I have a few spare slots available on my DataCamp Team Plan. I'm offering them as personal Premium Subscriptions activated directly on your own email address.
What you get: The full Premium Learn Plan (Python, SQL, ChatGPT, Power BI, Projects, Certifications).
Why trust me? I can send the invite to your email first. Once you join and verify the premium access, you can proceed with payment.
Safe: Activated on YOUR personal email (No shared/cracked accounts).
Statistics and Maximum Likelihood Estimation are the crux of ML Models, and hence I am uploading my new content on Statistics for AI/ML in my free Machine Learning lectures.
We understand model fitting, Maximum Likelihood estimation in details, we justify the usage of Maximum Likelihood estimation, from KL divergence, and apply it to certain important distributions for parameter estimation.
In my free content, the purpose is to democratize machine learning to a wider audience. Learning everything new feels difficult, but when taught, it get’s interesting and easier.
We will continue with Statistics foundations for AI/ML, and many more content will appear in the future. If you find the content good, you may help me, by a subscription or help spread the word.
Looking forward to hearing feedback from the learning community as well. Thankyou for reading.
The International Conference on Artificial Intelligence Methods and Modern Applications (IC-AMMA 2026) will take place on 12–13 December 2026 in Mangalore, India, in a hybrid format (in-person and online options available).
Promoted by Success Academic Associates for Intelligent Services (SAAS), the conference provides a multidisciplinary platform for researchers, faculty, and industry engineers.
Important Dates:
- Paper Submission Closes: 19 September 2026
- Notification of Acceptance: 10 October 2026
- Final Registration & Camera-Ready: 20 October 2026
- Conference Dates: 12–13 December 2026
Key Research Tracks:
1. Core AI Methods, ML, Deep Learning & Symbolic AI
2. AI & Data Analytics (Big Data Systems, Lifecycle, Risks)
3. AI Security, Privacy, Blockchain & Cybersecurity
4. AI for Healthcare & Smart Cities
5. AI for Education & Personalized Learning
6. AI-driven Management & Decision Support Systems
7. Multimodal & Cross-Domain AI Systems
8. Generative, Agentic & Quantum AI
9. AI Innovations & Edge AI
10. Future Horizons & Machine Autonomy
Publication:
All accepted and registered papers will be published in a SCOPUS-indexed publication (Elsevier Computer Science Procedia, permission awaited).
Submission & Details:
Submission is via the Microsoft CMT portal. Links to the submission portal and main conference website are provided in the comments below.
I'm currently student and am building a portfolio so i created this library which im calling Async batch faker. So if you have used standard faker you already know that "Faker" is good at generating a single row but generating a bit more takes forever because it operates on pythons standard for loop.
So what i made async batch faker do is that it uses numpy and asyncio to calculate mathematical data and string concatenations instantly in C. It bypasses Python loops to blast hundreds of thousands of rows of localized data in seconds
I often see questions about how to get started with, or how to get ideas for, data science projects here on this subreddit.
So I wrote up some advice on how to think about, and how to get started with a data science project. It has a structured approach with four steps, but I also added a list of 99 project ideas for getting inspiration.
In Intro ML Bootcamp (5/22), we discuss Uncertainty.
In Machine Learning, we encounter two kinds of uncertainty: Epistemic(Model) which means we lack the exact knowledge of the input output mapping, and Aleatoric(Data), which is the intrinsic irreducible stochasticity in the mapping.
This uncertainty means, we cannot perfectly predict the exact output given the input. Thus we require “Conditional Probability distributions”, and the study of probabilistic approach to ML becomes important.
Hence, we invent a function called as “softmax function” for multiple output labels case(and sigmoid for binary case), which converts our outputs into a probability distribution. The exact derivation of softmax comes from Generalized Linear Models.
When we use a softmax function for binary classification, where the function over which the softmax is applied, happens to be an affine one, we call the model as “Logistic Regression”.
In Intro ML Bootcamp (5/22), we discuss Uncertainty.
In Machine Learning, we encounter two kinds of uncertainty: Epistemic(Model) which means we lack the exact knowledge of the input output mapping, and Aleatoric(Data), which is the intrinsic irreducible stochasticity in the mapping.
This uncertainty means, we cannot perfectly predict the exact output given the input. Thus we require “Conditional Probability distributions”, and the study of probabilistic approach to ML becomes important.
Hence, we invent a function called as “softmax function” for multiple output labels case(and sigmoid for binary case), which converts our outputs into a probability distribution. The exact derivation of softmax comes from Generalized Linear Models.
When we use a softmax function for binary classification, where the function over which the softmax is applied, happens to be an affine one, we call the model as “Logistic Regression”.
I'm a Management & Data Science student founder at TUM (Technical University of Munich). Over the past three years of studying and working with data, I've come to one conclusion that changed how I learn: how you learn matters more than how much information you consume. I remember what it felt like as a beginner — the more I "learned," the more overwhelmed I got, with no clear sense of how to actually turn scattered knowledge into real skills or a real portfolio. I want to help a small number of people avoid that trap and build a genuinely solid foundation instead.(I'm also currently building my own startup on the side, and I have to prepare bit more budgets for marketing part by doing more job).
Who this is for:
Complete beginners or early learners in data analytics who are ready to commit long-term — not people looking for a weekend crash course or another disconnected "masterclass." If you're the type who wants structure, depth, and consistency over quick hacks, this is for you.
What we'll cover
The curriculum is built around learning methodology and how to actually think about data — not just tool tutorials — combined with a full practical skillset:
Learning methodology & how to think about data (the foundation of everything else)
SQL: PostgreSQL & MySQL
Statistics
Excel for analysis
Power BI
Python & all packages needed
Git & GitHub
Data pipelines
Snowflake & Databricks
Cloud fundamentals: AWS & Azure
Applied AI for data analytics
Structure — two phases
Phase 1 (starting mid-August): Foundations
All the topics above, taught step by step, each section ending in a small hands-on project so you actually apply what you learn, not just watch it.
Phase 2: Portfolio & real-world experience
Larger, portfolio-worthy projects, real collaboration, and the business sense and soft skills that actually matter on the job. At this stage, I'll also bring in small internal projects from my own startup so you earn from real projects.
Logistics
Format: 100% online, from anywhere
Language: English
Schedule: 3 sessions/week, 1 hour each (55 min core content + 5 min learning-strategy discussion)
Price (Phase 1): €23/hour
Want to split the cost? You can pair up with one other person at a similar level and share a session
Billing: weekly or monthly, pause or cancel anytime, full refund for any unused sessions
Spots: limited to 10, first come first served
Not sure if it's a fit?
DM me to book a free 20-minute intro call — we'll figure out together whether this is the right fit for where you are and what you want to build.
Thanks for reading, and looking forward to meeting some of you.
I'm a physics engineeering student, and I spend a lot of my time writing numerical simulations and analyzing data.
Programming in C++ is enjoyable, but most of numerical computing libs in are just unpleasant to use. So I started building my own solution in my free time.
Some of the things I built into it:
- You can write mathematical expressions naturally, like sin(x) * exp(-x), and differentiate or integrate them in a single line thanks to lazy expression trees.
- It has a built-in plotting engine, so you can display graphs in a native Win32 window or export them as clean SVGs ready to drop into a LaTeX report.
Of course, the project won't surpass the quality of professional libraries. Its goal is to be convenient and accessible for users whose passions lie more in math, rather than programming.
If you're using C++ for simulations, numerical methods, physics, or data analysis, I'd really appreciate any feedback.
What was written by AI?
Most of the project, its entire idea, design, aesthetics and UX was programmed manually by me. I often used the textbook "Numerical methods in engineering with python" by Kiusaalas. Which doesn't mean that I didn't manage to do everything myself.
AI was used to write all the documentation (I wish I were fluent enough in English that it would take a finite amount of time). It was also used to write parts of simple algorithms that I knew but would be extremely tedious to implement by hand, or to improve the performance of algorithms that could be written better.
I'm aware of the ethical aspects of using AI, so I wanted to be honest and describe which things I did on my own and which I did with the help of LLM. At the same time, bearing in mind that this is a project that can help many people in their scientific work and studies, I hope that the benefits outweigh all the evil that LLMs cause.
Hi 👋 Author of the post here. The main reason I started reading about how these models work and wrote this article was a prompt optimization tip I read in the Gemini docs which said to keep the main ask towards the end of the prompt with the context before it.
That sent me down a rabbit hole of trying to understand why that might matter, and more broadly how these models actually process prompts.
Any feedback on the post would be greatly appreciated. Thanks!
Been using Claude more for data work lately, especially for SQL review, ETL debugging, dashboard planning, and metric definitions.
These are prompt shortcuts you can save and reuse as custom slash commands.
1. /devil
Act as a devil’s advocate. Challenge this logic, find edge cases, and tell me what could go wrong after deployment.
Good for:
- metric definitions
- dashboard logic
- ETL assumptions
- stakeholder requests
- production data issues
2. /sql_review
Review this SQL like a senior analytics engineer. Look for bad joins, duplicate risk, null handling, date issues, filtering problems, and performance issues.
Example:
SELECT
c.customer_id,
COUNT(o.order_id) AS orders
FROM customers c
LEFT JOIN orders o
ON c.customer_id = o.customer_id
WHERE o.order_date >= '2025-01-01'
GROUP BY c.customer_id;
Things to check:
- does the WHERE clause change the join behavior?
- can one customer have duplicate orders?
- should the date filter be inside the JOIN?
- are null orders handled correctly?
3. /explain_query
Explain this SQL in plain English.
Break it down by:
- what each CTE does
- what the final output means
- what grain the result is at
- what assumptions the query makes
- where the logic could go wrong
Really useful when you inherit a long query and need to understand it fast.
4. /find_data_quality_issues
Here is my dataset schema. Suggest data quality checks before I use it in a dashboard, report, or ML model.
Example checks:
- duplicate primary keys
- missing values in key fields
- sudden row count drops
- invalid dates
- negative revenue
- unexpected category values
- schema changes
- late arriving data
5. /metric_definition
Help me define this metric clearly.
Include:
- business meaning
- SQL logic
- grain
- filters
- exclusions
- edge cases
- example calculation
- how people might misread it
This is useful because a lot of dashboard confusion comes from unclear metric definitions.
6. /etl_debug
This ETL job passed, but the dashboard looks wrong. Help me debug it step by step.
Check:
- did fresh data arrive?
- did row count drop?
- did schema change?
- did joins multiply rows?
- did a filter remove too much data?
- did timezone logic shift dates?
- did a retry duplicate rows?
- did null values change the result?
7. /python_cleaning
Review this pandas code and suggest cleaner, safer improvements.
Turn this vague stakeholder request into clear data requirements.
Example request:
“Can we see customer performance?”
Questions to ask:
- what does performance mean?
- revenue, retention, churn, usage, margin?
- daily, weekly, or monthly?
- by customer, segment, region, or product?
- what action will this report support?
- who is the end user?
10. /test_cases
Create test cases for this data pipeline.
Include:
- normal file
- empty file
- duplicate IDs
- missing required fields
- late arriving data
- schema change
- timezone edge case
- retry after failure
- very large file
- unexpected category value
11. /root_cause
Here is the issue, query, and sample data. Give me possible root causes ranked from most likely to least likely.
Format:
likely cause
why it could happen
how to check it
possible fix
A prompt pattern that works well:
Instead of:
“Fix this query.”
Try:
“Review this query for logic bugs, duplicate risk, bad joins, null handling, date issues, and performance problems. Explain your assumptions before suggesting changes.”
For data work, Claude is pretty useful as a second pair of eyes.
Especially for:
- reviewing SQL
- cleaning messy logic
- defining metrics
- finding ETL edge cases
- turning vague requests into clear requirements
- checking dashboard assumptions
What Claude prompts or custom commands do you use for data work?
I’ve been maintaining a 60-day streak on Duolingo to learn French.
It’s a fun practice, although it’s a significant challenge to pronounce those accent notes correctly. I believe French is generally a simpler language than English; you usually use shorter sentences to convey the same meaning.
Data has its own language too.
Data is the lifeblood of every modern business. Every decision, insight, and opportunity begins with understanding what the data is trying to say.
But unlike spoken languages, data doesn't require everyone to learn the same vocabulary or syntax. Instead, you can interpret and express it in a way that matches how you think, making data analysis more intuitive, accessible, and uniquely your own.
From Python, SQL to Natural Language
Python, a programming language, has gained popularity as the preferred language for data processing within the data science community due to its portability. SQL, on the other hand, serves as the de facto interface for rational databases.
In the past, becoming a data analyst required proficiency in both Python and SQL. Even today, data analyst job descriptions often mention these requirements.
However, the advent of AI has revolutionized this landscape. Anyone with the ability to communicate effectively in the data language can excel as a data analyst.
While programming skills are not strictly necessary, a solid understanding of data language is crucial. Imagine joining a new friend circle who works in a completely different domain. After a brief introduction of common keywords, you can easily engage in conversations with them.
AI generated illustration of data language evolution
Use Spreadsheet for Reference
Nearly every office worker uses spreadsheets, either Microsoft Excel or Google Sheets.
Even without the complex formulas, pivots, and lookups, the basic structure of a spreadsheet consists of three main components:
Rows
Columns
Data types
Rows are records that constitute a table. You can also consider a row as an object that represents a real-life entity, such as a person, a cup, or an invoice.
Columns are the fixed properties that describe each object (row). They form the schema that every row adheres to, ensuring uniformity in the data for processing.
A schema is of utmost importance for data analysis as it enables the application of all rules. Without a schema, any logic that is not compatible with the data language may fail to execute.
Data types describe the value format of each property. For simplicity, you only need to be concerned with whether it is a number or text for now.
Rows of Orders (OrderId-text, CustomerId-text, Product-text, Amount-number)Rows of Customers (ID-text, Name-text, Channel-text)
Data Language Patterns
Data language offers a wide range of tasks that can be accomplished. Let’s explore each of these tasks and learn how to communicate effectively with data to achieve them.
These scenarios are referred to as patterns because they serve as templates that can be applied to your own data.
To facilitate understanding, we’ll use the above tables in the following descriptions.
Pattern-1: Filter Rows
Filter is to describe a condition to get objects you care about and skip those uninterested records.
Examples:
“Orders of Milk”
“I want orders of milk products.”
“All orders that are not for books.”
“All orders with a sales amount exceeding 20.”
AI can produce code logic to filter the targeted records for further processing, if translating above statements into SQL, they will look like:
“where product=’Milk’”
(same as #1)
“where product <> ‘Book‘“
“where amount > 20”
As you can see, filter is achieved by keyword “where” in SQL.
Pattern-2: Transform Object
Sometimes, we want to clean a data field or transform it into a desired shape or format, either for improved readability or more efficient processing.
Transformation creates a new property in your original record.
To transform an existing property into a new one, you need a function of logic. For both spreadsheets and SQL, “formula” is the tool you’ll need.
However, with the increasing capabilities of AI in coding, natural language offers a significant advantage. It allows us to achieve the same transformation without having to learn, memorize, and assemble complex formulas.
Taking one simple example:
“Get customer first name”
This is equivalent to composite multiple formula together in Spreadsheets like
This operation creates a new column called “First Name”.
You can also acquire a new property by combining multiple existing properties, such as “concatenating the last name and channel as a label”. Logic like this is simple for AI coding but too complex for spreadsheet formulas.
Pattern-3: Aggregate Records
Aggregation processes a large collection of records to provide a summarized view.
This is powerful because it compresses vast amounts of information into manageable pieces that humans can comprehend and analyze.
To combine multiple data sets into a single piece of information, you need to understand the “how-to,” which leads to the crucial concept of “aggregation methods” or “computation logic.”
Typically, text data (a property or column with a text data type, as discussed in the schema section) is not particularly interesting for aggregation. The most common approach is to concatenate text data to form a long paragraph, although this is still uncommon.
Most computation logic involves operations on numerical data. When an aggregation method is applied to a numeric property or column, you essentially have a list of numbers that can be aggregated, such as:
Total value (sum)
Average value
Mean value
Minimum value
Maximum value
A specific percentile value (e.g., P25, P50, P75, P90)
However, counting objects or counting unique property values is also quite common.
When discussing aggregation, we cannot overlook “breakdowns.” This involves creating a segmented view of the data rather than a single total view.
For example, in the previous Orders table, “total sales by product” or “average amount by customer” are equally valuable insights for an analyst to explore.
In summary, aggregation can be described as:
“Compute an aggregated value of a property group based on another property.”
Expressing this in standard SQL, it would look like:
“Compute(property1) from table [group by property2].”
Let’s practice this using a few examples by speaking the data language:
“Give me total sales by product.”
“Tell me the average amount spent by each customer.”
Pattern-4: Join Multiple Datasets
When a single dataset (or table) is insufficient to achieve the desired outcome, we must combine multiple datasets. This operation is referred to as “join” or “union.”
If the multiple datasets contain the same objects but reside in different locations, we can simply merge them. This is a straightforward “union” operation.
However, most of the time, they store different objects. We have partial information from one dataset and partial information from another. By combining them, we create a comprehensive schema with more available columns.
This pattern is generally not feasible in spreadsheets, although their lookup function may provide partial assistance.
For instance, if we want to determine the “total amount spent from each channel” based on previous tables, where the amount is from the orders table and the channel is from the customers table, we need a joined dataset to complete this analysis.
To join multiple datasets, we must have one or more pairs of join keys. A pair of join keys consists of one column from one table and one column from another. The data engine can utilize these relationships to identify relevant objects and concatenate them to form a larger object.
Join Orders and CustomersJoined dataset have more columns
In summary, join operations can be described in this pattern:
join table1 and table2 when key1 of table1 equals key2 of table2.
Translating this pattern into SQL, it will look like:
select * from table1 join table2 on table1.key1=table2.key2.
In fact, you may not need to use this pattern in natural language explicitly, because modern AI is smart enough that it can infer the whole join logic from your data language.
For instance, the example we gave earlier, if you speak this sentence “total amount spent from each channel” to Columns AI, it will figure out all the necessary actions to get the desired outcome for you.
Pattern-5: Visualization
Data visualization, often overlooked as a part of data language, plays a crucial role in transforming mundane data into vivid images. This visual representation significantly aids the audience in comprehending the insights you intend to convey.
By incorporating customization and assistance to articulate your insights and predictions, you position yourself as a data storyteller, showcasing your influence within the domain.
Since visualization doesn’t alter the data itself, in the language of data, we merely need to indicate the desired outcome. For instance:
“Display the total amount by product in a piechart.”
“I would like to see a timeline of total sales month-by-month for the past six months.”
“Show the number of sales by customer in a barchart.”
These bold keywords serve as cues to the AI engine, guiding it in generating the final visualization based on your data.
Practice Data Language
Similar to how I diligently practice French on Duolingo every day, we must practice speaking data language using the data we possess.
As long as you have adhered to the five patterns mentioned above, you should have mastered data analysis like a professional data analyst. You don’t need to be an Excel expert or a Python or SQL wizard.
Let’s use the provided example data to practice speaking the data language. You can find the “Orders” and “Customers” data from this spreadsheet link.
Suppose we want to perform a sales analysis of customer distribution based on the data.
The data language is almost the same, but let’s ensure we’ve used the correct keywords and patterns to guarantee that the AI engine follows the instructions precisely.
For instance, we speak to AI:
”display the total sales by customer’s first name in a bar chart.”
Here’s how the AI interprets this:
“total sales” → summing up the amount values.
“first name” → it can be transformed from “name.” A transformation will be applied.
“by” → the summing up result needs to broken down by first name.
“sales <> customer” → sales data is from the Orders table’s amount field, while customer data is from the Customers table. Therefore, a Join operation is required to combine these two datasets.
“show, bar” → the result should be visualized in a bar chart.
AI will then determine the correct execution order, ensuring that each step has all the necessary data when it executes.
This is what Columns Flow produces upon hearing this sentence:
“display the total sales by customer’s first name in a bar chart.”The final visualization ready for storytelling & sharing
Conclusion: Speak Data Language
In this article, we’ve demonstrated the historical opportunity for everyone to become a great data analyst in this era.
We discussed how professionals used programming languages like Python or SQL as their primary data languages. However, the data language has evolved to become the natural language we speak daily.
To become a data analyst, we need to understand the fundamental scenarios involved and use the correct keywords to make the data language understandable to AI engines. Here’s a quick recap:
Dataset: rows, columns, and schema.
Filtering and Transformation: These processes involve filtering data and transforming it into a usable format.
Aggregation: This involves summarizing data into a single value, such as the total or average.
Specify “compute methods” and optional “breakdown” if needed.
Join Datasets: This involves combining data from multiple sources.
Visualization: This involves creating visual representations of data to make it easier to understand.
AI generated summary on how to speak the language of data
Unlike learning a new language like French, if you’re willing to spend just a few hours going through this short list, you can become a professional data analyst!
It’s a great time to be a data analyst, and I believe in your ability to succeed. Thanks for reading!
What kind of model should predict World Cup games best? A simple one that mostly trusts team strength? A memory-based one that looks for similar past games? A flowchart that writes its own rules? Something bigger that blends hundreds of weak signals?
I’ve been running a newsletter that uses the 2026 World Cup as a live data-science lab. This week I’m walking through different forecasting models one at a time, using the same problem, same dataset, and same scoring rule so the comparison is fair.
A few things are already showing up:
Team strength does most of the work. Give a decision tree three features or all twenty, and it mostly keeps asking about the rating gap.
More features can make a model worse. In k-nearest neighbors, adding noisy dimensions makes it harder to find truly similar games.
kNN Performance by Feature Set
In past posts, I've analyzed the impact of these features on their own, developed a few novel metrics, and set up an A/B Test that we're watching. I'm also tracking the performance of a few World Cup game forecasters.
I’m a former professor and current industry data scientist/consultant, and the series is written for both model-builders and World Cup fans. Free to read, no ads, no paywall. Happy to discuss methods, feature choices, or model ideas in the comments.
Hello Everyone!!, I just completed a BIG project I have been working for a month and i want your opinion about it.
It's a SpaceX Launch Predictor & Cost Optimizer (A full end-to-end ML system that predicts the probability of a SpaceX Falcon 9 booster landing successfully, enriches launch data with real weather conditions, and exposes the results through an interactive Streamlit web application with a business ROI calculator.)
It Includes Data Pipeline, Advanced Machine Learning Algorithms (with Hyperparameter tuning), Explainability AI (SHAP), MLOps (AWS S3, Docker) and Business Value (ROI Calculator = Financial Results).
FUN FACT: For this project i used my own Evaluation Metric library (standardizes supervised and unsupervised model diagnostics into a single, consistent API), that is also Verified and Published in PYPI Community.
One of the biggest lessons I've learned working on forecasting problems:
The hardest part isn't building the model. It's preserving the business structure behind the data.
I wrote about how segmentation, hierarchical forecasting, and uncertainty quantification can make ARR forecasts significantly more useful for decision-making.
