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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.

Join @datasciencefree for more

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Starting your career in data science is an exciting step into a field that blends statistics, programming, and domain expertise. As you gain experience, you might discover new specializations that align with your passions:

• Machine Learning: If you're fascinated by building predictive models and automating decision-making processes, diving deeper into machine learning could be your next move.

• Deep Learning: If working with neural networks and advanced AI models excites you, focusing on deep learning might be your calling, especially for projects involving computer vision, natural language processing, or speech recognition.

• Natural Language Processing (NLP): If you're intrigued by the challenge of teaching machines to understand and generate human language, NLP could be a compelling area to explore.

• Data Engineering: If you enjoy building and managing the infrastructure that supports data science projects, transitioning to a data engineering role could be a great fit.

• Research Scientist: If you're passionate about pushing the boundaries of what's possible with data and algorithms, you might find fulfillment as a research scientist, working on cutting-edge innovations.

Even if you choose to stay within the broad realm of data science, there’s always something new to explore, especially with the rapid advancements in AI and big data technologies.

The key is to keep learning, experimenting, and refining your skills. Each step you take in data science opens up new opportunities to make impactful contributions in various industries.

Top free Data Science resources

@datasciencefun

1. CS109 Data Science
http://cs109.github.io/2015/pages/videos.html

2. ML Crash Course by Google
https://developers.google.com/machine-learning/crash-course/

3. Learning From Data from California Institute of Technology
http://work.caltech.edu/telecourse

4. Mathematics for Machine Learning by University of California, Berkeley
https://gwthomas.github.io/docs/math4ml.pdf?fbclid=IwAR2UsBgZW9MRgS3nEo8Zh_ukUFnwtFeQS8Ek3OjGxZtDa7UxTYgIs_9pzSI

5. Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravindran Kannan
https://www.cs.cornell.edu/jeh/book.pdf?fbclid=IwAR19tDrnNh8OxAU1S-tPklL1mqj-51J1EJUHmcHIu2y6yEv5ugrWmySI2WY

6. Python Data Science Handbook
https://jakevdp.github.io/PythonDataScienceHandbook/?fbclid=IwAR34IRk2_zZ0ht7-8w5rz13N6RP54PqjarQw1PTpbMqKnewcwRy0oJ-Q4aM

7.  CS 221 ― Artificial Intelligence
https://stanford.edu/~shervine/teaching/cs-221/

8. Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science
https://ocw.mit.edu/courses/mathematics/18-s096-topics-in-mathematics-of-data-science-fall-2015/lecture-notes/MIT18_S096F15_TenLec.pdf

9. Python for Data Analysis by Boston University
https://www.bu.edu/tech/files/2017/09/Python-for-Data-Analysis.pptx

10.  Data Mining bu University of Buffalo
https://cedar.buffalo.edu/~srihari/CSE626/index.html?fbclid=IwAR3XZ50uSZAb3u5BP1Qz68x13_xNEH8EdEBQC9tmGEp1BoxLNpZuBCtfMSE

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10 commonly asked data science interview questions along with their answers

1️⃣ What is the difference between supervised and unsupervised learning?
Supervised learning involves learning from labeled data to predict outcomes while unsupervised learning involves finding patterns in unlabeled data.

2️⃣ Explain the bias-variance tradeoff in machine learning.
The bias-variance tradeoff is a key concept in machine learning. Models with high bias have low complexity and over-simplify, while models with high variance are more complex and over-fit to the training data. The goal is to find the right balance between bias and variance.

3️⃣ What is the Central Limit Theorem and why is it important in statistics?
The Central Limit Theorem (CLT) states that the sampling distribution of the sample means will be approximately normally distributed regardless of the underlying population distribution, as long as the sample size is sufficiently large. It is important because it justifies the use of statistics, such as hypothesis testing and confidence intervals, on small sample sizes.

4️⃣ Describe the process of feature selection and why it is important in machine learning.
Feature selection is the process of selecting the most relevant features (variables) from a dataset. This is important because unnecessary features can lead to over-fitting, slower training times, and reduced accuracy.

5️⃣ What is the difference between overfitting and underfitting in machine learning? How do you address them?
Overfitting occurs when a model is too complex and fits the training data too well, resulting in poor performance on unseen data. Underfitting occurs when a model is too simple and cannot fit the training data well enough, resulting in poor performance on both training and unseen data. Techniques to address overfitting include regularization and early stopping, while techniques to address underfitting include using more complex models or increasing the amount of input data.

6️⃣ What is regularization and why is it used in machine learning?
Regularization is a technique used to prevent overfitting in machine learning. It involves adding a penalty term to the loss function to limit the complexity of the model, effectively reducing the impact of certain features.

7️⃣ How do you handle missing data in a dataset?
Handling missing data can be done by either deleting the missing samples, imputing the missing values, or using models that can handle missing data directly.

8️⃣ What is the difference between classification and regression in machine learning?
Classification is a type of supervised learning where the goal is to predict a categorical or discrete outcome, while regression is a type of supervised learning where the goal is to predict a continuous or numerical outcome.

9️⃣ Explain the concept of cross-validation and why it is used.
Cross-validation is a technique used to evaluate the performance of a machine learning model. It involves spliting the data into training and validation sets, and then training and evaluating the model on multiple such splits. Cross-validation gives a better idea of the model's generalization ability and helps prevent over-fitting.

🔟 What evaluation metrics would you use to evaluate a binary classification model?
Some commonly used evaluation metrics for binary classification models are accuracy, precision, recall, F1 score, and ROC-AUC. The choice of metric depends on the specific requirements of the problem.

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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.

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Feature Scaling is one of the most useful and necessary transformations to perform on a training dataset, since with very few exceptions, ML algorithms do not fit well to datasets with attributes that have very different scales.

Let's talk about it 🧵

There are 2 very effective techniques to transform all the attributes of a dataset to the same scale, which are:
▪️ Normalization
▪️ Standardization

The 2 techniques perform the same task, but in different ways. Moreover, each one has its strengths and weaknesses.

Normalization (min-max scaling) is very simple: values are shifted and rescaled to be in the range of 0 and 1.

This is achieved by subtracting each value by the min value and dividing the result by the difference between the max and min value.

In contrast, Standardization first subtracts the mean value (so that the values always have zero mean) and then divides the result by the standard deviation (so that the resulting distribution has unit variance).

More about them:
▪️Standardization doesn't frame the data between the range 0-1, which is undesirable for some algorithms.
▪️Standardization is robust to outliers.
▪️Normalization is sensitive to outliers. A very large value may squash the other values in the range 0.0-0.2.

Both algorithms are implemented in the Scikit-learn Python library and are very easy to use. Check below Google Colab code with a toy example, where you can see how each technique works.

https://colab.research.google.com/drive/1DsvTezhnwfS7bPAeHHHHLHzcZTvjBzLc?usp=sharing

Check below spreadsheet, where you can see another example, step by step, of how to normalize and standardize your data.

https://docs.google.com/spreadsheets/d/14GsqJxrulv2CBW_XyNUGoA-f9l-6iKuZLJMcc2_5tZM/edit?usp=drivesdk

Well, the real benefit of feature scaling is when you want to train a model from a dataset with many features (e.g., m > 10) and these features have very different scales (different orders of magnitude). For NN this preprocessing is key.

Enable gradient descent to converge faster

Complete Data Science Roadmap 
👇👇 

1. Introduction to Data Science 
   - What is Data Science? 
   - Importance of Data Science 
   - Data Science Lifecycle 
   - Roles in Data Science (Data Scientist, Data Engineer, etc.) 

2. Mathematics and Statistics for Data Science 
   - Probability and Distributions 
   - Denoscriptive and Inferential Statistics 
   - Hypothesis Testing 
   - Linear Algebra 
   - Calculus Basics 

3. Python for Data Science 
   - Python Basics (Variables, Loops, Functions) 
   - Libraries for Data Science: NumPy, Pandas, Matplotlib, Seaborn 
   - Data Manipulation with Pandas 
   - Data Visualization with Matplotlib and Seaborn 
   - Jupyter Notebooks for Data Analysis 

4. R Programming for Data Science 
   - Introduction to R 
   - R Libraries: dplyr, ggplot2, tidyr 
   - Data Manipulation in R 
   - Data Visualization in R 
   - R Markdown for Reporting 

5. Data Collection and Preprocessing 
   - Data Collection Techniques 
   - Cleaning and Wrangling Data 
   - Handling Missing Data 
   - Feature Engineering 
   - Scaling and Normalization 

6. Exploratory Data Analysis (EDA) 
   - Understanding the Dataset 
   - Summary Statistics 
   - Data Visualization (Histograms, Box Plots, Scatter Plots) 
   - Correlation and Covariance 
   - Identifying Patterns and Trends 

7. Databases for Data Science 
   - Introduction to SQL 
   - CRUD Operations 
   - SQL Joins, Group By, Aggregations 
   - Working with NoSQL Databases (MongoDB) 
   - Database Normalization 

8. Machine Learning Fundamentals 
   - Supervised vs Unsupervised Learning 
   - Linear Regression, Logistic Regression 
   - Decision Trees and Random Forests 
   - K-Nearest Neighbors (KNN) 
   - K-Means Clustering 

9. Advanced Machine Learning 
   - Support Vector Machines (SVM) 
   - Ensemble Methods (Bagging, Boosting) 
   - Principal Component Analysis (PCA) 
   - Neural Networks Basics 
   - Model Selection and Cross-Validation 

10. Deep Learning 
    - Introduction to Deep Learning 
    - Neural Networks Architecture 
    - Activation Functions 
    - Convolutional Neural Networks (CNNs) 
    - Recurrent Neural Networks (RNNs) 

11. Natural Language Processing (NLP) 
    - Introduction to NLP 
    - Text Preprocessing (Tokenization, Lemmatization, Stop Words) 
    - Sentiment Analysis 
    - Named Entity Recognition (NER) 
    - Word Embeddings (Word2Vec, GloVe) 

12. Time Series Analysis 
    - Introduction to Time Series Data 
    - Stationarity and Autocorrelation 
    - ARIMA Models 
    - Forecasting Techniques 
    - Seasonal Decomposition of Time Series (STL) 

13. Big Data Technologies 
    - Introduction to Big Data 
    - Hadoop Ecosystem (HDFS, MapReduce) 
    - Apache Spark 
    - Data Processing with PySpark 
    - Distributed Computing Basics 

14. Data Visualization and Storytelling 
    - Creating Dashboards (Tableau, Power BI) 
    - Advanced Data Visualization (Heatmaps, Network Graphs) 
    - Interactive Visualizations (Plotly, Bokeh) 
    - Telling a Story with Data 
    - Best Practices for Data Presentation 

15. Model Deployment and MLOps 
    - Model Deployment with Flask and Django 
    - Docker for Packaging Models 
    - CI/CD for Machine Learning Models 
    - Monitoring and Retraining Models 
    - MLOps Best Practices 

16. Cloud for Data Science 
    - AWS, Google Cloud, Microsoft Azure for Data Science 
    - Cloud Storage (S3, Azure Blob Storage) 
    - Using Cloud-Based Jupyter Notebooks 
    - Machine Learning Services (SageMaker, Google AI Platform) 
    - Cloud Databases 

17. Data Engineering 
    - Data Pipelines (ETL/ELT) 
    - Data Warehousing (Redshift, BigQuery) 
    - Batch Processing vs Stream Processing 
    - Data Lake vs Data Warehouse 
    - Tools like Apache Airflow, Kafka

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