Learn Data Science in 2024

𝟭. 𝗔𝗽𝗽𝗹𝘆 𝗣𝗮𝗿𝗲𝘁𝗼'𝘀 𝗟𝗮𝘄 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻 𝗝𝘂𝘀𝘁 𝗘𝗻𝗼𝘂𝗴𝗵 📚

Pareto's Law states that "that 80% of consequences come from 20% of the causes".

This law should serve as a guiding framework for the volume of content you need to know to be proficient in data science.

Often rookies make the mistake of overspending their time learning algorithms that are rarely applied in production. Learning about advanced algorithms such as XLNet, Bayesian SVD++, and BiLSTMs, are cool to learn.

But, in reality, you will rarely apply such algorithms in production (unless your job demands research and application of state-of-the-art algos).

For most ML applications in production - especially in the MVP phase, simple algos like logistic regression, K-Means, random forest, and XGBoost provide the biggest bang for the buck because of their simplicity in training, interpretation and productionization.

So, invest more time learning topics that provide immediate value now, not a year later.

𝟮. 𝗙𝗶𝗻𝗱 𝗮 𝗠𝗲𝗻𝘁𝗼𝗿 ⚡

There’s a Japanese proverb that says “Better than a thousand days of diligent study is one day with a great teacher.” This proverb directly applies to learning data science quickly.

Mentors can teach you about how to build a model in production and how to manage stakeholders - stuff that you don’t often read about in courses and books.

So, find a mentor who can teach you practical knowledge in data science.

𝟯. 𝗗𝗲𝗹𝗶𝗯𝗲𝗿𝗮𝘁𝗲 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ✍️

If you are serious about growing your excelling in data science, you have to put in the time to nurture your knowledge. This means that you need to spend less time watching mindless videos on TikTok and spend more time reading books and watching video lectures.

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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.

Data Science Resources for Beginners
👇👇
https://drive.google.com/drive/folders/1uCShXgmol-fGMqeF2hf9xA5XPKVSxeTo

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Introduction to Data Science: Complete Guide for Beginners
👇👇
https://medium.com/@data_analyst/introduction-to-data-science-complete-guide-for-beginners-af0517923d61

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▎Essential Data Science Concepts Everyone Should Know:

1. Data Types and Structures:

• Categorical: Nominal (unordered, e.g., colors) and Ordinal (ordered, e.g., education levels)

• Numerical: Discrete (countable, e.g., number of children) and Continuous (measurable, e.g., height)

• Data Structures: Arrays, Lists, Dictionaries, DataFrames (for organizing and manipulating data)

2. Denoscriptive Statistics:

• Measures of Central Tendency: Mean, Median, Mode (describing the typical value)

• Measures of Dispersion: Variance, Standard Deviation, Range (describing the spread of data)

• Visualizations: Histograms, Boxplots, Scatterplots (for understanding data distribution)

3. Probability and Statistics:

• Probability Distributions: Normal, Binomial, Poisson (modeling data patterns)

• Hypothesis Testing: Formulating and testing claims about data (e.g., A/B testing)

• Confidence Intervals: Estimating the range of plausible values for a population parameter

4. Machine Learning:

• Supervised Learning: Regression (predicting continuous values) and Classification (predicting categories)

• Unsupervised Learning: Clustering (grouping similar data points) and Dimensionality Reduction (simplifying data)

• Model Evaluation: Accuracy, Precision, Recall, F1-score (assessing model performance)

5. Data Cleaning and Preprocessing:

• Missing Value Handling: Imputation, Deletion (dealing with incomplete data)

• Outlier Detection and Removal: Identifying and addressing extreme values

• Feature Engineering: Creating new features from existing ones (e.g., combining variables)

6. Data Visualization:

• Types of Charts: Bar charts, Line charts, Pie charts, Heatmaps (for communicating insights visually)

• Principles of Effective Visualization: Clarity, Accuracy, Aesthetics (for conveying information effectively)

7. Ethical Considerations in Data Science:

• Data Privacy and Security: Protecting sensitive information

• Bias and Fairness: Ensuring algorithms are unbiased and fair

8. Programming Languages and Tools:

• Python: Popular for data science with libraries like NumPy, Pandas, Scikit-learn

• R: Statistical programming language with strong visualization capabilities

• SQL: For querying and manipulating data in databases

9. Big Data and Cloud Computing:

• Hadoop and Spark: Frameworks for processing massive datasets

• Cloud Platforms: AWS, Azure, Google Cloud (for storing and analyzing data)

10. Domain Expertise:

• Understanding the Data: Knowing the context and meaning of data is crucial for effective analysis

• Problem Framing: Defining the right questions and objectives for data-driven decision making

Bonus:

• Data Storytelling: Communicating insights and findings in a clear and engaging manner

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

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Here are 25 most common Deep Learning interview questions for ML research positions:

Fundamentals:
- What is deep learning, and how does it differ from traditional machine learning?
- What is an activation function, and why is it important? Explain three types of activation functions.
- You are using a deep neural network for prediction, but it overfits the training data. What can you do to reduce overfitting?
- What is the vanishing gradient problem in neural networks, and how can it be fixed?
- Explain the process of backpropagation.

Neural Network Architectures:
- Describe the architecture of a typical Convolutional Neural Network (CNN).
- What are Autoencoders, and what are three practical uses of them?
- What is a transformer architecture, and how is it used in NLP tasks?
- What is the role of pooling layers in CNNs?
- What are Recurrent Neural Networks (RNNs), and where are they used?

Training and Optimization:
- How does L1/L2 regularization affect a neural network?
- Why should we use Batch Normalization?
- How do you know if your model is suffering from exploding gradients?
- What is the purpose of dropout in neural networks, and how does it affect training?
- What are some hyperparameters used in training neural networks?

Advanced Topics:
- What are the main gates in LSTM networks, and what are their tasks?
- Explain how self-attention works in transformers.
- Can CNNs be used to classify 1D signals?
- What is transfer learning, and when is it recommended or not?
- How do depthwise separable convolutions improve CNNs?

Practical Implementation:
- Describe the process of pre-training and fine-tuning in transformers.
- What are the main challenges when training a deep learning model with limited data?
- How do you handle class imbalance in deep learning?
- What are the challenges of deploying deep learning models in production?
- How would you modify a pre-trained model from classification to regression?

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9 tips to improve your code:

- Declare variables close to usage
- Functions do 1 thing
- Avoid long functions
- Avoid long lines
- Don't repeat code
- Use denoscriptive variable/function names
- Use few arguments
- Simplify conditions (return age >17;)
- Remove unused code

Without errors, No-one can become a good programmer.
Errors are the most important phase of learning to code.

#coding

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.


Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

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Top three most required tech stack for the following roles:

1. Data Analyst: SQL, Excel, Tableau/Power BI
2. Data Scientist: Python, R, SQL
3. Quantitative Analyst: Python, R, MATLAB
4. Business Analyst: SQL, Business Requirements Gathering, Agile Methodologies, Power BI/Tableau
5. Data Engineer: Python/Scala, SQL, Cloud, Apache Spark
6. Machine Learning Engineer: Python, TensorFlow/PyTorch, Docker/Kubernetes.

10 great Python packages for Data Science not known to many:

1️⃣ CleanLab

Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.

2️⃣ LazyPredict

A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code.

3️⃣ Lux

A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data.

4️⃣ PyForest

A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code.

5️⃣ PivotTableJS

PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥

6️⃣ Drawdata

Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook.

7️⃣ black

The Uncompromising Code Formatter

8️⃣ PyCaret

An open-source, low-code machine learning library in Python that automates the machine learning workflow.

9️⃣ PyTorch-Lightning by LightningAI

Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation.

🔟 Streamlit

A framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data viz & model deployment.

Evaluating Machine Learning Agents on Machine Learning Engineering

We introduce MLE-bench, a benchmark for measuring how well AI agents perform at machine learning engineering. To this end, we curate 75 ML engineering-related competitions from Kaggle, creating a diverse set of challenging tasks that test real-world ML engineering skills such as training models, preparing datasets, and running experiments. We establish human baselines for each competition using Kaggle's publicly available leaderboards. We use open-source agent scaffolds to evaluate several frontier language models on our benchmark, finding that the best-performing setup — OpenAI's o1-preview with AIDE scaffolding — achieves at least the level of a Kaggle bronze medal in 16.9% of competitions. In addition to our main results, we investigate various forms of resource-scaling for AI agents and the impact of contamination from pre-training.

Top 10 important data science concepts

1. Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in a dataset. It is a crucial step in the data science pipeline as it ensures the quality and reliability of the data.

2. Exploratory Data Analysis (EDA): EDA is the process of analyzing and visualizing data to gain insights and understand the underlying patterns and relationships. It involves techniques such as summary statistics, data visualization, and correlation analysis.

3. Feature Engineering: Feature engineering is the process of creating new features or transforming existing features in a dataset to improve the performance of machine learning models. It involves techniques such as encoding categorical variables, scaling numerical variables, and creating interaction terms.

4. Machine Learning Algorithms: Machine learning algorithms are mathematical models that learn patterns and relationships from data to make predictions or decisions. Some important machine learning algorithms include linear regression, logistic regression, decision trees, random forests, support vector machines, and neural networks.

5. Model Evaluation and Validation: Model evaluation and validation involve assessing the performance of machine learning models on unseen data. It includes techniques such as cross-validation, confusion matrix, precision, recall, F1 score, and ROC curve analysis.

6. Feature Selection: Feature selection is the process of selecting the most relevant features from a dataset to improve model performance and reduce overfitting. It involves techniques such as correlation analysis, backward elimination, forward selection, and regularization methods.

7. Dimensionality Reduction: Dimensionality reduction techniques are used to reduce the number of features in a dataset while preserving the most important information. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are common dimensionality reduction techniques.

8. Model Optimization: Model optimization involves fine-tuning the parameters and hyperparameters of machine learning models to achieve the best performance. Techniques such as grid search, random search, and Bayesian optimization are used for model optimization.

9. Data Visualization: Data visualization is the graphical representation of data to communicate insights and patterns effectively. It involves using charts, graphs, and plots to present data in a visually appealing and understandable manner.

10. Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets that cannot be processed using traditional data processing techniques. It involves technologies such as Hadoop, Spark, and distributed computing to extract insights from massive amounts of data.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

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Top Platforms for Building Data Science Portfolio

Build an irresistible portfolio that hooks recruiters with these free platforms.

Landing a job as a data scientist begins with building your portfolio with a comprehensive list of all your projects. To help you get started with building your portfolio, here is the list of top data science platforms. Remember the stronger your portfolio, the better chances you have of landing your dream job.

1. GitHub
2. Kaggle
3. LinkedIn
4. Medium
5. MachineHack
6. DagsHub
7. HuggingFace

#datascienceprojects

Most Important Mathematical Equations in Data Science!

1️⃣ Gradient Descent: Optimization algorithm minimizing the cost function.
2️⃣ Normal Distribution: Distribution characterized by mean μ\muμ and variance σ2\sigma^2σ2.
3️⃣ Sigmoid Function: Activation function mapping real values to 0-1 range.
4️⃣ Linear Regression: Predictive model of linear input-output relationships.
5️⃣ Cosine Similarity: Metric for vector similarity based on angle cosine.
6️⃣ Naive Bayes: Classifier using Bayes’ Theorem and feature independence.
7️⃣ K-Means: Clustering minimizing distances to cluster centroids.
8️⃣ Log Loss: Performance measure for probability output models.
9️⃣ Mean Squared Error (MSE): Average of squared prediction errors.
🔟 MSE (Bias-Variance Decomposition): Explains MSE through bias and variance.
1️⃣1️⃣ MSE + L2 Regularization: Adds penalty to prevent overfitting.
1️⃣2️⃣ Entropy: Uncertainty measure used in decision trees.
1️⃣3️⃣ Softmax: Converts logits to probabilities for classification.
1️⃣4️⃣ Ordinary Least Squares (OLS): Estimates regression parameters by minimizing residuals.
1️⃣5️⃣ Correlation: Measures linear relationships between variables.
1️⃣6️⃣ Z-score: Standardizes value based on standard deviations from mean.
1️⃣7️⃣ Maximum Likelihood Estimation (MLE): Estimates parameters maximizing data likelihood.
1️⃣8️⃣ Eigenvectors and Eigenvalues: Characterize linear transformations in matrices.
1️⃣9️⃣ R-squared (R²): Proportion of variance explained by regression.
2️⃣0️⃣ F1 Score: Harmonic mean of precision and recall.
2️⃣1️⃣ Expected Value: Weighted average of all possible values.

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