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7 Best GitHub Repositories to Break into Data Analytics and Data Science
If you're diving into data science or data analytics, these repositories will give you the edge you need. Check them out:
1️⃣ 100-Days-Of-ML-Code
🔗 https://github.com/Avik-Jain/100-Days-Of-ML-Code
⭐️ Stars: ~42k
2️⃣ awesome-datascience
🔗 https://github.com/academic/awesome-datascience
⭐️ Stars: ~22.7k
3️⃣ Data-Science-For-Beginners
🔗 https://github.com/microsoft/Data-Science-For-Beginners
⭐️ Stars: ~14.5k
4️⃣ data-science-interviews
🔗 https://github.com/alexeygrigorev/data-science-interviews
⭐️ Stars: ~5.8k
5️⃣ Coding and ML System Design
🔗 https://github.com/weeeBox/coding-and-ml-system-design
⭐️ Stars: ~3.5k
6️⃣ Machine Learning Interviews from MAANG
🔗 https://github.com/arunkumarpillai/Machine-Learning-Interviews
⭐️ Stars: ~8.1k
7️⃣ data-science-ipython-notebooks
🔗 https://github.com/donnemartin/data-science-ipython-notebooks
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The Data Science skill no one talks about...
Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
1. a dataset, and
2. a clearly defined metric to optimize for, e.g. accuracy
But it doesn’t.
It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.
Let’s go through an example.
Example
Imagine you are a data scientist at Uber. And your product lead tells you:
👩💼: “We want to decrease user churn by 5% this quarter”
In a nutshell
Resume key words for data scientist role explained in points:
1. Data Analysis:
- Proficient in extracting, cleaning, and analyzing data to derive insights.
- Skilled in using statistical methods and machine learning algorithms for data analysis.
- Experience with tools such as Python, R, or SQL for data manipulation and analysis.
2. Machine Learning:
- Strong understanding of machine learning techniques such as regression, classification, clustering, and neural networks.
- Experience in model development, evaluation, and deployment.
- Familiarity with libraries like TensorFlow, scikit-learn, or PyTorch for implementing machine learning models.
3. Data Visualization:
- Ability to present complex data in a clear and understandable manner through visualizations.
- Proficiency in tools like Matplotlib, Seaborn, or Tableau for creating insightful graphs and charts.
- Understanding of best practices in data visualization for effective communication of findings.
4. Big Data:
- Experience working with large datasets using technologies like Hadoop, Spark, or Apache Flink.
- Knowledge of distributed computing principles and tools for processing and analyzing big data.
- Ability to optimize algorithms and processes for scalability and performance.
5. Problem-Solving:
- Strong analytical and problem-solving skills to tackle complex data-related challenges.
- Ability to formulate hypotheses, design experiments, and iterate on solutions.
- Aptitude for identifying opportunities for leveraging data to drive business outcomes and decision-making.
Resume key words for a data analyst role
1. SQL (Structured Query Language):
- SQL is a programming language used for managing and querying relational databases.
- Data analysts often use SQL to extract, manipulate, and analyze data stored in databases, making it a fundamental skill for the role.
2. Python/R:
- Python and R are popular programming languages used for data analysis and statistical computing.
- Proficiency in Python or R allows data analysts to perform various tasks such as data cleaning, modeling, visualization, and machine learning.
3. Data Visualization:
- Data visualization involves presenting data in graphical or visual formats to communicate insights effectively.
- Data analysts use tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn to create visualizations that help stakeholders understand complex data patterns and trends.
4. Statistical Analysis:
- Statistical analysis involves applying statistical methods to analyze and interpret data.
- Data analysts use statistical techniques to uncover relationships, trends, and patterns in data, providing valuable insights for decision-making.
5. Data-driven Decision Making:
- Data-driven decision making is the process of making decisions based on data analysis and evidence rather than intuition or gut feelings.
- Data analysts play a crucial role in helping organizations make informed decisions by analyzing data and providing actionable insights that drive business strategies and operations.
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Roadmap for Learning Machine Learning (ML)
Here’s a concise and point-wise roadmap for learning ML:
1. Prerequisites
- Learn programming basics (e.g., Python).
- Understand mathematics:
1 - Linear Algebra (vectors, matrices).
2 - Probability and Statistics (distributions, Bayes’ theorem).
3 - Calculus (derivatives, gradients).
4 - Familiarize yourself with data structures and algorithms.
2. Basics of Machine Learning
-Understand ML concepts:
Supervised, unsupervised, and reinforcement learning.
Training, validation, and testing datasets.
- Learn how to preprocess and clean data.
- Get familiar with Python libraries:
NumPy, Pandas, Matplotlib, and Seaborn.
3. Supervised Learning
- Study regression techniques:
Linear and Logistic Regression.
- Explore classification algorithms:
Decision Trees, Support Vector Machines (SVM), k-NN.
- Learn model evaluation metrics:
Accuracy, Precision, Recall, F1 Score, ROC-AUC.
4. Unsupervised Learning
- Learn clustering techniques:
k-Means, DBSCAN, Hierarchical Clustering.
- Understand Dimensionality Reduction:
PCA, t-SNE.
5. Advanced Concepts
- Explore ensemble methods:
Random Forest, Gradient Boosting, XGBoost, LightGBM.
- Learn hyperparameter tuning techniques:
Grid Search, Random Search.
6. Deep Learning (Optional for Advanced ML)
- Learn neural networks basics:
Forward and Backpropagation.
- Study Deep Learning libraries:
TensorFlow, PyTorch, Keras.
Explore CNNs, RNNs, and Transformers.
7. Hands-on Practice
- Work on small projects like:
1 - Predicting house prices.
2 - Sentiment analysis on tweets.
3 - Image classification.
4 - Explore Kaggle competitions and datasets.
8. Deployment
- Learn how to deploy ML models:
Use Flask, FastAPI, or Django.
- Explore cloud platforms: AWS, Azure, Google Cloud.
9. Keep Learning
- Stay updated with new techniques:
Follow blogs, papers, and conferences (e.g., NeurIPS, ICML).
- Dive into specialized fields:
NLP, Computer Vision, Reinforcement Learning.
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Complete Roadmap to learn Data Science
1. Foundational Knowledge
Mathematics and Statistics
- Linear Algebra: Understand vectors, matrices, and tensor operations.
- Calculus: Learn about derivatives, integrals, and optimization techniques.
- Probability: Study probability distributions, Bayes' theorem, and expected values.
- Statistics: Focus on descriptive statistics, hypothesis testing, regression, and statistical significance.
Programming
- Python: Start with basic syntax, data structures, and OOP concepts. Libraries to learn: NumPy, pandas, matplotlib, seaborn.
- R: Get familiar with basic syntax and data manipulation (optional but useful).
- SQL: Understand database querying, joins, aggregations, and subqueries.
2. Core Data Science Concepts
Data Wrangling and Preprocessing
- Cleaning and preparing data for analysis.
- Handling missing data, outliers, and inconsistencies.
- Feature engineering and selection.
Data Visualization
- Tools: Matplotlib, seaborn, Plotly.
- Concepts: Types of plots, storytelling with data, interactive visualizations.
Machine Learning
- Supervised Learning: Linear regression, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors.
- Unsupervised Learning: K-means clustering, hierarchical clustering, PCA.
- Advanced Techniques: Ensemble methods, gradient boosting (XGBoost, LightGBM), neural networks.
- Model Evaluation: Train-test split, cross-validation, confusion matrix, ROC-AUC.
3. Advanced Topics
Deep Learning
- Frameworks: TensorFlow, Keras, PyTorch.
- Concepts: Neural networks, CNNs, RNNs, LSTMs, GANs.
Natural Language Processing (NLP)
- Basics: Text preprocessing, tokenization, stemming, lemmatization.
- Advanced: Sentiment analysis, topic modeling, word embeddings (Word2Vec, GloVe), transformers (BERT, GPT).
Big Data Technologies
- Frameworks: Hadoop, Spark.
- Databases: NoSQL databases (MongoDB, Cassandra).
4. Practical Experience
Projects
- Start with small datasets (Kaggle, UCI Machine Learning Repository).
- Progress to more complex projects involving real-world data.
- Work on end-to-end projects, from data collection to model deployment.
Competitions and Challenges
- Participate in Kaggle competitions.
- Engage in hackathons and coding challenges.
5. Soft Skills and Tools
Communication
- Learn to present findings clearly and concisely.
- Practice writing reports and creating dashboards (Tableau, Power BI).
Collaboration Tools
- Version Control: Git and GitHub.
- Project Management: JIRA, Trello.
6. Continuous Learning and Networking
Staying Updated
- Follow data science blogs, podcasts, and research papers.
- Join professional groups and forums (LinkedIn, Kaggle, Reddit, DataSimplifier).
7. Specialization
After gaining a broad understanding, you might want to specialize in areas such as:
- Data Engineering
- Business Analytics
- Computer Vision
- AI and Machine Learning Research
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WHISTLEBLOWER: Musk ordered X employees to manipulate the algorithm during 2024 United States Presidential Election
💥 Anonymous Whistleblower Letter dated 01/10/2025: A former X employee claims their team was ordered to deliberately interfere in the 2024 U.S. elections.
📌 What happened?
🔹 AI systems (Grok and Eliza) generated thousands of fake accounts that shaped public opinion
🔹 Elon Musk ordered algorithm changes – boosting right-wing posts while creating an illusion of balance by sprinkling in Democrat discourse. He was directly involved and called himself Black Hat MAGA. Sound familiar?
🔹 The interference wasn’t limited to the U.S. – it affected users worldwide
🔹 Musk is now using his platform to do the same in Europe, notably Germany
❗️Thousands of accounts vanished "like magic” after it was clear Trump would be sworn in – did you notice?
The Whistleblower says they left “breadcrumbs” in the code, and provided the following link
https://elizaos.github.io/eliza/docs/core/characterfile/ for more evidence.
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Top 10 Python Libraries for Data Science & Machine Learning
1. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.
2. Pandas: Pandas is a powerful data manipulation library that provides data structures like DataFrame and Series, which make it easy to work with structured data. It offers tools for data cleaning, reshaping, merging, and slicing data.
3. Matplotlib: Matplotlib is a plotting library for creating static, interactive, and animated visualizations in Python. It allows you to generate various types of plots, including line plots, bar charts, histograms, scatter plots, and more.
4. Scikit-learn: Scikit-learn is a machine learning library that provides simple and efficient tools for data mining and data analysis. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and model selection.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google. It enables you to build and train deep learning models using high-level APIs and tools for neural networks, natural language processing, computer vision, and more.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It allows you to quickly prototype deep learning models with minimal code and easily experiment with different architectures.
7. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a high-level interface for creating attractive and informative statistical graphics. It simplifies the process of creating complex visualizations like heatmaps, violin plots, and pair plots.
8. Statsmodels: Statsmodels is a library that focuses on statistical modeling and hypothesis testing in Python. It offers a wide range of statistical models, including linear regression, logistic regression, time series analysis, and more.
9. XGBoost: XGBoost is an optimized gradient boosting library that provides an efficient implementation of the gradient boosting algorithm. It is widely used in machine learning competitions and has become a popular choice for building accurate predictive models.
10. NLTK (Natural Language Toolkit): NLTK is a library for natural language processing (NLP) that provides tools for text processing, tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and more. It is a valuable resource for working with textual data in data science projects.
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Key Concepts for Machine Learning Interviews
1. Supervised Learning: Understand the basics of supervised learning, where models are trained on labeled data. Key algorithms include Linear Regression, Logistic Regression, Support Vector Machines (SVMs), k-Nearest Neighbors (k-NN), Decision Trees, and Random Forests.
2. Unsupervised Learning: Learn unsupervised learning techniques that work with unlabeled data. Familiarize yourself with algorithms like k-Means Clustering, Hierarchical Clustering, Principal Component Analysis (PCA), and t-SNE.
3. Model Evaluation Metrics: Know how to evaluate models using metrics such as accuracy, precision, recall, F1 score, ROC-AUC, mean squared error (MSE), and R-squared. Understand when to use each metric based on the problem at hand.
4. Overfitting and Underfitting: Grasp the concepts of overfitting and underfitting, and know how to address them through techniques like cross-validation, regularization (L1, L2), and pruning in decision trees.
5. Feature Engineering: Master the art of creating new features from raw data to improve model performance. Techniques include one-hot encoding, feature scaling, polynomial features, and feature selection methods like Recursive Feature Elimination (RFE).
6. Hyperparameter Tuning: Learn how to optimize model performance by tuning hyperparameters using techniques like Grid Search, Random Search, and Bayesian Optimization.
7. Ensemble Methods: Understand ensemble learning techniques that combine multiple models to improve accuracy. Key methods include Bagging (e.g., Random Forests), Boosting (e.g., AdaBoost, XGBoost, Gradient Boosting), and Stacking.
8. Neural Networks and Deep Learning: Get familiar with the basics of neural networks, including activation functions, backpropagation, and gradient descent. Learn about deep learning architectures like Convolutional Neural Networks (CNNs) for image data and Recurrent Neural Networks (RNNs) for sequential data.
9. Natural Language Processing (NLP): Understand key NLP techniques such as tokenization, stemming, and lemmatization, as well as advanced topics like word embeddings (e.g., Word2Vec, GloVe), transformers (e.g., BERT, GPT), and sentiment analysis.
10. Dimensionality Reduction: Learn how to reduce the number of features in a dataset while preserving as much information as possible. Techniques include PCA, Singular Value Decomposition (SVD), and Feature Importance methods.
11. Reinforcement Learning: Gain a basic understanding of reinforcement learning, where agents learn to make decisions by receiving rewards or penalties. Familiarize yourself with concepts like Markov Decision Processes (MDPs), Q-learning, and policy gradients.
12. Big Data and Scalable Machine Learning: Learn how to handle large datasets and scale machine learning algorithms using tools like Apache Spark, Hadoop, and distributed frameworks for training models on big data.
13. Model Deployment and Monitoring: Understand how to deploy machine learning models into production environments and monitor their performance over time. Familiarize yourself with tools and platforms like TensorFlow Serving, AWS SageMaker, Docker, and Flask for model deployment.
14. Ethics in Machine Learning: Be aware of the ethical implications of machine learning, including issues related to bias, fairness, transparency, and accountability. Understand the importance of creating models that are not only accurate but also ethically sound.
15. Bayesian Inference: Learn about Bayesian methods in machine learning, which involve updating the probability of a hypothesis as more evidence becomes available. Key concepts include Bayes’ theorem, prior and posterior distributions, and Bayesian networks.
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Top 5 Tools to master Data Analytics
1. Python:
- Versatile programming language.
- Offers powerful libraries like Pandas, NumPy, and Scikit-learn.
- Used for data manipulation, analysis, and machine learning tasks.
2. R:
- Statistical programming language.
- Provides extensive statistical capabilities.
- Popular for data analysis in academia.
- Offers visualization libraries like ggplot2.
3. SQL (Structured Query Language):
- Essential for working with relational databases.
- Allows querying, manipulation, and management of data.
- Standard language for database management systems.
4. Tableau:
- Data visualization tool.
- Enables creation of interactive dashboards.
- Helps in communicating insights effectively.
- Widely used in business intelligence.
5. Apache Spark:
- Framework for large-scale data processing.
- Offers distributed computing capabilities.
- Libraries like Spark SQL and MLlib for data manipulation and machine learning.
- Ideal for processing big data efficiently.
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Python
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Data visualization
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Advanced Sql
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Intro to ML
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Advanced ML
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Essential Tools and Libraries for Data Science Students
1. Programming Languages:
Python
R
SQL
2. Python Libraries:
NumPy: For numerical computations.
Pandas: For data manipulation and analysis.
Matplotlib: For basic data visualization.
Seaborn: For statistical data visualization.
Scikit-learn: For machine learning models.
TensorFlow: For deep learning.
PyTorch: For advanced neural networks.
3. R Libraries:
ggplot2: For data visualization.
dplyr: For data manipulation.
caret: For machine learning.
shiny: For building interactive web apps.
4. Data Visualization Tools:
Tableau
Power BI
Google Data Studio
5. Big Data Tools:
Apache Hadoop
Apache Spark
6. Cloud Platforms:
AWS (Amazon Web Services)
Google Cloud Platform (GCP)
Microsoft Azure
7. Statistical Software:
SAS
SPSS
8. Version Control System:
Git
9. Notebook Tools:
Jupyter Notebook
Google Colab
10. Data Sources for Practice:
Kaggle Datasets
UCI Machine Learning Repository
GitHub Repositories
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