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 😄👍
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 😄👍
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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 👍👍
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 👍👍
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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 😄👍
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 😄👍
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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 👍👍
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 👍👍
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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 👍👍
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 👍👍
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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 👍👍
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 👍👍
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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 👍👍
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 👍👍
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Python Hacks to instantly level up your coding skills 👆
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