CROSS JOIN LATERAL + generate_series()

◾ Why do we need LATERAL: Because the function depends on the column t.n.

LATERAL tells PostgreSQL:
Evaluate this function once per row from left-hand table, & allow it to reference that row’s values."
Without LATERAL, the query either errors out or behaves incorrectly


To repeat each number n times, I used CROSS JOIN LATERAL generate_series(1,n).

LATERAL lets the function access columns from the left table, making this the most efficient and idiomatic PostgreSQL pattern for row explosion.”



“To repeat each number n times, I used CROSS JOIN LATERAL generate_series(1,n). LATERAL lets the function access columns from the left table, making this the most efficient and idiomatic PostgreSQL pattern for row explosion.”


Query:
SELECT t.n AS number_output 
FROM numbers_table t 
CROSS JOIN LATERAL generate_series(1, t.n) AS gs(i) 
ORDER BY t.n, gs.i;


This pattern is used when you want to:
1) Expand one row into multiple rows
2) Generate sequences dynamically
3) Create repetitions
4) Simulate time windows
5) Do simulations (e.g., churn per day, cohort expansion)


Day 69 of SQL: https://lnkd.in/gpA8Efaq



#sql #database #dataanalyst #productanalyst #sqlqueries

This SQL problem looks simple, but it tests how well you understand ordered data, edge cases, and real product logic


Q)Problem Statement:
We have a seat allocation table.
Seats are in order, and we want to swap students sitting in adjacent seats.

Rules:
Seat 1 ↔ 2, 3 ↔ 4, 5 ↔ 6 …
If the last seat is odd, it remains unchanged.
Do NOT change seat numbers — only swap students.

Input:
| seat_id | student |
| 1    | A    |
| 2    | B    |
| 3    | C    |
| 4    | D    |
| 5    | E    |


Query
SELECT
 seat_id,
 CASE
  WHEN seat_id % 2 = 1 THEN
   COALESCE(LEAD(student) OVER (ORDER BY seat_id), student)
  ELSE
   LAG(student) OVER (ORDER BY seat_id)
 END AS student
FROM seats;


Explanation:
Why COALESCE() is Needed
COALESCE(LEAD(student), student)
If next student exists → take it
 Else → keep current student


Day 73 of SQL: https://lnkd.in/g3NyvFtR



#SQL #WindowFunctions #ProductAnalytics #ProductAnalyst #DataAnalytics DataLemur 🐵 (Ace the SQL & Data Interview) Nick Singh 📕🐒 StrataScratch Aditya Kalyan

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