Open Forem: Philemon Adaghe

Navigating the New AI Epoch: DeepMind’s Strategy in the Race to AGI

Philemon Adaghe — Wed, 24 Dec 2025 16:55:53 +0000

In the global sprint toward Artificial General Intelligence, every passing month matters.

At Google DeepMind’s London labs, this race feels like a cinematic thriller unfolding in real time. Demis Hassabis, DeepMind’s co-founder and CEO, sees AGI as “the ultimate scientific tool to unlock the secrets of the universe,” a quest not just for tech supremacy, but to crack fundamental problems of science and society. With rival nations & companies pouring billions into AI’s development, DeepMind stands at the forefront, driven by an urgency that the future of humanity is on the line.

DeepMind’s strategy is as audacious as it is calculated. They’re pursuing a dual-engine approach: 50% pure innovation, 50% sheer scaling. In practice, this means one engine revs up massive models and infrastructure, while the other crafts new algorithms and breakthroughs, betting that true AGI demands both bigger machines & smarter ideas. This balanced “scale plus science” formula is DeepMind’s competitive edge. Backed by Google’s immense computing might, DeepMind has built a formidable moat by tightly integrating world-class research, engineering excellence, & cutting-edge infrastructure. It’s a rare combination that enables them to transform never-solved puzzles into practical AI systems, & it may be what propels them past the finish line in the AGI race. The message is clear: simply throwing more GPUs at the problem isn’t enough; you need visionary science and robust engineering in equal measure.

The stakes? Nothing less than civilization-altering. This isn’t just a tech story; it’s a crossroads for our species. The arrival of AGI will be a “pivotal civilizational inflection point,” a moment of promise and peril. If guided responsibly, advanced AI could become our greatest ally, supercharging progress in medicine, climate solutions, & education, ushering in an era of “incredible productivity” and “radical abundance.”

But misaligned or misused, it could trigger disruptions and ethical dilemmas unprecedented in scope. We are, in real time, writing the origin story of a new era, one that will redefine work, power, & what it means to be human. It’s no wonder DeepMind frames its mission in epochal terms: a responsibility to ensure that AI serves humanity, not the other way around.

And so, the world watches: in boardrooms & research labs, from Silicon Valley to Beijing, leaders recognize that whoever shapes AGI will shape the future. DeepMind’s bold strategy, its triumphs & trials, will reverberate far beyond tech – this is history’s next chapter. Now is the time to pay attention. Dive into our documentary-style deep-dive to see how Google DeepMind is racing to bring about, & safely harness, the most significant technological revolution of our lifetime.

hashtag

hashtag#AGI hashtag
hashtag#AIethics hashtag
hashtag#ArtificialIntelligence hashtag
hashtag#DeepMind hashtag
hashtag#FutureOfWork hashtag
hashtag#TechLeadership hashtag
hashtag#AIstrategy hashtag
hashtag#AIresearch hashtag
hashtag#HumanityAI

Ai

Philemon Adaghe — Wed, 24 Dec 2025 16:40:00 +0000

I am sometimes thrown into the uncomfortable position of being asked to tell students how scary the future is with AI. How there won’t be jobs, for example.

I won’t do it. Not because I am unconcerned. Quite the opposite. But because students need optimism and a sense of agency in dealing with future issues.

The future is not set, and we shouldn’t pretend we know what’s going to happen. More importantly, trained helplessness is more damaging than any AI, or climate, or whatever catastrophe you think awaits.

A startup leader doesn’t tell their employees their business doesn’t have a great chance of succeeding, or that whatever they do the forces of the world will undermine their work. Chances are that company leader would be correct based on the success rates of startups.

Hope and optimism are key ingredients of passion. Of agency. Let’s not scare students. Let’s teach them how to deal with challenges. I see too much doom and gloom, and that really does rub off on students.

✅ *Power BI DAX Basics

Philemon Adaghe — Fri, 12 Dec 2025 00:14:23 +0000

✅ Power BI DAX Basics 📊🧠

DAX (Data Analysis Expressions) is the formula language in Power BI. It's used to create custom calculations, measures, and calculated columns.

1️⃣ SUM()

Adds up values from a column

Example:

Total Sales = SUM(Sales[Amount])

2️⃣ AVERAGE()

Calculates the average of a column

Example:

Avg Salary = AVERAGE(Employee[Salary])

3️⃣ COUNT(), COUNTA(), COUNTROWS()

COUNT() – counts numeric values
COUNTA() – counts non-blank values
COUNTROWS() – counts rows in a table Examples: Total Orders = COUNT(Orders[OrderID]) Total Customers = COUNTROWS(Customers)

4️⃣ CALCULATE()

Modifies the context of a calculation

Example:

Sales East = CALCULATE(SUM(Sales[Amount]), Sales[Region] = "East")

5️⃣ FILTER()

Creates a filtered table for use inside CALCULATE

Example:

High Sales = CALCULATE(SUM(Sales[Amount]), FILTER(Sales, Sales[Amount] > 1000))

6️⃣ IF()

Adds logic to DAX

Example:

Sales Category = IF(Sales[Amount] > 500, "High", "Low")

7️⃣ SWITCH()

Simpler alternative to nested IFs

Example:

Rating = SWITCH(TRUE(),
 [Score] >= 90, "A",
 [Score] >= 75, "B",
 [Score] >= 60, "C",
 "Fail"
)

8️⃣ ALL()
Removes filters from a column or table

Example:

% of Total = DIVIDE(SUM(Sales[Amount]), CALCULATE(SUM(Sales[Amount]), ALL(Sales)))

9️⃣ DISTINCT()

Returns unique values in a column

Example:

Unique Products = DISTINCT(Sales[Product])

🔟 Measures vs Calculated Columns

Measure: Calculated based on report context (preferred for visuals)
Calculated Column: Stored in the data model, row-by-row logic

💬

The Algorithm Apocalypse is Already Here

Philemon Adaghe — Mon, 11 Aug 2025 21:21:47 +0000

The Algorithm Apocalypse is Already Here

While you're debating ChatGPT prompts, 40% of white-collar jobs are scheduled for deletion by 2027.

Meanwhile, China's building a $170B hydropower beast in Tibet that could power the entire UK—just for AI.

The corporate extinction event isn't coming. It's happening.

📊 Learn Power BI in 14 Days:

Philemon Adaghe — Tue, 05 Aug 2025 16:04:15 +0000

📊 Learn Power BI in 14 Days:

💻 Day 1 - Introduction to Power BI
Understand what Power BI is, its components (Desktop, Service, Mobile), and use cases.

📁 Day 2 - Connecting to Data
Learn to import data from Excel, CSV, SQL Server, and Web sources.

🧹 Day 3 - Data Cleaning with Power Query
Use Power Query to remove errors, filter rows, split columns, and change data types.

📐 Day 4 - Data Transformation
Merge, append, pivot/unpivot tables to structure your data properly.

📊 Day 5 - Building Your First Report
Drag fields to visuals, create bar, pie, table, and card charts.

🎨 Day 6 - Visual Customization
Customize visuals with colors, labels, tooltips, themes, and titles.

🧠 Day 7 - Introduction to DAX
Learn basic DAX functions: SUM, AVERAGE, COUNTROWS, IF.

🧮 Day 8 - Calculated Columns & Measures
Create calculated fields to enhance data insights with DAX.

🔗 Day 9 - Data Modeling
Establish relationships between tables, understand star & snowflake schemas.

🧭 Day 10 - Filters & Slicers
Use page-level, report-level filters and interactive slicers for dynamic reports.

📆 Day 11 - Time Intelligence in DAX
Use DAX to calculate YTD, MTD, previous year, and rolling averages.

📡 Day 12 - Publishing & Sharing
Publish reports to Power BI Service, create dashboards, and share with others.

🔒 Day 13 - Row-Level Security (RLS)
Learn to restrict data access per user with RLS roles.

🚀 Day 14 - Final Project & Recap
Build a complete dashboard from scratch using real-world data!

hashtag#POWER BI hashtag#DATAANALYTICS hashtag#LEARNING hashtag#Dashboards

Business Analyst Interview Questions with Answers:

Philemon Adaghe — Fri, 01 Aug 2025 18:47:31 +0000

Business Analyst Interview Questions with Answers:

A. SQL Questions

Find the employee with the highest salary in each location

SELECT
e.Employee_Name,
d.Location,
e.Salary
FROM
Employee e
JOIN
Department d ON e.Department_id = d.Department_id
WHERE
(e.Salary, d.Location) IN (
SELECT
MAX(e2.Salary), d2.Location
FROM
Employee e2
JOIN
Department d2 ON e2.Department_id = d2.Department_id
GROUP BY
d2.Location
);

Alternate using window function:

SELECT
Employee_Name,
Location,
Salary
FROM (
SELECT
e.Employee_Name,
d.Location,
e.Salary,
RANK() OVER (PARTITION BY d.Location ORDER BY e.Salary DESC) as rnk
FROM
Employee e
JOIN
Department d ON e.Department_id = d.Department_id
) sub
WHERE rnk = 1;

Total order amount for customers who joined in the current year

SELECT
c.Customer_Name,
SUM(o.Amount) AS Total_Amount
FROM
Customers c
JOIN
Orders o ON c.Customer_id = o.Customer_id
WHERE
EXTRACT(YEAR FROM c.Join_Date) = EXTRACT(YEAR FROM CURRENT_DATE)
GROUP BY
c.Customer_Name;

B. Python Questions

Oral Topics to Prepare:

NumPy

Creating arrays: np.array(), np.zeros(), np.ones(), np.arange(), np.linspace()

Indexing and slicing: arr[1:5], arr[:, 1]

Broadcasting: Adding scalar to array, or adding arrays of different shapes

Useful functions: np.mean(), np.sum(), np.max(), np.sort()

Matplotlib

Plot types: plt.plot(), plt.bar(), plt.hist(), plt.scatter()

Customization: titles, labels, colors, legends

plt.title("Sales Trend")
plt.xlabel("Month")
plt.ylabel("Sales")
plt.legend()
plt.grid(True)

Pandas

groupby(), agg()

Filtering with loc[] and position-based with iloc[]

Merging: pd.merge(), join(), concat()

Useful functions: isnull(), fillna(), dropna()

Python code for SQL Q2 (Total order amount for current year customers)

import pandas as pd
from datetime import datetime

Sample data

customers = pd.DataFrame({
'Customer_id': [1, 2, 3],
'Customer_Name': ['Alice', 'Bob', 'Charlie'],
'Join_Date': ['2025-01-10', '2023-06-20', '2025-03-15']
})

orders = pd.DataFrame({
'Order_id': [101, 102, 103, 104],
'Customer_id': [1, 2, 1, 3],
'Order_Date': ['2025-01-11', '2023-06-21', '2025-02-05', '2025-03-20'],
'Amount': [100, 200, 150, 300]
})

Convert Join_Date to datetime

customers['Join_Date'] = pd.to_datetime(customers['Join_Date'])
orders['Order_Date'] = pd.to_datetime(orders['Order_Date'])

Filter customers who joined this year

current_year = datetime.now().year
filtered_customers = customers[customers['Join_Date'].dt.year == current_year]

Merge and group

merged = pd.merge(filtered_customers, orders, on='Customer_id')
result = merged.groupby('Customer_Name')['Amount'].sum().reset_index()

print(result)

C. Leadership Principles (Sample Answers)

Bias for Action

In my previous role, a data feed failed right before a critical monthly report deadline. I quickly investigated the source, identified a schema change in the API, and manually pulled data from a backup. I then created a temporary ETL script to fill in the gap. This quick decision allowed us to deliver the report on time.

Dive Deep

During a marketing campaign analysis, I noticed a sudden drop in conversion rates. Rather than accepting surface-level metrics, I dove into user-level data, funnel drop-off points, and traffic sources. I discovered that a change in our landing page URL had broken tracking. Fixing this brought conversion back to normal and saved future revenue loss.

Customer Obsession

A customer team struggled with our dashboard tool. I set up a 1:1 session, listened to their challenges, and built a custom version tailored to their daily tasks. This not only improved their productivity but they also became one of our strongest advocates.

D. Excel Interview Topics

Functions You Must Know:

VLOOKUP, XLOOKUP → Lookup values from other tables

INDEX + MATCH → More dynamic alternative to VLOOKUP

SUMPRODUCT → Conditional multiplications and filters

INDIRECT → Refer to dynamic ranges

TEXT, LEFT, RIGHT, MID → Clean and manipulate strings

SUMIFS, COUNTIFS, AVERAGEIFS → Aggregate with multiple criteria

Python Developer Roadmap

Philemon Adaghe — Tue, 29 Jul 2025 12:43:47 +0000

Technology should be a universal right, not a privilege. Despite rapid advancements, millions remain disconnected due to digital literacy gaps.

📂 Python Basics (Syntax, Variables, Data Types)

∟📂 Control Flow (if, loops, functions)

∟📂 Data Structures (List, Dict, Set, Tuple)

∟📂 File Handling & Exception Handling

∟📂 Object-Oriented Programming (OOP)

∟📂 Modules & Packages

∟📂 Virtual Environments & pip

∟📂 Libraries (NumPy, Pandas, Requests)

∟📂 Web Development (Flask/Django)

∟📂 APIs & JSON

∟📂 Database (SQLite, PostgreSQL)

∟📂 Testing (unittest, pytest)

∟📂 Real Projects

∟✅ Apply for Python Dev Roles

Data Analyst Interview Questions with Answers

Philemon Adaghe — Sat, 26 Jul 2025 19:05:21 +0000

Data Analyst Interview Questions with Answers

How do you perform joins in Power BI using relationships?

In Power BI, joins are handled through relationships between tables instead of traditional SQL joins. You can create relationships using the Model View, where you define one-to-one, one-to-many, or many-to-many relationships. Power BI automatically determines the best relationship based on column values, but you can modify the cardinality and cross-filter direction to control how data is connected across tables.

What are some common aggregate functions in Excel?

Aggregate functions summarize data in Excel. Common ones include:
SUM: Adds values in a range.
AVERAGE: Calculates the mean.
COUNT: Counts the number of non-empty cells.
MAX/MIN: Finds the highest and lowest values.
MEDIAN: Returns the middle value of a dataset.
STDEV: Measures data variation (Standard Deviation).
These functions are commonly used in financial analysis, data validation, and reporting.

What are DAX functions in Power BI, and why are they important?

DAX (Data Analysis Expressions) functions help create custom calculations and measures in Power BI. They are important because they allow users to perform dynamic aggregations, conditional calculations, and time-based analysis. Key categories include:
Aggregation Functions: SUM, AVERAGE, COUNT
Filter Functions: FILTER, CALCULATE
Time Intelligence Functions: DATEADD, SAMEPERIODLASTYEAR
Logical Functions: IF, SWITCH
DAX enables advanced reporting and helps build meaningful insights from raw data.

What is data normalization, and why is it important?

Data normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. It ensures efficient storage and retrieval by dividing large tables into smaller, related tables and using foreign keys to maintain relationships.

Benefits of normalization include:

Eliminates duplicate data
Improves consistency and accuracy
Enhances database performance
Reduces data anomalies

Normalization is crucial in relational databases to maintain a clean and scalable data structure.

What are some common data visualization best practices?

Effective data visualization helps communicate insights clearly. Best practices include:

Choose the right chart (e.g., bar charts for comparisons, line charts for trends).
Keep it simple (avoid unnecessary elements like 3D effects).
Use colors wisely (highlight key insights without overloading with colors).
Ensure data accuracy (labels, scales, and values must be correct).
Use interactive elements (filters, drill-downs in Power BI/Tableau).
Provide context (titles, legends, and annotations to explain findings).

Well-designed visualizations improve decision-making and help stakeholders understand data easily.

Your Daily SQL Might Work — But This Makes It Exceptional

Philemon Adaghe — Thu, 17 Jul 2025 20:11:43 +0000

If you’re a Data Analyst, chances are you use 𝐒𝐐𝐋 every single day. And if you’re preparing for interviews, you’ve probably realized that it's not just about writing queries it's about writing smart, efficient, and scalable ones.

𝐁𝐫𝐞𝐚𝐤 𝐈𝐭 𝐃𝐨𝐰𝐧 𝐰𝐢𝐭𝐡 𝐂𝐓𝐄𝐬 (𝐂𝐨𝐦𝐦𝐨𝐧 𝐓𝐚𝐛𝐥𝐞 𝐄𝐱𝐩𝐫𝐞𝐬𝐬𝐢𝐨𝐧𝐬)

Ever worked on a query that became an unreadable monster? CTEs let you break that down into logical steps. You can treat them like temporary views — great for simplifying logic and improving collaboration across your team.

𝐔𝐬𝐞 𝐖𝐢𝐧𝐝𝐨𝐰 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧𝐬

Forget the mess of subqueries. With functions like ROW_NUMBER(), RANK(), LEAD() and LAG(), you can compare rows, rank items, or calculate running totals — all within the same query. Total

𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬 (𝐍𝐞𝐬𝐭𝐞𝐝 𝐐𝐮𝐞𝐫𝐢𝐞𝐬)

Yes, they're old school, but nested subqueries are still powerful. Use them when you want to filter based on results of another query or isolate logic step-by-step before joining with the big picture.

𝐈𝐧𝐝𝐞𝐱𝐞𝐬 & 𝐐𝐮𝐞𝐫𝐲 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧

Query taking forever? Look at your indexes. Index the columns you use in JOINs, WHERE, and GROUP BY. Even basic knowledge of how the SQL engine reads data can take your skills up a notch.

𝐉𝐨𝐢𝐧𝐬 𝐯𝐬. 𝐒𝐮𝐛𝐪𝐮𝐞𝐫𝐢𝐞𝐬

Joins are usually faster and better for combining large datasets. Subqueries, on the other hand, are cleaner when doing one-off filters or smaller operations. Choose wisely based on the context.

𝐂𝐀𝐒𝐄 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬:

Want to categorize or bucket data without creating a separate table? Use CASE. It’s ideal for conditional logic, custom labels, and grouping in a single query.

𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐆𝐑𝐎𝐔𝐏 𝐁𝐘

Most analytics questions start with "how many", "what’s the average", or "which is the highest?". SUM(), COUNT(), AVG(), etc., and pair them with GROUP BY to drive insights that matter.

𝐃𝐚𝐭𝐞𝐬 𝐀𝐫𝐞 𝐀𝐥𝐰𝐚𝐲𝐬 𝐓𝐫𝐢𝐜𝐤𝐲

Time-based analysis is everywhere: trends, cohorts, seasonality, etc. Get familiar with functions like DATEADD, DATEDIFF, DATE_TRUNC, and DATEPART to work confidently with time series data.

𝐒𝐞𝐥𝐟-𝐉𝐨𝐢𝐧𝐬 & 𝐑𝐞𝐜𝐮𝐫𝐬𝐢𝐯𝐞 𝐐𝐮𝐞𝐫𝐢𝐞𝐬 𝐟𝐨𝐫 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐞𝐬

Whether it's org charts or product categories, not all data is flat. Learn how to join a table to itself or use recursive CTEs to navigate parent-child relationships effectively.

You don’t need to memorize 100 functions. You need to understand 10 really well and apply them smartly. These are the concepts I keep going back to not just in interviews, but in the real world where clarity, performance, and logic matter most.

10 Data Analyst Interview Questions You Should Be Ready For (2025) 💼📊

Philemon Adaghe — Fri, 11 Jul 2025 11:13:49 +0000

10 Data Analyst Interview Questions You Should Be Ready For (2025) 💼📊

1️⃣ Q: Explain the difference between INNER JOIN and LEFT JOIN.

✅ INNER JOIN returns only matching records between tables.

✅ LEFT JOIN returns all records from the left table + matched records from the right (NULLs if no match).

Example:

Get all customers and their orders:

INNER JOIN → shows only customers who placed orders.
LEFT JOIN → shows all customers, even those with no orders.

2️⃣ Q: What are window functions in SQL?

Window functions perform calculations across a set of rows related to the current row.

Example:

Use ROW_NUMBER() to rank sales per region without collapsing data:

sql
SELECT region, sales, ROW_NUMBER() OVER(PARTITION BY region ORDER BY sales DESC)

FROM sales_data;

3️⃣ Q: How do you handle missing or duplicate data in a dataset?

For missing data: use imputation (mean/median), drop, or flag it.
For duplicates: use .drop_duplicates() in Python or DISTINCT in SQL. Approach depends on context and business impact.

4️⃣ Q: Describe a situation where you derived insights that influenced a business decision.
Example: Analyzed user engagement data → found drop in mobile retention. Suggested UX improvements → increased retention by 15%.

5️⃣ Q: What’s the difference between correlation and causation?

Correlation = relationship (e.g., A & B move together)
Causation = A directly causes B Always validate with experiments or domain expertise.

6️⃣ Q: How would you optimize a slow SQL query?

Check for missing indexes
Avoid SELECT *
Use EXPLAIN plans
Limit subqueries
Optimize joins and filters

7️⃣ Q: Explain the use of GROUP BY and HAVING in SQL.

GROUP BY aggregates data by columns (e.g., total sales by region)
HAVING filters aggregated results Example: sql SELECT region, SUM(sales) FROM orders GROUP BY region HAVING SUM(sales) > 10000;

8️⃣ Q: How do you choose the right chart for a dataset?

Line chart → trends
Bar chart → comparison
Pie chart → proportion (rarely ideal)
Scatter plot → relationships Always match chart to message and audience.

9️⃣ Q: What’s the difference between a dashboard and a report?

Dashboard → Interactive, real-time, for quick insights
Report → Static or scheduled, detailed summaries Dashboards = action; reports = reference.

🔟 Q: Which libraries in Python do you use for data cleaning and analysis?

pandas → data manipulation
numpy → numerical ops
matplotlib / seaborn → visualization
sklearn → basic ML and preprocessing

Power BI Essentials for Data Analysts 📊⚡

Philemon Adaghe — Sat, 05 Jul 2025 14:26:49 +0000

Power BI Essentials for Data Analysts 📊⚡

If you’re starting out in Data Analytics, Power BI is one of the most in-demand tools for building dashboards and visualizing business data. Here's what you should focus on:

1️⃣ Power BI Interface:

– Learn to navigate the workspace: Fields, Visualizations, Data, and Model views.

2️⃣ Data Importing:

– Connect Excel, CSV, SQL, or web data easily.

– Clean and transform data using Power Query Editor.

3️⃣ Building Visuals:

– Use bar charts, line charts, cards, pie charts, and slicers.

– Add filters and drill-throughs for interactive dashboards.

4️⃣ DAX (Data Analysis Expressions):

– Start with basics:

• SUM()

• CALCULATE()

• FILTER()

• IF() and SWITCH()

5️⃣ Relationships & Data Modeling:

– Connect multiple tables via keys.

– Create a star schema if possible.

6️⃣ Publishing & Sharing:

– Publish to Power BI Service.

– Set up scheduled refresh and share dashboards with teams.

💡 Pro Tip:

Practice with open datasets (like Kaggle or government data) and build 2–3 mini dashboards for your portfolio.

💬 Tap ❤️ if this helped!

🎯 Step-by-Step Roadmap to Become a Data Analyst:

Philemon Adaghe — Tue, 01 Jul 2025 09:20:59 +0000

🎯 Step-by-Step Roadmap to Become a Data Analyst:

🔹 Math & Stats → Foundation for logic and data-driven decisions
🔹 Excel → Your first best friend for handling and exploring data
🔹 Python → For deeper analysis, automation, and data wrangling
🔹 SQL & Databases → Extract and manipulate data like a pro
🔹 Exploratory Analysis → Spot trends and hidden patterns
🔹 Data Preparation → Clean, transform, and validate datasets
🔹 Data Visualization → Tell compelling stories with Power BI / Tableau
🔹 Machine Learning (Bonus) → Predict, recommend, and level up your skills
🔹 Data Storytelling → Because data alone doesn’t drive change—stories do

Open Forem: Philemon Adaghe

Navigating the New AI Epoch: DeepMind’s Strategy in the Race to AGI

Ai

✅ *Power BI DAX Basics

The Algorithm Apocalypse is Already Here

*📊 Learn Power BI in 14 Days:*

Business Analyst Interview Questions with Answers:

Sample data

Convert Join_Date to datetime

Filter customers who joined this year

Merge and group

Python Developer Roadmap

Data Analyst Interview Questions with Answers

Your Daily SQL Might Work — But This Makes It Exceptional

10 Data Analyst Interview Questions You Should Be Ready For (2025) 💼📊

Power BI Essentials for Data Analysts 📊⚡

🎯 Step-by-Step Roadmap to Become a Data Analyst:

📊 Learn Power BI in 14 Days: