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5 SQL Queries Every Data Engineer Must Master (with Examples)

SQL has been the backbone of #DataEngineering for years. Whether you’re building pipelines, optimizing databases, or troubleshooting, mastering these concepts is crucial:

🔹 1️⃣ Aggregation and Grouping
Efficiently summarize and analyze data with key functions like SUM, COUNT, AVG, MIN, MAX, and GROUP BY.

🔹 2️⃣ Window Functions
Perform advanced analytics like rankings, running totals, and comparisons while preserving row-level detail. Learn functions like ROW_NUMBER, RANK, NTILE, LAG, LEAD, and windowed SUM.

🔹 3️⃣ Join Operations
Combine data from multiple tables using INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN, and CROSS JOIN.

🔹 4️⃣ Subqueries and CTEs
Simplify complex queries with WITH statements, or use subqueries in SELECT, FROM, and WHERE clauses to enhance readability and performance.

🔹 5️⃣ Data Cleaning and Transformation
Prepare your data with functions like DISTINCT, LOWER, UPPER, TRIM, REGEXP_REPLACE, and COALESCE to ensure high-quality outputs.

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Candlestick Patterns

Technical skills for Power BI Developer :

- Proficiency in Power BI (Desktop, Service, Mobile).

- Expertise in creating dashboards, reports, and visualizations.

- Advanced knowledge of DAX (Data Analysis Expressions).

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- Proficiency in SQL for querying and optimizing databases.

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1. Netflix Marathon = Sleep
2. Fast Food = Homemade Food
3. Toxic Friends = Caring Friends
4. TV = Exercise
5. Complaining = Gratitude
6. Overthinking = Meditation
7. Self-doubt = Journal
8. Tired Start Over
9. Jealous = Self-focus
10. Irritated = Alter Perspective

30 WAYS TO MAKE PROGRESS

1. Wake up early
2. Read daily
3. Eat well
4. Love yourself
5. Judge less
6. Be yourself
7. Set goals
8. Plan your day
9. Positive attitude
10. Have purpose
11. Find inspiration
12. Help others
13. Network
14. Save money
15. Automate
16. Delegate
17. Track finances
18. Build a brand
19. Fail fast
20. Interact
21. Learn skills
22. Invest
23. Journal
24. Meditate
25. Get a mentor
26. Think Big
27. Be productive
28. Do more
29. Spend wisely
30. Be ambitious

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Exploring the Benefits of Non-Recourse Loans in Crypto

In finance, minimizing risk is key. Non-recourse loans offer a unique way to leverage assets while limiting risk exposure strictly to the collateral.

Picture this: John wants to invest in Ethereum without selling his Bitcoin holdings. He takes a non-recourse loan, using Bitcoin as collateral. If the investment goes south, the lender can only seize the Bitcoin—not John's other assets.

Non-recourse loans are especially useful in crypto and trading due to their:

- Collateralization: Use crypto as collateral to access funds without selling assets.
- Volatility Risk Management: Limit exposure to the collateral, even in volatile markets.
- Flexibility: Ideal for leveraging investments or meeting personal expenses.

Similar to margin trading in traditional finance, non-recourse loans allow you to boost buying power while controlling risk. However, these loans can carry challenges like margin calls or liquidation.

For those in crypto and trading, understanding non-recourse loans is essential for managing financial risk and seizing growth opportunities while safeguarding assets.

Signs of Bearish Trend in Crypto: https://t.me/techpsyche/739

Cryptocurrency Investing: https://t.me/techpsyche/723

Liquidity: https://t.me/techpsyche/712

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#crypto #web3 #blockchain #finance

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Safe Superintelligence (SSI), the AI startup led by OpenAI’s co-founder and former chief scientist Ilya Sutskever, has raised an additional $2 billion in funding at a $32 billion valuation, according to the Financial Times.

The startup had already raised $1 billion, and there were reports that an additional $1 billion round was in the works.

Google for Startups Accelerator – Africa 2025

Applications are open for the Google for Startups Accelerator – Africa 2025. This is a three-month hybrid accelerator program for Seed to Series A technology startups.

Benefits:

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- Dedicated mentoring from Google teams.
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Java Developer Roadmap

10 free tools to become top level creator.

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Types Of Technical Analysis

#crypto #web3 #blockchain #finance #stocks

What is funding in crypto trading? 💸

In cryptocurrencies, funding refers to the funding rate that is redistributed among traders holding positions in perpetual futures.

Funding is a periodic payment/write-off for traders with open positions in perpetual futures, which allows them to compensate for the long-term difference between the price of the underlying asset and the derivative contract.

The need for funding arose from the idea of ​​perpetual futures, which have no maturity and can be held indefinitely. Therefore, to compensate for the difference in the price of the asset and the contract, a financing rate mechanism was launched.

Non-Recourse Loans in Crypto: https://t.me/techpsyche/756

Signs of Bearish Trend in Crypto: https://t.me/techpsyche/739

Cryptocurrency Investing: https://t.me/techpsyche/723

Liquidity: https://t.me/techpsyche/712

More Resources Here:
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#crypto #web3 #blockchain #finance #stocks

This is a very COMMON issue that I observe in the projects of aspiring candidates

They download a DATASET from Kaggle or any other website

Export it to a Data Analysis TOOL

And START the project with data cleaning

After cleaning the data, they PLUG it into a dashboard

In the dashboard, they put EVERY column into the visuals

Also they APPLY the filters of top bottom 10

Once done, they crack their KNUCKLES

And put this project in a list of SUCCESSFULLY completed projects

Over time, I have REVIEWED so many portfolio projects

And I see this ISSUES almost every time

When I go to their portfolio, for every project there is a DASHBOARD

But WHAT should I do after seeing a dashboard

What is it trying to SAY

What should I do after SEEING top or bottom 10 cities, states or products

Every dashboard lacks CONTEXT

And why NOT

Because they DON'T even know the business problem or problem statement

So the dashboard you created is of NO use

Your job is not just to create DASHBOARDS

Your job would be to create DASHBOARDS to take out important INSIGHTS

And from those insights, you will build RECOMMENDATIONS

And these recommendations will be given to stakeholders as a SOLUTION to their business problem

If they implemented your IDEAS and the problem gets solved

Now you can say your work is DONE

If you are SHOWING bottom 10 states, then what

You should write the INSIGHTS too

For example, the sales of North India zone are FALLING

The insights can be used like this

Delhi that used to be in TOP 5 states is now in the BOTTOM 10 states

And this might be the REASON why our North India sales are DROPPING so hard

This is just a RANDOM example showing how your charts become UNDERSTANDABLE

Well, everyone can EXTRACT insights from charts

Even a KID can do this after looking at the tallest and smallest bar

The real task is to give RECOMMENDATIONS to solve the BUSINESS problem

And I have NEVER seen this in anyone's portfolio

If you are doing this, then you are easily STANDING out in the crowd

In my PORTFOLIO, I used to keep business problem, insights, dashboard and recommendations

Even in the bullet point of projects in my resume, I included RECOMMENDATIONS

Now this is what you can call a STRONG portfolio

Because your analysis skills are the SAME as those used in the real life by a Data Analyst

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𝐍𝐕𝐈𝐃𝐈𝐀 𝐅𝐑𝐄𝐄 𝐀𝐈 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🚀💻

Transform your skills with these cutting-edge courses by NVIDIA.

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Complete Data Science Roadmap 👇👇

1. Introduction to Data Science
- Overview and Importance
- Data Science Lifecycle
- Key Roles (Data Scientist, Analyst, Engineer)

2. Mathematics and Statistics
- Probability and Distributions
- Descriptive/Inferential Statistics
- Hypothesis Testing
- Linear Algebra and Calculus Basics

3. Programming Languages
- Python: NumPy, Pandas, Matplotlib
- R: dplyr, ggplot2
- SQL: Joins, Aggregations, CRUD

4. Data Collection & Preprocessing
- Data Cleaning and Wrangling
- Handling Missing Data
- Feature Engineering

5. Exploratory Data Analysis (EDA)
- Summary Statistics
- Data Visualization (Histograms, Box Plots, Correlation)

6. Machine Learning
- Supervised (Linear/Logistic Regression, Decision Trees)
- Unsupervised (K-Means, PCA)
- Model Selection and Cross-Validation

7. Advanced Machine Learning
- SVM, Random Forests, Boosting
- Neural Networks Basics

8. Deep Learning
- Neural Networks Architecture
- CNNs for Image Data
- RNNs for Sequential Data

9. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Word Embeddings (Word2Vec)

10. Data Visualization & Storytelling
- Dashboards (Tableau, Power BI)
- Telling Stories with Data

11. Model Deployment
- Deploy with Flask or Django
- Monitoring and Retraining Models

12. Big Data & Cloud
- Introduction to Hadoop, Spark
- Cloud Tools (AWS, Google Cloud)

13. Data Engineering Basics
- ETL Pipelines
- Data Warehousing (Redshift, BigQuery)

14. Ethics in Data Science
- Ethical Data Usage
- Bias in AI Models

15. Tools for Data Science
- Jupyter, Git, Docker

16. Career Path & Certifications
- Building a Data Science Portfolio

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Struggling with Machine Learning algorithms? 🤖

Then you better stay with me! 🤓

We are going back to the basics to simplify ML algorithms.
... today's turn is Logistic Regression! 👇🏻

1️⃣ 𝗟𝗢𝗚𝗜𝗦𝗧𝗜𝗖 𝗥𝗘𝗚𝗥𝗘𝗦𝗦𝗜𝗢𝗡
It is a binary classification model used to classify our input data into two main categories.

It can be extended to multiple classifications... but today we'll focus on a binary one.

Also known as Simple Logistic Regression.

2️⃣ 𝗛𝗢𝗪 𝗧𝗢 𝗖𝗢𝗠𝗣𝗨𝗧𝗘 𝗜𝗧?
The Sigmoid Function is our mathematical wand, turning numbers into neat probabilities between 0 and 1.

It's what makes Logistic Regression tick, giving us a clear 'probabilistic' picture.

3️⃣ 𝗛𝗢𝗪 𝗧𝗢 𝗗𝗘𝗙𝗜𝗡𝗘 𝗧𝗛𝗘 𝗕𝗘𝗦𝗧 𝗙𝗜𝗧?
For every parametric ML algorithm, we need a LOSS FUNCTION.

It is our map to find our optimal solution or global minimum.

(hoping there is one! 😉)

✚ 𝗕𝗢𝗡𝗨𝗦 - FROM LINEAR TO LOGISTIC REGRESSION
To obtain the sigmoid function, we can derive it from the Linear Regression equation.

Handling Imbalanced Data in ML: https://t.me/mlresourcestp/86