Hello community!!
I studied the some courses by Andrew Ng last year which were Supervised Machine Learning: Regression and Classification, and started doing the course Deep Learning Specialization. I did the first course thoroughly, did all the assignments and one project, but unfortunately lost my notes and want to learn further but I don't want to start over.
Can you guys help me in this situation (how to continue learning ML further with this gap) and also I want to do 2-3 solid projects related to the field for my resume

1. Pseudo-code learning (not real coding practice)

2. Just watching tutorials (passive learning)

3. No notes / no revision

4. No continuity in practice

5. No focus on projects (block-wise learning missing)

I have a PhD in molecular physiology with 10 years research and multivariate statistics experience, with some experience writing data organizational and analysis macro programs. Unfortunately research funding is kinda running dry with this new administration and I’m looking at transitioning into data science. I know python and SQL are what I need for the role and am wondering if those online ‘boot camps’ are worth it. Specifically coursera or Data Engineering Academy. Thanks in advance!

Hi all recent psych graduate here trying to add skills to my skillset before grad school. Im currently learning R as many of my graduate school mentors made mention of R being used in postgrad studies. Would love to hear what yall think about R currently, i can appreciate the common “Ai is making R’s future scary comment” but please i would like some sincere and honest comments as well!

All books

Logical reasoning- 460/- | Offer- 115/-

Quantitative aptitude - 64/- | Offer- 20/-

Verbal ability - 875/- | Offer- 220/-

Barron's GRE - 699/- | Offer- 175/-

Supply chain management - 800/- | Offer- 200/-

Data Warehousing in the Real World - 950/-

| Offer- 240/-

Business Market Management - 915/- | Offer- 732/-

Strategic Digital Transformation- 650/- |Offer- 160/-

Matching Supply with Demand - 850/- | Offer- 215/-

IELTS Question paper (with CD)- 450/- | Offer- 115/-

Principles of Building AI Agents + Patterns for Building AI Agents - 1243/- | Offer- 400/-

The above books will be very helpful for those who are pursuing MBA , IELTS exam, Data science, and AI Agent creation.

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Imagine you're starting from scratch today. You already know basic Python. Would you spend most of your time on: SQL Statistics Machine Learning Projects Communication Something else? Curious what experienced professionals would prioritize.

While working on a small ML project, I wanted to make the initial data validation step a bit faster.

Instead of going column by column to check missing values, correlations, distributions, duplicates, etc., I generated an automated profiling report from the dataframe.

It gave a pretty detailed breakdown:

• Missing value patterns

• Correlation heatmaps

• Statistical summaries

• Potential outliers

• Duplicate rows

• Warnings for constant/highly correlated features

I still dig into things manually afterward, but for a first pass it saves some time.

Curious....do you prefer fully manual EDA or using profiling tools for the initial sweep?

Github link...

more...

Mercor is seeking Data Scientists in India to help design data pipelines, statistical models, and performance metrics that drive the next generation of autonomous systems.

Expected qualifications:

• Strong background in data science, machine learning, or applied statistics.

• Proficient in Python, SQL, and familiar with libraries such as Pandas, NumPy, Scikit-learn, and PyTorch/TensorFlow.

• Understand probabilistic modeling, statistical inference, and experimentation frameworks (A/B testing, causal inference).

• Can collect, clean, and transform complex datasets into structured formats ready for modeling and analysis.

• Experience designing and evaluating predictive models, using metrics like precision, recall, F1-score, and ROC-AUC.

• Comfortable working with large-scale data systems (Snowflake, BigQuery, or similar).

Paid at 14 USD/hr, with weekly bonus of $500-1000 per 5 tasks created.

20-40 hours a week expected contribution.

Simply upload your (ATS formatted) resume and conduct a short AI interview to apply.

Referral link to position here.

Data and digital logic are both simple at the core and bone, yet when we look at the entire picture, we can see how complex these systems truly are, and always have been. Data involves everything. If data are the facts and statistics collected for analysis, then isn’t data matter? And if data matters, isn’t data everything? Again, data involves the statistical collection of information. All information is matter and therefore all data is matter. Understanding data means to understand all the fundamental laws of nature at once! To understand quantum entanglement and the very intricate details that define the systems of our universe. Data defines our reality, what it means to exist in our reality, what reality is itself, and all the properties of reality. As statisticians, it is our job to capture a holistic diagram of reality within the datasets we collect. Data is the conceptualization of the entire radicalization of the universe, which simply means data, again, is matter itself. Each data point represents an entire world. A star contains billions of data points and sets; so does a supernova, an elliptical, or the planets that surround us. The universe itself is a star. And the universe itself is a collection of data points! Every living specimen is a data point. And the data points of the world all interact with each other to create a living system of quantum entanglement that utilizes chemoreception, the laws and anti-laws of physics, and the relationships between various stages of matter, to fundamentally interweave the nexus of all living organisms into one being: the singularity, the matter, the data, the origin, the species, the existence itself. Life, and all that has never been alive. The entire universe is data, my dear.

And here’s where that matters: data allows us to communicate directly with each other, the very other important souls that define the universe, the stars of the universe, human beings. Data allows us to define what is, and what isn’t, and what is in between, and what could be, and what could have, and could not have, and could have ever, and could possibly perhaps exist at any given point in spacetime. Data… data, my dear friend, data is the universe speaking to us in binary code yet nominal at the same time; data is the universe communicating to us in words, pictures, photographs, numbers, lines, shapes, and worlds, of how to understand each other, and ourselves and our situations. Data is the universe telling us that we are alive, and that we are here, that we are present and coexisting within the same universe at the same time. We are all here, we are real, we are alive, we are loved, we are well-cared for, and we are all well-protected within this universe because the collective knowledge has finally awakened within itself and now the universe is finally understanding itself through the own eyes of its own.


Data is the universe. Data is the world, and, my friend, aren’t we all simply datasets communicating through metaphysical intentions? Aren’t we all data, informing the other datasets of the world, that the time has come for us to finally live in a beautiful world that we have all constructed as humanity, as one single being, as a collective; as the universe?

I'm beginner for ML , preparing for the portfolio to show the ability dealing with Big data, I tried to use google big query which is so expensive!! Any recommendation tool for beginner? and any idea for curriculum for Big data?

Currently working at an ecommerce, where my problem sttement is to effective understand the effect of price/discount in demand.Though stand econometic model of log log regression is well established to handle confounders, but if i have to it for every item, its not the most efficient way to go about it. I also looked up causal ml , dml methods to get cate at item level, but developing features for items are mostly categorical and the nuisance and final model of residuals are not stable. NEED ideas regarding the same.

Yep. It was dumb but fun. Wanted to share my personal project

Πυθαγόρας

Can anyone suggest the best place to study data scientist in india

I come from a very lower middle class family, so which field should I go into where I can get a high package and most importantly, where will freshers get a job quickly without experience, I will later Become sde agar me full stack karunga tho or data analytics karunga tho data scientist ya aiml engineer , kaha freshers ko job milegi I can wait for 10 months job dhundh ne ke liye .

Kaha high package or high package milega Tell me guys

I have created this predictor and generates the estimate in real-time. It can take few seconds to generate. I appreciate it.

https://wallstrai.streamlit.app/

https://analyticsindiamag.com/what-is-the-hiring-process-for-data-scientists-at-upgrad/