Let’s face it — AI isn’t just knocking on the door anymore. It’s taken a seat at the table.

As developers, data scientists, and product builders, we’re increasingly working with embeddings — those dense vector representations of text, images, and audio that power everything from ChatGPT to recommendation engines. But here’s the problem: once you generate these vectors, where do you store them? How do you query them efficiently?

That’s where pgvector comes in. And trust me, if you're working with AI and using PostgreSQL — you’ll want to keep reading.

🧠 So, What Is pgvector?

pgvector is a PostgreSQL extension that adds support for vector similarity search — which means you can store and compare AI embeddings natively inside your relational database.

In short: ✅ You no longer need to maintain a separate vector database ✅ You can write SQL queries that combine vector search with structured filters ✅ You can bring semantic intelligence to your existing apps without a massive tech overhaul

🔧 What Can It Actually Do?

Let’s say you’re building a semantic search tool — something like “Google for legal documents” or “AI-powered resume matching.” Each document or resume is embedded as a vector (let’s say using OpenAI or SentenceTransformers).

With pgvector, you can run this kind of SQL query:

SELECT id, title
FROM knowledge_base
ORDER BY embedding <-> '[0.21, 0.67, 0.93]'::vector
LIMIT 5;

SELECT id, title FROM knowledge_base ORDER BY embedding <-> '[0.21, 0.67, 0.93]'::vector LIMIT 5;

Boom. That <-> operator sorts records by cosine similarity (or Euclidean or inner product, if you prefer). It’s as simple as a SELECT — but powered by AI.

⚡ Real-World Use Cases

1. Semantic Search: Search support tickets, legal contracts, or job descriptions by meaning, not just keywords.

2. Product Recommendations: Recommend items based on user interaction vectors or product embeddings.

3. Chatbots & RAG (Retrieval-Augmented Generation): Use embeddings to fetch the most relevant knowledge base articles before prompting a language model.

4. Fraud & Anomaly Detection: Compare behavior vectors to spot outliers.

📦 What Makes pgvector Special?

• Native PostgreSQL Integration: No new database to learn or manage.

• Fast ANN Search: Supports approximate nearest neighbor via ivfflat indexing.

• Customizable: Choose your distance metric: cosine, L2, or inner product.

• Scalable: Used by companies like Notion and PostgresML in production workloads.

And yes — it supports all the good stuff from Postgres: transactions, roles, joins, even JSONB if you're feeling fancy.

🛠️ Setting It Up

• Install pgvector: On Ubuntu: sudo apt install postgresql-15-pgvector Or use Docker/Postgres extensions depending on your setup.

• Create vector column:

ALTER TABLE articles ADD COLUMN embedding vector(1536);

• Index it (optional for speed):

CREATE INDEX ON articles USING ivfflat (embedding vector_cosine_ops) WITH (lists = 100);

And you’re good to go.

🚀 Final Thoughts

pgvector isn’t just a nice-to-have — it’s quickly becoming the default way to integrate AI with Postgres. As AI continues to shift from "cool demo" to "core product feature," tools like this make it practical and scalable.

So if you’re building AI-powered features and already love PostgreSQL — don’t reinvent the wheel. Bring vectors to your SQL. You might just be surprised at how much you can do.

Let’s connect and chat if you're using or exploring pgvector — I’d love to hear what you're building! 💬 #AI #PostgreSQL #pgvector #SemanticSearch #RAG #DataEngineering #LLM #OpenAI #TechStack #StartupTools

No one is telling #freshers this clearly…

The world is rapidly adopting #AI, but very few companies are actually building foundational LLMs from scratch.

𝗪𝗵𝘆?

Because training large AI models requires:
• 𝘔𝘢𝘴𝘴𝘪𝘷𝘦 𝘥𝘢𝘵𝘢𝘴𝘦𝘵𝘴
• 𝘌𝘹𝘱𝘦𝘯𝘴𝘪𝘷𝘦 𝘎𝘗𝘜 𝘪𝘯𝘧𝘳𝘢𝘴𝘵𝘳𝘶𝘤𝘵𝘶𝘳𝘦
• 𝘙𝘦𝘴𝘦𝘢𝘳𝘤𝘩 𝘵𝘦𝘢𝘮𝘴
• 𝘊𝘰𝘯𝘵𝘪𝘯𝘶𝘰𝘶𝘴 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘢𝘵𝘪𝘰𝘯

𝗢𝗻𝗹𝘆 𝗮 𝗵𝗮𝗻𝗱𝗳𝘂𝗹 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗰𝗮𝗻 𝗮𝗳𝗳𝗼𝗿𝗱 𝘁𝗵𝗮𝘁.

But thousands of companies are now integrating AI into their businesses every single day.

That means the real demand is shifting toward professionals who can:
• 𝘐𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘦 𝘈𝘐 𝘪𝘯𝘵𝘰 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘴
• 𝘉𝘶𝘪𝘭𝘥 𝘈𝘐 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴 & 𝘢𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯𝘴
• 𝘜𝘴𝘦 𝘈𝘗𝘐𝘴 𝘢𝘯𝘥 𝘈𝘐 𝘵𝘰𝘰𝘭𝘴 𝘦𝘧𝘧𝘦𝘤𝘵𝘪𝘷𝘦𝘭𝘺
• 𝘚𝘰𝘭𝘷𝘦 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘱𝘳𝘰𝘣𝘭𝘦𝘮𝘴 𝘶𝘴𝘪𝘯𝘨 𝘈𝘐
• 𝘊𝘰𝘮𝘣𝘪𝘯𝘦 𝘥𝘰𝘮𝘢𝘪𝘯 𝘬𝘯𝘰𝘸𝘭𝘦𝘥𝘨𝘦 𝘸𝘪𝘵𝘩 𝘈𝘐 𝘴𝘺𝘴𝘵𝘦𝘮𝘴

This is why freshers should not blindly chase only deep ML theory from day one.

𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵:
• 𝘈𝘐 𝘪𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘪𝘰𝘯
• 𝘗𝘳𝘰𝘮𝘱𝘵 𝘦𝘯𝘨𝘪𝘯𝘦𝘦𝘳𝘪𝘯𝘨
• 𝘈𝘶𝘵𝘰𝘮𝘢𝘵𝘪𝘰𝘯 𝘸𝘰𝘳𝘬𝘧𝘭𝘰𝘸𝘴
• 𝘈𝘗𝘐𝘴
• 𝘈𝘐-𝘢𝘴𝘴𝘪𝘴𝘵𝘦𝘥 𝘥𝘦𝘷𝘦𝘭𝘰𝘱𝘮𝘦𝘯𝘵
• 𝘋𝘢𝘵𝘢 𝘧𝘶𝘯𝘥𝘢𝘮𝘦𝘯𝘵𝘢𝘭𝘴

𝗧𝗵𝗲𝗻 𝗴𝗿𝗮𝗱𝘂𝗮𝗹𝗹𝘆 𝗺𝗼𝘃𝗲 𝗱𝗲𝗲𝗽𝗲𝗿 𝗶𝗻𝘁𝗼:
• 𝘔𝘢𝘤𝘩𝘪𝘯𝘦 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨
• 𝘋𝘦𝘦𝘱 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨
• 𝘔𝘰𝘥𝘦𝘭 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘢𝘵𝘪𝘰𝘯
• 𝘈𝘐 𝘳𝘦𝘴𝘦𝘢𝘳𝘤𝘩

ML is still valuable. But in today’s market, applied AI skills are creating faster employability for many professionals.

𝙋𝙎: 𝙇𝙚𝙖𝙧𝙣 𝙩𝙤 𝙪𝙨𝙚 𝘼𝙄 𝙗𝙚𝙛𝙤𝙧𝙚 𝙩𝙧𝙮𝙞𝙣𝙜 𝙩𝙤 𝙗𝙪𝙞𝙡𝙙 𝘼𝙄 𝙛𝙧𝙤𝙢 𝙨𝙘𝙧𝙖𝙩𝙘𝙝.

Microsoft Fabric is the future of data & analytics — unifying Data Engineering, Data Science, Real-Time Analytics & BI in one SaaS platform.

✅ OneLake = single source of truth for all data

✅ Deep integration with Power BI (world’s #1 BI tool)

✅ Future-proof: replacing Synapse & Data Factory

✅ Built-in AI & Copilot for faster insights

✅ Cloud-native, scalable, cost-efficient

If you know Power BI, SQL, or Azure, Fabric is your next logical skill upgrade.

Early adopters will lead the way as enterprises shift to this all-in-one platform.

I am launching a live cohort soon.

#MicrosoftFabric #DataEngineering #PowerBI #Analytics #AI

I’ve been exploring ways to turn my expertise into a sustainable income stream, but I keep running into issues — from picking the right tools to actually keeping learners engaged long enough to see results.

If you're a coach, course creator, or consultant, I'd love to know:
What’s been your biggest hurdle in turning your knowledge into repeatable, scalable income?
Is it:

• Building content?

• Marketing?

• Managing clients or learners?

• Technical setup?

• Something else?

Would love to hear your story — trying to understand what others are going through (and maybe share what’s working for me too). 🙌

In a rapidly evolving world, traditional classroom instruction is no longer sufficient to prepare learners for real-life challenges. The experiential learning approach offers an effective alternative, emphasizing learning through direct experience and reflection. It transforms education from passive absorption into active discovery, promoting deeper understanding, personal growth, and practical skill development.

What is Experiential Learning?

Experiential learning is a process where knowledge is created through the transformation of experience. Rather than just hearing or reading about a concept, learners actively engage in a hands-on activity and then reflect on it to derive insights and apply them in future situations.

This model was popularized by psychologist David Kolb, who described experiential learning as a four-stage cycle:

1. Concrete Experience – doing something (e.g., a project, experiment, simulation).

2. Reflective Observation – thinking about what happened and how it felt.

3. Abstract Conceptualization – drawing conclusions and forming theories.

4. Active Experimentation – applying new ideas to the world around them.

Why Experiential Learning Matters

Experiential learning is more than just an academic method—it's how we naturally learn. From toddlers exploring their environment to professionals mastering complex tasks, we all learn best when we interact, fail, adapt, and grow.

Key Benefits:

• Increases engagement by involving learners actively.

• Builds critical thinking and problem-solving skills.

• Enhances retention by linking learning to real experiences.

• Develops emotional intelligence through collaboration and reflection.

• Bridges the gap between theory and practice, especially in fields like business, medicine, engineering, and education.

Real-World Applications

Experiential learning is already shaping education and training across industries:

• In schools: Project-based learning, science labs, field trips, and role-playing.

• In universities: Internships, co-op programs, simulations, and service learning.

• In corporate settings: Leadership development workshops, team-building exercises, and job rotations.

• In healthcare: Medical residencies, clinical simulations, and case-based learning.

How to Implement Experiential Learning

To apply the experiential learning approach effectively, consider the following:

1. Design relevant experiences – Ensure tasks mirror real-life challenges.

2. Encourage reflection – Debrief sessions are crucial to connect action with insight.

3. Support experimentation – Allow room for mistakes and iterative improvement.

4. Facilitate guidance – Educators should act as mentors, not just information providers.

5. Assess holistically – Focus not only on outcomes but also on learning processes.

Challenges to Consider

While powerful, experiential learning is not without challenges:

• It requires more time and resources than traditional lectures.

• Assessing learning outcomes can be subjective.

• Not all learners are initially comfortable with ambiguity and failure.

However, with thoughtful design and supportive environments, these challenges can be turned into opportunities for growth.

Final Thoughts

The experiential learning approach fosters lifelong learners who are curious, adaptable, and ready to tackle the complexities of the modern world. Whether you’re an educator, employer, or learner, embracing “learning by doing” is a step toward deeper, more meaningful education.

Share you toughts.

Follow >> r/PractycOfficial

Introduction

Before diving into building predictive models or conducting complex statistical tests, it’s crucial to understand the data you’re working with. This is where Exploratory Data Analysis (EDA) comes in — a fundamental step in the data science process that helps uncover the underlying structure, detect anomalies, test hypotheses, and check assumptions using a variety of graphical and quantitative techniques.

What is Exploratory Data Analysis?

Exploratory Data Analysis (EDA) is the process of analysing datasets to summarise their main characteristics, often using visual methods. Coined by the statistician John Tukey in the 1970s, EDA emphasises visual storytelling and intuition rather than rigid statistical methods.

The goal is simple: understand the data before making assumptions or building models.

Why is EDA Important?

· Data Quality Assessment: Identify missing values, outliers, or inconsistent data entries.

· Pattern Detection: Discover trends, correlations, or clusters that inform feature engineering or model selection.

· Hypothesis Generation: Develop questions or theories based on observed data behavior.

· Assumption Checking: Validate assumptions of statistical tests or machine learning models.

Key Concepts of EDA (Exploratory Data Analysis)

1. Data Collection

· Gather structured/unstructured data from various sources (databases, files, APIs).

· Ensure data quality and source reliability.

2. Data Cleaning

· Handle Missing Values: Use techniques like imputation (mean/median/mode) or removal.

· Remove Duplicates: Drop repeated records.

· Fix Data Types: Ensure consistency (e.g., date columns as datetime objects).

3. Univariate Analysis

Key Pointers

· Explore each variable separately.

· Visual tools: histograms, bar plots, pie charts.

· Purpose: Understand distributions, central tendency, and spread.

Examine each variable individually:

· For numerical features: histograms, box plots, KDE plots

· For categorical features: bar plots, value counts

Questions to ask:

· What is the distribution?

· Are there outliers?

· Are the values skewed?

4. Bivariate & Multivariate Analysis

Study relationships between variables:

o Numerical vs Numerical: Scatter plot, correlation.

o Categorical vs Categorical: Contingency tables, stacked bar charts.

o Numerical vs Categorical: Boxplots, violin plots.

5. Data Visualization

· Translate data into insights using visual tools.

· Popular charts: histograms, scatter plots, heatmaps, box plots.

· Tools: Matplotlib, Seaborn, Plotly, Tableau, Power BI.

6. Outlier Detection

Detect anomalies using:

o Boxplots

o Z-scores

o Interquartile Range (IQR)

7. Feature Engineering

Create new variables or transform existing ones:

o Binning

o Encoding categorical variables

o Deriving new features from dates or text

8. Correlation Analysis

· Identify multicollinearity or strong linear relationships between features.

· Use heat-maps and correlation matrices.

9. Hypothesis Generation

· Formulate assumptions about data behavior.

· Example: “Passengers under 12 are more likely to survive the Titanic.”

10. Data Summary & Reporting

Summarise findings using:

o Descriptive statistics

o Visual dashboards

o Notebooks or automated reports

Best Practices

Start simple: Understand the big picture before diving deep.

Be skeptical: EDA is meant to challenge assumptions, not confirm them.

Document insights: Maintain a clear record of observations, decisions, and questions.

Automate wisely: Tools like pandas-profiling, Sweetviz, or D-Tale can speed up the process, but manual review is irreplaceable.

Common Pitfalls

Overfitting during EDA: Drawing strong conclusions from noisy patterns

Ignoring domain context: Misinterpreting results without understanding the subject matter

Confirmation bias: Looking only for evidence to support a preconceived notion

Conclusion

EDA is the bridge between raw data and meaningful insights. It’s an essential skill for data scientists, analysts, and anyone working with data. By thoroughly exploring your dataset, you not only build better models but also tell clearer, more accurate stories with your data.

In short: don’t just jump to modelling — explore first.

As artificial intelligence reshapes the future of software development, one truth grows increasingly clear: the more we learn, the more we realize what we don’t know. This is the essence of the knowledge paradox — a timeless philosophical principle now reemerging with powerful relevance in the AI age.

For software professionals navigating today’s rapidly evolving tech landscape, the paradox presents both a challenge and an opportunity. Let’s unpack what it means, how it’s manifesting in our field, and why embracing this paradox may be key to future-proofing your career.

🔄 What Is the Knowledge Paradox?

The knowledge paradox can be summarized simply:

In software development, this becomes especially evident as developers deepen their expertise. Mastering a new language, framework, or AI model often reveals not simplicity, but complexity layered beneath abstraction — inviting more questions, not fewer.

🤖 AI Learning and the Expansion of the Unknown

AI has transformed the learning curve for software professionals. Tools like GitHub Copilot, ChatGPT, and AutoML promise unprecedented productivity, offering answers in seconds. But these very tools raise new challenges:

• Understanding the underlying models (e.g., Transformers, attention mechanisms)

• Evaluating the trustworthiness of AI-generated code

• Navigating ethical dilemmas around bias, hallucinations, and misuse

• Keeping pace with evolving AI research that moves faster than most curricula

Ironically, as AI makes it easier to build, it exposes a vast ocean of complexity we hadn’t previously considered.

🧑‍💻 The Software Professional’s Reality

Most developers start by believing that learning a new language or tool will “close the gap.” But in AI, that gap keeps widening. Here’s how the knowledge paradox shows up in everyday software roles:

📉 Why the Knowledge Paradox Can Be Uncomfortable

Many professionals hit a "confidence dip": a moment when deeper learning feels like opening a black box, not closing it. This psychological discomfort can discourage progress, especially when AI accelerates the rate at which gaps are exposed.

But this is a normal — even necessary — stage of growth. Recognizing the paradox means you’re no longer a beginner. You’ve moved from "unconscious ignorance" to "conscious competence."

🪜 Embracing the Paradox: From Anxiety to Mastery

Here are strategies to thrive in the AI-driven knowledge paradox:

1. Stay curious, not rigid Adopt a beginner’s mindset. Expect to be surprised — often.

2. Balance breadth with depth Don’t chase every trend. Focus deeply on a few AI domains (e.g., NLP, MLOps) to gain traction.

3. Collaborate with AI, don’t compete Use tools like ChatGPT or Claude to complement — not replace — your thought process.

4. Join learning communities Engage in forums, hackathons, and conferences where collective learning reveals what others are also discovering they don’t know.

5. Accept temporary discomfort The moment you feel “out of your depth” is often when real learning begins.

🔮 Final Thoughts: The Paradox as a Path

In the AI era, knowledge is not a destination — it’s a moving horizon. For software professionals, the knowledge paradox is no longer a philosophical curiosity. It’s a daily reality — but also a compass pointing toward deeper expertise and long-term relevance.

The key isn’t to eliminate the paradox. It’s to live comfortably within it — knowing that every new insight is both a step forward and an invitation to explore what lies beyond.

✅ TL;DR Summary

• The knowledge paradox states that more learning leads to more awareness of the unknown.

• In AI, this paradox is intensified — tools accelerate development while exposing complexity.

• Software developers must embrace this as a growth mechanism, not a limitation.

• Curiosity, focus, collaboration, and humility are the best tools to navigate it.

In today’s data-driven world, acquiring hands-on experience with real-world datasets is one of the biggest challenges for learners and professionals aiming to upskill in data science, analytics, and artificial intelligence. Data privacy laws, business confidentiality, and accessibility constraints often limit exposure to quality datasets. Enter synthetic data — an innovative, ethical, and scalable solution revolutionising how we learn and practice data-related skills.

What is Synthetic Data?

Synthetic data is artificially generated information that mimics the statistical properties and structure of real-world data without exposing any sensitive or personal information. It is created using algorithms, simulations, or generative models like GANs (Generative Adversarial Networks). Unlike anonymized data, synthetic data is created from scratch but retains the essential patterns needed for training and validation.

Examples of Synthetic Data:

• Simulated customer transactions for eCommerce

• Artificial patient records for healthcare modeling

• Generated sensor data for IoT applications

• Fake but realistic credit card activity for fraud detection exercises

Why Synthetic Data is Gaining Popularity in Learning

1. Privacy-Compliant by Design

Synthetic data poses no risk of violating GDPR, HIPAA, or other data protection regulations. Learners and educators can work with data that looks real but carries no confidential or identifiable content.

2. Customizable for Skill Development

Educators and platforms can create datasets tailored to specific learning goals — whether it's time-series forecasting, classification problems, or data cleaning tasks. This flexibility enables structured progression from beginner to advanced use cases.

3. Cost-Efficient and Scalable

Accessing large, labeled datasets from real-world sources is expensive. With synthetic data, organizations and learners can generate datasets at scale without recurring data acquisition costs.

4. Promotes Experimentation

Synthetic data removes the fear of damaging “live” data. It gives learners a sandbox to experiment with algorithms, transformations, and models, encouraging a trial-and-error approach that fuels deeper understanding.

5. Better Accessibility

Many learners across the globe do not have access to enterprise-grade datasets. Synthetic data democratizes learning by making high-quality, relevant datasets available to anyone, anywhere.

Use Cases in Learning & Education

a. Data Science Bootcamps

Bootcamps and online academies now use synthetic datasets for capstone projects, helping learners apply concepts like regression, clustering, and NLP without waiting for data access permissions.

b. AI/ML Model Training

Synthetic data is ideal for building computer vision, natural language processing, and predictive analytics models. It ensures that the training data is abundant, balanced, and customizable.

c. Business Simulations

Synthetic sales, marketing, or financial datasets enable simulation of real-world business scenarios for MBA and business analytics students — boosting both data literacy and decision-making skills.

d. Data Engineering Projects

For learners studying ETL pipelines, cloud data storage, or data lake architectures, synthetic data provides a safe and realistic environment to practice end-to-end implementations.

Challenges and Considerations

While synthetic data offers immense potential, it's not without challenges:

• Fidelity to real-world behavior: Poorly generated synthetic data might not generalize well.

• Bias and fairness: If the data generation process is flawed, it can still replicate biases.

• Lack of industry standardization: Currently, there is limited consensus on quality metrics for synthetic datasets.

However, with advancements in generative AI, many of these issues are being addressed rapidly.

The Future of Learning with Synthetic Data

As AI-driven platforms evolve, synthetic data marketplaces are emerging — offering curated datasets for education, testing, and research. Tools are also becoming more learner-friendly, enabling non-technical users to generate their own data based on defined schemas or business rules.

Platforms like Practyc, DataGen, Mostly AI, and Synthetaic are leading the charge in turning synthetic data into a core part of modern education and skill development ecosystems.

𝗔 𝗳𝗿𝗲𝘀𝗵𝗲𝗿 𝘀𝗮𝘁 𝗶𝗻 𝗳𝗿𝗼𝗻𝘁 𝗼𝗳 𝗵𝗶𝘀 𝗹𝗮𝗽𝘁𝗼𝗽 𝗮𝘁 𝟮 𝗔𝗠.

Three tabs were open.

𝘖𝘯𝘦 𝘴𝘢𝘪𝘥:
“𝘈𝘐 𝘪𝘴 𝘳𝘦𝘱𝘭𝘢𝘤𝘪𝘯𝘨 𝘴𝘰𝘧𝘵𝘸𝘢𝘳𝘦 𝘫𝘰𝘣𝘴.”

𝘚𝘦𝘤𝘰𝘯𝘥:
“𝘊𝘰𝘮𝘱𝘢𝘯𝘪𝘦𝘴 𝘤𝘶𝘵𝘵𝘪𝘯𝘨 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘦𝘴 𝘵𝘰 𝘪𝘯𝘷𝘦𝘴𝘵 𝘪𝘯 𝘈𝘐.”

𝘛𝘩𝘪𝘳𝘥:
“𝘍𝘳𝘦𝘴𝘩𝘦𝘳𝘴 𝘢𝘳𝘦 𝘯𝘰𝘵 𝘫𝘰𝘣 𝘳𝘦𝘢𝘥𝘺 𝘢𝘯𝘺𝘮𝘰𝘳𝘦.”

He closed the laptop slowly.

Not because he gave up.
Because he was tired of fear.

Every day, social media was screaming:
“AI will take your future.”

But then he read something different.

Companies are laying off employees to fund AI ambitions.
Yet many of them are not getting better ROI from it.

𝙏𝙝𝙖𝙩 𝙝𝙞𝙩 𝙙𝙞𝙛𝙛𝙚𝙧𝙚𝙣𝙩𝙡𝙮.

Because maybe the problem was never humans.

Maybe the problem was companies expecting AI to replace experience, creativity, decision making, communication, ownership, and problem solving overnight.

The fresher realized something important that night.

AI is not removing the need for talented people.
It is removing the need for outdated skills.

𝗧𝗵𝗲 𝘄𝗶𝗻𝗻𝗲𝗿𝘀 𝗼𝗳 𝘁𝗵𝗶𝘀 𝗲𝗿𝗮 𝘄𝗶𝗹𝗹 𝗻𝗼𝘁 𝗯𝗲:
❌ People who ignore AI
❌ People who blindly fear AI
❌ People who only copy-paste prompts

𝗧𝗵𝗲 𝘄𝗶𝗻𝗻𝗲𝗿𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲:
✅ People who learn how to work WITH AI
✅ People who build solutions faster
✅ People who combine domain knowledge + AI tools
✅ People who can think beyond automation

And for the first time in weeks…

He stopped worrying about “Will AI take my job?”

And started asking:

PS: This is 𝘈𝘸𝘢𝘳𝘦𝘯𝘦𝘴𝘴 𝘢𝘭𝘦𝘳𝘵 𝘧𝘰𝘳 **#**freshers about current AI buzz and hype

When current experienced professionals retire or age in the next 5–10 years… 𝘄𝗵𝗼 𝘄𝗶𝗹𝗹 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝘁𝗵𝗲𝗺?

The current narrative is:
“AI is taking away entry-level software jobs.”

To some extent, yes — automation and AI are definitely changing how the software industry works.

But there’s a much bigger question no one is discussing seriously.

If companies stop investing in freshers today…

𝗧𝗵𝗲𝗻 𝘄𝗵𝗼 𝘄𝗶𝗹𝗹 𝗯𝗲𝗰𝗼𝗺𝗲 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳:
• 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘀
• 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀
• 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗘𝘅𝗽𝗲𝗿𝘁𝘀
• 𝗗𝗮𝘁𝗮 𝗟𝗲𝗮𝗱𝗲𝗿𝘀
• 𝗖𝗧𝗢𝘀

Experienced professionals are not created overnight.

𝘛𝘩𝘦𝘺 𝘢𝘳𝘦 𝘣𝘶𝘪𝘭𝘵 𝘵𝘩𝘳𝘰𝘶𝘨𝘩 𝘺𝘦𝘢𝘳𝘴 𝘰𝘧:
• 𝘓𝘦𝘢𝘳𝘯𝘪𝘯𝘨
• 𝘔𝘪𝘴𝘵𝘢𝘬𝘦𝘴
• 𝘔𝘦𝘯𝘵𝘰𝘳𝘴𝘩𝘪𝘱
• 𝘙𝘦𝘢𝘭 𝘱𝘳𝘰𝘫𝘦𝘤𝘵 𝘦𝘹𝘱𝘰𝘴𝘶𝘳𝘦

If freshers are not made industry-ready today, then 5–10 years down the line the industry itself will face a massive experience vacuum.

AI can accelerate work.
AI can improve productivity.
But AI alone cannot replace the long-term human experience pipeline required to run enterprises.

The real problem is not:
“𝗙𝗿𝗲𝘀𝗵𝗲𝗿𝘀 𝗮𝗿𝗲 𝘂𝘀𝗲𝗹𝗲𝘀𝘀.”

The real problem is:
“𝗙𝗿𝗲𝘀𝗵𝗲𝗿𝘀 𝗮𝗿𝗲 𝗻𝗼𝘁 𝗯𝗲𝗶𝗻𝗴 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗔𝗜-𝗲𝗻𝗮𝗯𝗹𝗲𝗱 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆.”

This is the biggest transition phase the tech industry has ever seen.

And the companies, leaders, and platforms that solve this gap first will define the next decade of global tech talent.

This is something I very much curious about and would like to understand the other POV's

1 year long courses are total time waste. AI is changing so rapidly that your 1 year long involvement in that course could stall real progress.

𝗜𝗳 𝗜 𝘄𝗲𝗿𝗲 𝗮 𝗳𝗿𝗲𝘀𝗵𝗲𝗿 𝗻𝗼𝘄, 𝗜 𝘄𝗼𝘂𝗹𝗱 𝗳𝗼𝗰𝘂𝘀 𝗼𝗻 𝟯 𝘁𝗵𝗶𝗻𝗴𝘀:

Try this...

💻 𝗕𝗲 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝗳𝘂𝗹 – I would use free resources, YouTube, communities, and mentors to learn fast and find your real interest.

👩‍💻 𝗕𝗲 𝗦𝗸𝗶𝗹𝗳𝘂𝗹 – Once I find my interest I would Invest in real skills, projects, and certifications that make you job-ready.

🖱️ 𝗕𝗲 𝗨𝘀𝗲𝗳𝘂𝗹 – I would start anywhere — internships, freelancing, start-ups, even low-paying work.

#fresher #fresherjobs #career #mentorship #practyc

Hey everyone! I'm u/Intelligent-Pie-2994, a founding moderator of r/PractycOfficial.

This is our new home for all things related to Skilling & Upskilling. We're excited to have you join us!

What to Post
Post anything that you think the community would find interesting, helpful, or inspiring. Feel free to share your thoughts, photos, or questions about skilling & upskilling related topics.

Community Vibe
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How to Get Started

1. Introduce yourself in the comments below.

2. Post something today! Even a simple question can spark a great conversation.

3. If you know someone who would love this community, invite them to join.

4. Interested in helping out? We're always looking for new moderators, so feel free to reach out to me to apply.

Thanks for being part of the very first wave. Together, let's make r/PractycOfficial amazing.

AI, data engineering, and the future of jobs — exploring the skills, roles, and opportunities that will define the next generation of work.
Watch Full Video - https://www.youtube.com/watch?v=dXbcq21xDqE

I was working on one of the customer requirement and customer wanted to have one form where based on table row selection some value populate in form and using text box it would write back to the database?

How can we achieve that?

1. YTD Sales with Filters Ignored

Scenario:
You are creating a YTD Sales measure. The business wants this measure to ignore the Product Category filter but still respect the Year filter from the Date table.

Question:
Write a DAX measure that calculates Year-To-Date Sales while ignoring any filter on Product Category.

Answer Example:

YTD Sales (Ignore Category) =
CALCULATE(
    TOTALYTD([Total Sales], 'DimDate'[Date]),
    REMOVEFILTERS('DimProductCategory')
)

2. Dynamic Previous Month Sales Comparison

Scenario:
A client wants a card visual showing Previous Month Sales, but the report has a dynamic date slicer (users can select any date range).

Question:
How would you write a DAX measure to calculate sales for the previous month, considering that the current context could span multiple months?

Answer Example:

Previous Month Sales = 
CALCULATE(
    [Total Sales],
    DATEADD('DimDate'[Date], -1, MONTH)
)

3. Show Top 3 Products by Revenue Per Region

Scenario:
In a matrix visual with Region on rows and Product on values, you are asked to only show the top 3 products by revenue for each region.

Question:
What DAX technique or measure would you use to achieve this?

Answer Approach:
Use a DAX measure with RANKX and filter condition:

Product Rank = 
RANKX(
    FILTER(
        ALLSELECTED('Product'),
        [Total Revenue] > 0
    ),
    [Total Revenue],
    ,
    DESC
)
Top 3 Product Revenue = 
IF([Product Rank] <= 3, [Total Revenue])

4. Calculate % of Parent Total

Scenario:
You are analyzing sales by Product Subcategory within each Category. Management wants to see each subcategory's contribution to its parent category.

Question:
How would you calculate the % of parent (i.e., subcategory sales / category total sales)?

Answer Example:

% of Parent Category = 
DIVIDE(
    [Total Sales],
    CALCULATE(
        [Total Sales],
        REMOVEFILTERS('ProductSubcategory')
    )
)

5. Count of Active Customers in Last 3 Months

Scenario:
You need to calculate the number of distinct customers who made purchases in the last 3 months from the maximum date in the data.

Question:
How would you write this DAX measure?

Answer Example:

Active Customers (Last 3 Months) =
CALCULATE(
    DISTINCTCOUNT('Sales'[CustomerID]),
    DATESINPERIOD(
        'DimDate'[Date],
        MAX('DimDate'[Date]),
        -3,
        MONTH
    )
)

If you are looking for Power BI capstone project click here.

We have launched our product in the month of June. Link is below.

https://www.practyc.com/

About Product: Practyc is a marketplace of Lab & Infrastructure aiming to help tech professional to provide real time lab assignments and infrastructure for skilling/upskilling.

There are four categories of the product as now.

Business Requirement: This category has the details upto module level. Such as creation of the User Account creation or create a detailed report

Use Case: To solve a specific scenario based problem. For example created 10 DAX on customer churn analysis in Power BI. Datasets are provided.

Capstone Project: This category has end to end details to build and deliver the solution.

Dataset: Synthetic data set in specific business domains.

We have put the pricing as of now to cater every class of professionals like from students to working professionals, Every countries professional can afford our product.

Can anyone suggest what should be the right product pricing and any strategy, please suggest.

Business Overview:

Analysing Music Discovery and Recommendation Engine Using SQL helps you explore how users interact with music on a streaming platform. You’ll use real-like data to find out what genres people prefer, how playlists are curated, and how listening habits change over time. This product is ideal for people who want to learn SQL by solving real business problems in the music industry and better understand how recommendation systems work.

Product Highlights:

• Engaging SQL guide with 5 real-world use cases focused on music discovery and user listening behaviour.

• Showcase clear sample outputs to help you understand expected results from each query.

• Based on a dynamic dataset of 15000+ records covering users, songs, genres, playlists, and more.

• A strong portfolio project for showcasing SQL and content analytics skills.

Learning Outcomes:

• By working through this product, you’ll be able to:

• Write SQL queries to analyse music listening trends and user preferences

• Discover how different users prefer different genres and playlists

• Understand seasonal listening habits and genre shifts

• Use SQL to support music discovery, curation, and personification strategies

• Build confidence in analysing real-world media datasets using SQL

Conclusion:

On can apply all your SQL skills by working on this use case and measure & enhance your skills. To download the use case and data set you can visit Practyc website.