Data Science & Machine Learning

04 Apr 2025 08:10

Planning for Data Science or Data Engineering Interview.

Focus on SQL & Python first. Here are some important questions which you should know.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐒𝐐𝐋 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Find out nth Order/Salary from the tables.
2- Find the no of output records in each join from given Table 1 & Table 2
3- YOY,MOM Growth related questions.
4- Find out Employee ,Manager Hierarchy (Self join related question) or
Employees who are earning more than managers.
5- RANK,DENSERANK related questions
6- Some row level scanning medium to complex questions using CTE or recursive CTE, like (Missing no /Missing Item from the list etc.)
7- No of matches played by every team or Source to Destination flight combination using CROSS JOIN.
8-Use window functions to perform advanced analytical tasks, such as calculating moving averages or detecting outliers.
9- Implement logic to handle hierarchical data, such as finding all descendants of a given node in a tree structure.
10-Identify and remove duplicate records from a table.

𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬

1- Reversing a String using an Extended Slicing techniques.
2- Count Vowels from Given words .
3- Find the highest occurrences of each word from string and sort them in order.
4- Remove Duplicates from List.
5-Sort a List without using Sort keyword.
6-Find the pair of numbers in this list whose sum is n no.
7-Find the max and min no in the list without using inbuilt functions.
8-Calculate the Intersection of Two Lists without using Built-in Functions
9-Write Python code to make API requests to a public API (e.g., weather API) and process the JSON response.
10-Implement a function to fetch data from a database table, perform data manipulation, and update the database.

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Data Science & Machine Learning

03 Apr 2025 17:25

Important Topics to become a data scientist
[Advanced Level]
👇👇

1. Mathematics

Linear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Regression
Dimensionality Reduction
Density Estimation
Classification

2. Probability

Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Normal Distribution

3. Statistics

Introduction to Statistics
Data Description
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing
Regression

4. Programming

Python:

Python Basics
List
Set
Tuples
Dictionary
Function
NumPy
Pandas
Matplotlib/Seaborn

R Programming:

R Basics
Vector
List
Data Frame
Matrix
Array
Function
dplyr
ggplot2
Tidyr
Shiny

DataBase:
SQL
MongoDB

Data Structures

Web scraping

Linux

Git

5. Machine Learning

How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forest
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation(R)
XGBoost(Python|R)
Data Leakage

6. Deep Learning

Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification

7. Feature Engineering

Baseline Model
Categorical Encodings
Feature Generation
Feature Selection

8. Natural Language Processing

Text Classification
Word Vectors

9. Data Visualization Tools

BI (Business Intelligence):
Tableau
Power BI
Qlik View
Qlik Sense

10. Deployment

Microsoft Azure
Heroku
Google Cloud Platform
Flask
Django

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Data Science & Machine Learning

03 Apr 2025 06:41

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Data Science & Machine Learning

02 Apr 2025 14:05

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Data Science & Machine Learning

02 Apr 2025 07:18

5 EDA Frameworks for Statistical Analysis every Data Scientist must know

🧵⬇️

1️⃣ Understand the Data Types and Structure:
Start by inspecting the data’s structure and types (e.g., categorical, numerical, datetime). Use commands like .info() or .describe() in Python to get a summary. This step helps in identifying how different columns should be handled and which statistical methods to apply.

Check for correct data types
Identify categorical vs. numerical variables
Understand the shape (dimensions) of the dataset

2️⃣ Handle Missing Data:

Missing values can skew analysis and lead to incorrect conclusions. It’s essential to decide how to deal with them—whether to remove, impute, or flag missing data.

Identify missing values with .isnull().sum()
Decide to drop, fill (imputation), or flag missing data based on context
Consider imputing with mean, median, mode, or more advanced techniques like KNN imputation

3️⃣ Summary Statistics and Distribution Analysis:
Calculate basic descriptive statistics like mean, median, mode, variance, and standard deviation to understand the central tendency and variability. For distributions, use histograms or boxplots to visualize data spread and detect potential outliers.

Summary statistics with .describe() (mean, std, min/max)
Visualize distributions with histograms, boxplots, or violin plots
Look for skewness, kurtosis, and outliers in data

4️⃣ Visualizing Relationships and Correlations:

Use scatter plots, heatmaps, and pair plots to identify relationships between variables. Look for trends, clusters, and correlations (positive or negative) that might reveal patterns in the data.

Scatter plots for variable relationships.
Correlation matrices and heatmaps to see correlations between numerical variables.
Pair plots for visualizing interactions between multiple variables.

5️⃣ Feature Engineering and Transformation:

Enhance your dataset by creating new features or transforming existing ones to better capture the patterns in the data. This can include handling categorical variables (e.g., one-hot encoding), creating interaction terms, or normalizing/scaling numerical features.

Create new features based on domain knowledge.
One-hot encode categorical variables for modeling.
Normalize or standardize numerical variables for models that require scaling (e.g., KNN, SVM)

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Data Science & Machine Learning

01 Apr 2025 19:59

Machine Learning – Essential Concepts 🚀

1️⃣ Types of Machine Learning

Supervised Learning – Uses labeled data to train models.

Examples: Linear Regression, Decision Trees, Random Forest, SVM


Unsupervised Learning – Identifies patterns in unlabeled data.

Examples: Clustering (K-Means, DBSCAN), PCA


Reinforcement Learning – Models learn through rewards and penalties.

Examples: Q-Learning, Deep Q Networks



2️⃣ Key Algorithms

Regression – Predicts continuous values (Linear Regression, Ridge, Lasso).

Classification – Categorizes data into classes (Logistic Regression, Decision Tree, SVM, Naïve Bayes).

Clustering – Groups similar data points (K-Means, Hierarchical Clustering, DBSCAN).

Dimensionality Reduction – Reduces the number of features (PCA, t-SNE, LDA).


3️⃣ Model Training & Evaluation

Train-Test Split – Dividing data into training and testing sets.

Cross-Validation – Splitting data multiple times for better accuracy.

Metrics – Evaluating models with RMSE, Accuracy, Precision, Recall, F1-Score, ROC-AUC.


4️⃣ Feature Engineering

Handling missing data (mean imputation, dropna()).

Encoding categorical variables (One-Hot Encoding, Label Encoding).

Feature Scaling (Normalization, Standardization).


5️⃣ Overfitting & Underfitting

Overfitting – Model learns noise, performs well on training but poorly on test data.

Underfitting – Model is too simple and fails to capture patterns.

Solution: Regularization (L1, L2), Hyperparameter Tuning.


6️⃣ Ensemble Learning

Combining multiple models to improve performance.

Bagging (Random Forest)

Boosting (XGBoost, Gradient Boosting, AdaBoost)



7️⃣ Deep Learning Basics

Neural Networks (ANN, CNN, RNN).

Activation Functions (ReLU, Sigmoid, Tanh).

Backpropagation & Gradient Descent.


8️⃣ Model Deployment

Deploy models using Flask, FastAPI, or Streamlit.

Model versioning with MLflow.

Cloud deployment (AWS SageMaker, Google Vertex AI).

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Data Science & Machine Learning

01 Apr 2025 14:46

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Data Science & Machine Learning

01 Apr 2025 08:31

Pandas Cheatsheet 👆

Data Science & Machine Learning

31 Mar 2025 14:27

Python Hacks to instantly level up your coding skills 👆

Data Science & Machine Learning

30 Mar 2025 10:03

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

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Data Science & Machine Learning

29 Mar 2025 15:37

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)

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Data Science & Machine Learning

29 Mar 2025 07:59

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.

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Data Science & Machine Learning

28 Mar 2025 11:12

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.

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Data Science & Machine Learning

27 Mar 2025 10:43

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

Python

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

Pandas

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Data visualization

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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

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Data Science & Machine Learning

26 Mar 2025 16:37

Machine Learning Models Regularisation Methods 👆

Data Science & Machine Learning

04 Apr 2025 05:38

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Data Science & Machine Learning

03 Apr 2025 07:58

Common Machine Learning Algorithms!

1️⃣ Linear Regression
->Used for predicting continuous values.
->Models the relationship between dependent and independent variables by fitting a linear equation.

2️⃣ Logistic Regression
->Ideal for binary classification problems.
->Estimates the probability that an instance belongs to a particular class.

3️⃣ Decision Trees
->Splits data into subsets based on the value of input features.
->Easy to visualize and interpret but can be prone to overfitting.

4️⃣ Random Forest
->An ensemble method using multiple decision trees.
->Reduces overfitting and improves accuracy by averaging multiple trees.

5️⃣ Support Vector Machines (SVM)
->Finds the hyperplane that best separates different classes.
->Effective in high-dimensional spaces and for classification tasks.

6️⃣ k-Nearest Neighbors (k-NN)
->Classifies data based on the majority class among the k-nearest neighbors.
->Simple and intuitive but can be computationally intensive.

7️⃣ K-Means Clustering
->Partitions data into k clusters based on feature similarity.
->Useful for market segmentation, image compression, and more.

8️⃣ Naive Bayes
->Based on Bayes' theorem with an assumption of independence among predictors.
->Particularly useful for text classification and spam filtering.

9️⃣ Neural Networks
->Mimic the human brain to identify patterns in data.
->Power deep learning applications, from image recognition to natural language processing.

🔟 Gradient Boosting Machines (GBM)
->Combines weak learners to create a strong predictive model.
->Used in various applications like ranking, classification, and regression.

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Data Science & Machine Learning

02 Apr 2025 17:49

Advanced Data Science Concepts 🚀

1️⃣ Feature Engineering & Selection

Handling Missing Values – Imputation techniques (mean, median, KNN).

Encoding Categorical Variables – One-Hot Encoding, Label Encoding, Target Encoding.

Scaling & Normalization – StandardScaler, MinMaxScaler, RobustScaler.

Dimensionality Reduction – PCA, t-SNE, UMAP, LDA.


2️⃣ Machine Learning Optimization

Hyperparameter Tuning – Grid Search, Random Search, Bayesian Optimization.

Model Validation – Cross-validation, Bootstrapping.

Class Imbalance Handling – SMOTE, Oversampling, Undersampling.

Ensemble Learning – Bagging, Boosting (XGBoost, LightGBM, CatBoost), Stacking.


3️⃣ Deep Learning & Neural Networks

Neural Network Architectures – CNNs, RNNs, Transformers.

Activation Functions – ReLU, Sigmoid, Tanh, Softmax.

Optimization Algorithms – SGD, Adam, RMSprop.

Transfer Learning – Pre-trained models like BERT, GPT, ResNet.


4️⃣ Time Series Analysis

Forecasting Models – ARIMA, SARIMA, Prophet.

Feature Engineering for Time Series – Lag features, Rolling statistics.

Anomaly Detection – Isolation Forest, Autoencoders.


5️⃣ NLP (Natural Language Processing)

Text Preprocessing – Tokenization, Stemming, Lemmatization.

Word Embeddings – Word2Vec, GloVe, FastText.

Sequence Models – LSTMs, Transformers, BERT.

Text Classification & Sentiment Analysis – TF-IDF, Attention Mechanism.


6️⃣ Computer Vision

Image Processing – OpenCV, PIL.

Object Detection – YOLO, Faster R-CNN, SSD.

Image Segmentation – U-Net, Mask R-CNN.


7️⃣ Reinforcement Learning

Markov Decision Process (MDP) – Reward-based learning.

Q-Learning & Deep Q-Networks (DQN) – Policy improvement techniques.

Multi-Agent RL – Competitive and cooperative learning.


8️⃣ MLOps & Model Deployment

Model Monitoring & Versioning – MLflow, DVC.

Cloud ML Services – AWS SageMaker, GCP AI Platform.

API Deployment – Flask, FastAPI, TensorFlow Serving.


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Data Science & Machine Learning

02 Apr 2025 10:28

This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.

1. Supervised Learning
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.

Some common supervised learning algorithms include:

➡️ Linear Regression – For predicting continuous values, like house prices.
➡️ Logistic Regression – For predicting categories, like spam or not spam.
➡️ Decision Trees – For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) – For finding similar data points.
➡️ Random Forests – A collection of decision trees for better accuracy.
➡️ Neural Networks – The foundation of deep learning, mimicking the human brain.

2. Unsupervised Learning
With unsupervised learning, the model explores patterns in data that doesn’t have any labels. It finds hidden structures or groupings.

Some popular unsupervised learning algorithms include:

➡️ K-Means Clustering – For grouping data into clusters.
➡️ Hierarchical Clustering – For building a tree of clusters.
➡️ Principal Component Analysis (PCA) – For reducing data to its most important parts.
➡️ Autoencoders – For finding simpler representations of data.

3. Semi-Supervised Learning
This is a mix of supervised and unsupervised learning. It uses a small amount of labeled data with a large amount of unlabeled data to improve learning.

Common semi-supervised learning algorithms include:

➡️ Label Propagation – For spreading labels through connected data points.
➡️ Semi-Supervised SVM – For combining labeled and unlabeled data.
➡️ Graph-Based Methods – For using graph structures to improve learning.

4. Reinforcement Learning
In reinforcement learning, the model learns by trial and error. It interacts with its environment, receives feedback (rewards or penalties), and learns how to act to maximize rewards.

Popular reinforcement learning algorithms include:

➡️ Q-Learning – For learning the best actions over time.
➡️ Deep Q-Networks (DQN) – Combining Q-learning with deep learning.
➡️ Policy Gradient Methods – For learning policies directly.
➡️ Proximal Policy Optimization (PPO) – For stable and effective learning.

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Data Science & Machine Learning

02 Apr 2025 06:13

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Data Science & Machine Learning

01 Apr 2025 16:58

Data Science Learning Plan

Step 1: Mathematics for Data Science (Statistics, Probability, Linear Algebra)

Step 2: Python for Data Science (Basics and Libraries)

Step 3: Data Manipulation and Analysis (Pandas, NumPy)

Step 4: Data Visualization (Matplotlib, Seaborn, Plotly)

Step 5: Databases and SQL for Data Retrieval

Step 6: Introduction to Machine Learning (Supervised and Unsupervised Learning)

Step 7: Data Cleaning and Preprocessing

Step 8: Feature Engineering and Selection

Step 9: Model Evaluation and Tuning

Step 10: Deep Learning (Neural Networks, TensorFlow, Keras)

Step 11: Working with Big Data (Hadoop, Spark)

Step 12: Building Data Science Projects and Portfolio

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Data Science & Machine Learning

01 Apr 2025 10:43

A-Z of essential data science concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

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Data Science & Machine Learning

01 Apr 2025 07:01

𝟰 𝗙𝗥𝗘𝗘 𝗦𝗤𝗟 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍

- Introduction to SQL (Simplilearn) 

- Intro to SQL (Kaggle) 

- Introduction to Database & SQL Querying 

- SQL for Beginners – Microsoft SQL Server

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Data Science & Machine Learning

31 Mar 2025 10:06

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

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Data Science & Machine Learning

30 Mar 2025 07:29

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.

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Data Science & Machine Learning

29 Mar 2025 09:42

Machine Learning Project Ideas 👆

Data Science & Machine Learning

28 Mar 2025 14:45

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

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Data Science & Machine Learning

28 Mar 2025 06:46

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

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Data Science & Machine Learning

27 Mar 2025 06:09

Data Science Projects based on domain 👆

Data Science & Machine Learning

26 Mar 2025 14:24

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