Top 10 Websites for Data Science 👇

1. Flowing Data (https://flowingdata.com)
2. Data Simplifier (https://www.datasimplifier.com)
3. R-Bloggers (https://www.r-bloggers.com)
4.  Edwin Chen (https://blog.echen.me)
5. Hunch (https://hunch.net)
6. KDNuggets (https://www.kdnuggets.com)
7. Data Science Central (https://www.datasciencecentral.com)
8. Kaggle Competitions (https://www.kaggle.com/competitions)
9. Simply Statistics (https://simplystatistics.org)
10. FastML (https://fastml.com)

Use these ChatGPT Prompts To 10X your Interview Chances

1. Company research
Prompt: "I have an interview with [company] for the position of [job].
Please summarize the company's mission, its main products or services, and its recent news or achievements by analyzing its website [website link] and any recent press release."

2. Resume Optimization
Prompt: "Review my current attached resume and suggest improvements tailored to applying for a [job] at [company]. Highlight gaps in my experience and recommend ways to fill them through online courses or projects."

3. Writing the cover letter
Prompt: "Based on the job description for [job title] at [company], generate a cover letter that highlights my relevant experience, skills, and why I am passionate about working for [company]."

4. Interview preparation
Prompt: "For [job title] at [company], what are some industry-specific challenges or trends I should be aware of? How can I demonstrate my understanding or propose possible solutions during the interview?"

5. Behavioral Interview Questions
Prompt: "Create a set of behavioural interview questions relevant to the [job] role at [company]. Include a brief guide on how to structure answers using the STAR (Situation, Task, Action, Result) method, tailored to my needs." experiences."

6. Craft Your Resume Perfectly
Prompt: "I want to tailor my resume to specific job descriptions so I get shortlisted more often. Analyze this job posting for [insert job title], extract the most important keywords and skills, and help me rewrite my resume to match it perfectly while maintaining authenticity."

7. Data-Driven Job Search
Prompt: "I want to use data and hiring trends to increase my chances of landing a high-paying job in [insert industry]. Provide me with data-backed job search strategies, salary benchmarks, and negotiation tips based on market trends."

8. Network Like a Pro
Prompt: "I want to build relationships with influential professionals in [insert industry] to increase my chances of getting a job.
Give me a step-by-step networking strategy, including outreach messages, follow-ups, and ways to provide value to them."

9. Craft the Perfect Elevator Pitch
Prompt: "I need a powerful 30-second elevator pitch that instantly impresses interviewers for [insert job title]. Craft a clear, concise, and compelling pitch that highlights my skills, experience, and what makes me unique."

10. The 30-Day Job Search Plan
Prompt: "I need to land a high-paying job in [insert industry] within 30 days. Create a daily action plan that includes networking, outreach, applications, and personal branding strategies to maximize my chances of success."

#aiprompts #jobs

Machine Learning Models Regularisation Methods 👆

Data Science Projects based on domain 👆

7 Free Kaggle Micro-Courses for Data Science Beginners with Certification

Python

https://www.kaggle.com/learn/python

Pandas

https://www.kaggle.com/learn/pandas

Data visualization

https://www.kaggle.com/learn/data-visualization

Intro to sql

https://www.kaggle.com/learn/intro-to-sql

Advanced Sql

https://www.kaggle.com/learn/advanced-sql

Intro to ML

https://www.kaggle.com/learn/intro-to-machine-learning

Advanced ML

https://www.kaggle.com/learn/intermediate-machine-learning

#datascienceprojects #kaggle

Data Science – Essential Topics 🚀

1️⃣ Data Collection & Processing
Web scraping, APIs, and databases
Handling missing data, duplicates, and outliers
Data transformation and normalization

2️⃣ Exploratory Data Analysis (EDA)
Descriptive statistics (mean, median, variance, correlation)
Data visualization (bar charts, scatter plots, heatmaps)
Identifying patterns and trends

3️⃣ Feature Engineering & Selection
Encoding categorical variables
Scaling and normalization techniques
Handling multicollinearity and dimensionality reduction

4️⃣ Machine Learning Model Building
Supervised learning (classification, regression)
Unsupervised learning (clustering, anomaly detection)
Model selection and hyperparameter tuning

5️⃣ Model Evaluation & Performance Metrics
Accuracy, precision, recall, F1-score, ROC-AUC
Cross-validation and bias-variance tradeoff
Confusion matrix and error analysis

6️⃣ Deep Learning & Neural Networks
Basics of artificial neural networks (ANNs)
Convolutional neural networks (CNNs) for image processing
Recurrent neural networks (RNNs) for sequential data

7️⃣ Big Data & Cloud Computing
Working with large datasets (Hadoop, Spark)
Cloud platforms (AWS, Google Cloud, Azure)
Scalable data pipelines and automation

8️⃣ Model Deployment & Automation
Model deployment with Flask, FastAPI, or Streamlit
Monitoring and maintaining machine learning models
Automating data workflows with Airflow

Join our WhatsApp channel for more resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

If you're a data science beginner, Python is the best programming language to get started.

Here are 7 Python libraries for data science you need to know if you want to learn:

- Data analysis
- Data visualization
- Machine learning
- Deep learning

NumPy

NumPy is a library for numerical computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

Pandas

Widely used library for data manipulation and analysis, offering data structures like DataFrame and Series that simplify handling of structured data and performing tasks such as filtering, grouping, and merging.

Matplotlib

Powerful plotting library for creating static, interactive, and animated visualizations in Python, enabling data scientists to generate a wide variety of plots, charts, and graphs to explore and communicate data effectively.

Scikit-learn

Comprehensive machine learning library that includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection, as well as utilities for data preprocessing and evaluation.

Seaborn

Built on top of Matplotlib, Seaborn provides a high-level interface for creating attractive and informative statistical graphics, making it easier to generate complex visualizations with minimal code.

TensorFlow or PyTorch

TensorFlow, Keras, or PyTorch are three prominent deep learning frameworks utilized by data scientists to construct, train, and deploy neural networks for various applications, each offering distinct advantages and capabilities tailored to different preferences and requirements.

SciPy

Collection of mathematical algorithms and functions built on top of NumPy, providing additional capabilities for optimization, integration, interpolation, signal processing, linear algebra, and more, which are commonly used in scientific computing and data analysis workflows.

Enjoy 😄👍

Step-by-Step Approach to Learn Data Science

➊ Learn a Programming Language → Python or R
↓
➋ Fundamentals → Statistics, Probability, Linear Algebra
↓
➌ Data Handling & Processing → Pandas, NumPy
↓
➍ Data Visualization → Matplotlib, Seaborn, Plotly
↓
➎ Exploratory Data Analysis (EDA) → Missing Values, Outliers, Feature Engineering
↓
➏ Machine Learning Basics → Supervised vs Unsupervised Learning
↓
➐ Model Building & Evaluation → Scikit-Learn, Cross-Validation, Metrics
↓
➑ Advanced Topics → Deep Learning, NLP, Time Series Analysis

Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

Accenture Data Scientist Interview Questions!

1st round-

Technical Round

- 2 SQl questions based on playing around views and table, which could be solved by both subqueries and window functions.

- 2 Pandas questions , testing your knowledge on filtering , concatenation , joins and merge.

- 3-4 Machine Learning questions completely based on my Projects, starting from
Explaining the problem statements and then discussing the roadblocks of those projects and some cross questions.

2nd round-

- Couple of python questions agains on pandas and numpy and some hypothetical data.

- Machine Learning projects explanations and cross questions.

- Case Study and a quiz question.

3rd and Final round.

HR interview

Simple Scenerio Based Questions.

Like if you need similar content 😄👍

Data Science Roadmap – Step-by-Step Guide 🚀

1️⃣ Programming & Data Manipulation

Python (Pandas, NumPy, Matplotlib, Seaborn)

SQL (Joins, CTEs, Window Functions, Aggregations)

Data Wrangling & Cleaning (handling missing data, duplicates, normalization)


2️⃣ Statistics & Mathematics

Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)

Probability Theory (Bayes' Theorem, Conditional Probability)

Hypothesis Testing (T-test, ANOVA, Chi-square test)

Linear Algebra & Calculus (Matrix operations, Differentiation)


3️⃣ Data Visualization

Matplotlib & Seaborn for static visualizations

Power BI & Tableau for interactive dashboards

ggplot (R) for advanced visualizations


4️⃣ Machine Learning Fundamentals

Supervised Learning (Linear Regression, Logistic Regression, Decision Trees)

Unsupervised Learning (Clustering, PCA, Anomaly Detection)

Model Evaluation (Confusion Matrix, Precision, Recall, F1-Score, AUC-ROC)


5️⃣ Advanced Machine Learning

Ensemble Methods (Random Forest, Gradient Boosting, XGBoost)

Hyperparameter Tuning (GridSearchCV, RandomizedSearchCV)

Deep Learning Basics (Neural Networks, TensorFlow, PyTorch)


6️⃣ Big Data & Cloud Computing

Distributed Computing (Hadoop, Spark)

Cloud Platforms (AWS, GCP, Azure)

Data Engineering Basics (ETL Pipelines, Apache Kafka, Airflow)


7️⃣ Natural Language Processing (NLP)

Text Preprocessing (Tokenization, Lemmatization, Stopword Removal)

Sentiment Analysis, Named Entity Recognition

Transformers & Large Language Models (BERT, GPT)


8️⃣ Deployment & Model Optimization

Flask & FastAPI for model deployment

Model monitoring & retraining

MLOps (CI/CD for Machine Learning)


9️⃣ Business Applications & Case Studies

A/B Testing & Experimentation

Customer Segmentation & Churn Prediction

Time Series Forecasting (ARIMA, LSTM)


🔟 Soft Skills & Career Growth

Data Storytelling & Communication

Resume & Portfolio Building (Kaggle Projects, GitHub Repos)

Networking & Job Applications (LinkedIn, Referrals)

Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

Want to learn machine learning without drowning in math or hype?

Start here:

5 ML algorithms every DIY data scientist should know 🧵👇

Day 1: Decision Trees

If you’ve ever asked, “What things can predict X?”

Decision trees are your best friend.

They split your data into rules like:

If age > 55 => Low risk
If call_count > 5 => Offer retention deal

Is your data in the form of a table?

(Hint - most data is).

Day 2: K-Means Clustering

The problem with predictive models like decision trees is that they need labeled data.

What if your data is unlabeled?

(Hint - most data is unlabeled)

K-means clustering discovers hidden groups - without needing labels.

Day 3: Logistic Regression

Logistic regression is a predictive modeling technique.

It predicts probabilities like:

Will this user churn?
Will this ad be clicked?
Will this customer convert?

Logistic regression is an excellent tool for explaining driving factors to business stakeholders.

Day 4: Random Forests

Random forests == a bunch of decision trees working together.

Each one is a bit different, and they vote on the outcome.

The result?

Better accuracy and stability than a single tree.

This is a production-quality ML algorithm.

Day 5: DBSCAN Clustering

K-means assumes groups are circular.

DBSCAN doesn’t.

It finds clusters of any shape and filters out noise automatically.

For example, you can use it for anomaly detection.

DBSCAN is the perfect complement to k-means in your DIY data science tool belt.

Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

Step-by-Step Approach to Learn Machine Learning

➊ Learn a Programming Language → Python or R
↓
➋ Mathematical Foundations → Linear Algebra, Probability, Statistics, Calculus
↓
➌ Data Preprocessing → Pandas, NumPy, Handling Missing Data, Feature Engineering
↓
➍ Exploratory Data Analysis (EDA) → Data Cleaning, Outliers, Visualization (Matplotlib, Seaborn)
↓
➎ Supervised Learning → Linear Regression, Logistic Regression, Decision Trees, Random Forest
↓
➏ Unsupervised Learning → Clustering (K-Means, DBSCAN), PCA, Association Rules
↓
➐ Model Evaluation & Optimization → Cross-Validation, Hyperparameter Tuning, Metrics
↓
➑ Deep Learning & Advanced ML → Neural Networks, NLP, Time Series, Reinforcement Learning

Like for detailed explanation ❤️

Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍

Step-by-Step Approach to Learn Python for Data Science

➊ Learn Python Basics → Syntax, Variables, Data Types (int, float, string, boolean)
↓
➋ Control Flow & Functions → If-Else, Loops, Functions, List Comprehensions
↓
➌ Data Structures & File Handling → Lists, Tuples, Dictionaries, CSV, JSON
↓
➍ NumPy for Numerical Computing → Arrays, Indexing, Broadcasting, Mathematical Operations
↓
➎ Pandas for Data Manipulation → DataFrames, Series, Merging, GroupBy, Missing Data Handling
↓
➏ Data Visualization → Matplotlib, Seaborn, Plotly
↓
➐ Exploratory Data Analysis (EDA) → Outliers, Feature Engineering, Data Cleaning
↓
➑ Machine Learning Basics → Scikit-Learn, Regression, Classification, Clustering

Free Data Science Resources: https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D

ENJOY LEARNING 👍👍