Data Scientist Roadmap
|
|-- 1. Basic Foundations
|   |-- a. Mathematics
|   |   |-- i. Linear Algebra
|   |   |-- ii. Calculus
|   |   |-- iii. Probability
|   |   -- iv. Statistics
|   |
|   |-- b. Programming
|   |   |-- i. Python
|   |   |   |-- 1. Syntax and Basic Concepts
|   |   |   |-- 2. Data Structures
|   |   |   |-- 3. Control Structures
|   |   |   |-- 4. Functions
|   |   |   -- 5. Object-Oriented Programming
|   |   |
|   |   -- ii. R (optional, based on preference)
|   |
|   |-- c. Data Manipulation
|   |   |-- i. Numpy (Python)
|   |   |-- ii. Pandas (Python)
|   |   -- iii. Dplyr (R)
|   |
|   -- d. Data Visualization
|       |-- i. Matplotlib (Python)
|       |-- ii. Seaborn (Python)
|       -- iii. ggplot2 (R)
|
|-- 2. Data Exploration and Preprocessing
|   |-- a. Exploratory Data Analysis (EDA)
|   |-- b. Feature Engineering
|   |-- c. Data Cleaning
|   |-- d. Handling Missing Data
|   -- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
|   |-- a. Supervised Learning
|   |   |-- i. Regression
|   |   |   |-- 1. Linear Regression
|   |   |   -- 2. Polynomial Regression
|   |   |
|   |   -- ii. Classification
|   |       |-- 1. Logistic Regression
|   |       |-- 2. k-Nearest Neighbors
|   |       |-- 3. Support Vector Machines
|   |       |-- 4. Decision Trees
|   |       -- 5. Random Forest
|   |
|   |-- b. Unsupervised Learning
|   |   |-- i. Clustering
|   |   |   |-- 1. K-means
|   |   |   |-- 2. DBSCAN
|   |   |   -- 3. Hierarchical Clustering
|   |   |
|   |   -- ii. Dimensionality Reduction
|   |       |-- 1. Principal Component Analysis (PCA)
|   |       |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
|   |       -- 3. Linear Discriminant Analysis (LDA)
|   |
|   |-- c. Reinforcement Learning
|   |-- d. Model Evaluation and Validation
|   |   |-- i. Cross-validation
|   |   |-- ii. Hyperparameter Tuning
|   |   -- iii. Model Selection
|   |
|   -- e. ML Libraries and Frameworks
|       |-- i. Scikit-learn (Python)
|       |-- ii. TensorFlow (Python)
|       |-- iii. Keras (Python)
|       -- iv. PyTorch (Python)
|
|-- 4. Deep Learning
|   |-- a. Neural Networks
|   |   |-- i. Perceptron
|   |   -- ii. Multi-Layer Perceptron
|   |
|   |-- b. Convolutional Neural Networks (CNNs)
|   |   |-- i. Image Classification
|   |   |-- ii. Object Detection
|   |   -- iii. Image Segmentation
|   |
|   |-- c. Recurrent Neural Networks (RNNs)
|   |   |-- i. Sequence-to-Sequence Models
|   |   |-- ii. Text Classification
|   |   -- iii. Sentiment Analysis
|   |
|   |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
|   |   |-- i. Time Series Forecasting
|   |   -- ii. Language Modeling
|   |
|   -- e. Generative Adversarial Networks (GANs)
|       |-- i. Image Synthesis
|       |-- ii. Style Transfer
|       -- iii. Data Augmentation
|
|-- 5. Big Data Technologies
|   |-- a. Hadoop
|   |   |-- i. HDFS
|   |   -- ii. MapReduce
|   |
|   |-- b. Spark
|   |   |-- i. RDDs
|   |   |-- ii. DataFrames
|   |   -- iii. MLlib
|   |
|   -- c. NoSQL Databases
|       |-- i. MongoDB
|       |-- ii. Cassandra
|       |-- iii. HBase
|       -- iv. Couchbase
|
|-- 6. Data Visualization and Reporting
|   |-- a. Dashboarding Tools
|   |   |-- i. Tableau
|   |   |-- ii. Power BI
|   |   |-- iii. Dash (Python)
|   |   -- iv. Shiny (R)
|   |
|   |-- b. Storytelling with Data
|   -- c. Effective Communication
|
|-- 7. Domain Knowledge and Soft Skills
|   |-- a. Industry-specific Knowledge
|   |-- b. Problem-solving
|   |-- c. Communication Skills
|   |-- d. Time Management
|   -- e. Teamwork
|
-- 8. Staying Updated and Continuous Learning
    |-- a. Online Courses
    |-- b. Books and Research Papers
    |-- c. Blogs and Podcasts
    |-- d. Conferences and Workshops
    `-- e. Networking and Community Engagement

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

All the best 👍👍

Complete Machine Learning Roadmap
👇👇

1. Introduction to Machine Learning
- Definition
- Purpose
- Types of Machine Learning (Supervised, Unsupervised, Reinforcement)

2. Mathematics for Machine Learning
- Linear Algebra
- Calculus
- Statistics and Probability

3. Programming Languages for ML
- Python and Libraries (NumPy, Pandas, Matplotlib)
- R

4. Data Preprocessing
- Handling Missing Data
- Feature Scaling
- Data Transformation

5. Exploratory Data Analysis (EDA)
- Data Visualization
- Denoscriptive Statistics

6. Supervised Learning
- Regression
- Classification
- Model Evaluation

7. Unsupervised Learning
- Clustering (K-Means, Hierarchical)
- Dimensionality Reduction (PCA)

8. Model Selection and Evaluation
- Cross-Validation
- Hyperparameter Tuning
- Evaluation Metrics (Precision, Recall, F1 Score)

9. Ensemble Learning
- Random Forest
- Gradient Boosting

10. Neural Networks and Deep Learning
- Introduction to Neural Networks
- Building and Training Neural Networks
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)

11. Natural Language Processing (NLP)
- Text Preprocessing
- Sentiment Analysis
- Named Entity Recognition (NER)

12. Reinforcement Learning
- Basics
- Markov Decision Processes
- Q-Learning

13. Machine Learning Frameworks
- TensorFlow
- PyTorch
- Scikit-Learn

14. Deployment of ML Models
- Flask for Web Deployment
- Docker and Kubernetes

15. Ethical and Responsible AI
- Bias and Fairness
- Ethical Considerations

16. Machine Learning in Production
- Model Monitoring
- Continuous Integration/Continuous Deployment (CI/CD)

17. Real-world Projects and Case Studies

18. Machine Learning Resources
- Online Courses
- Books
- Blogs and Journals

📚 Learning Resources for Machine Learning:
- [Python for Machine Learning](https://news.1rj.ru/str/udacityfreecourse/167)
- [Fast.ai: Practical Deep Learning for Coders](https://course.fast.ai/)
- [Intro to Machine Learning](https://learn.microsoft.com/en-us/training/paths/intro-to-ml-with-python/)

📚 Books:
- Machine Learning Interviews
- Machine Learning for Absolute Beginners

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ENJOY LEARNING! 👍👍

There are two types of Data Scientists in the world:

1. Those that Google every time they write a window function

2. Liars

The LLM Scientist Roadmap

Here are some essential AI terms that every data scientist should know:

* Machine Learning (ML): A subfield of AI that allows computers to learn without being explicitly programmed. ML algorithms learn from data to make predictions or decisions.
* Deep Learning (DL): A type of machine learning that uses artificial neural networks to model complex data. Deep learning models are inspired by the structure and function of the human brain.
* Natural Language Processing (NLP): A subfield of AI that deals with the interaction between computers and human language. NLP tasks include machine translation, sentiment analysis, and speech recognition.
* Computer Vision (CV): A subfield of AI that deals with the extraction of information from images and videos. CV tasks include object detection, image classification, and facial recognition.
* Big Data: Large and complex datasets that are difficult to store, process, and analyze using traditional methods. Big data often includes data from multiple sources and in various formats.
* Artificial Neural Network (ANN): A computational model inspired by the structure and function of the human brain. ANNs consist of interconnected nodes called neurons that can process information and learn from data.
* Algorithm: A set of instructions that a computer can follow to perform a specific task. In AI, algorithms are used to train machine learning models and to make predictions or decisions.
* Bias: A systematic preference for or against a particular outcome. Bias can be present in data, algorithms, and models. It's important to be aware of bias and to take steps to mitigate it.
* Explainability: The ability to understand how a machine learning model makes decisions. Explainable models are more trustworthy and easier to debug.
* Ethics: The branch of philosophy that deals with what is right and wrong. AI ethics is concerned with the development and use of AI in a responsible and ethical manner.

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

ENJOY LEARNING 👍👍

Here are some essential machine learning algorithms that every data scientist should know:

* Linear Regression: This is a supervised learning algorithm that is used for continuous target variables. It finds a linear relationship between a dependent variable (y) and one or more independent variables (X). It's widely used for tasks like predicting house prices or stock prices.
* Logistic Regression: This is another supervised learning algorithm that is used for binary classification problems. It predicts the probability of an event happening based on independent variables. It's commonly used for tasks like spam email detection or credit card fraud detection.
* Decision Tree: This is a supervised learning algorithm that uses a tree-like model to classify data. It breaks down a decision into a series of smaller and simpler decisions. Decision trees are easily interpretable, making them a good choice for understanding how a model makes predictions.
* Support Vector Machine (SVM): This is a supervised learning algorithm that can be used for both classification and regression tasks. It finds a hyperplane that best separates the data points into different categories. SVMs are known for their good performance on high-dimensional data.
* K-Nearest Neighbors (KNN): This is a supervised learning algorithm that classifies data points based on the labels of their nearest neighbors. The number of neighbors (k) is a parameter that can be tuned to improve the performance of the algorithm. KNN is a simple and easy-to-understand algorithm, but it can be computationally expensive for large datasets.
* Random Forest: This is a supervised learning algorithm that is an ensemble of decision trees. Random forests are often more accurate and robust than single decision trees. They are also less prone to overfitting.
* Naive Bayes: This is a supervised learning algorithm that is based on Bayes' theorem. It assumes that the features are independent of each other, which is often not the case in real-world data. However, Naive Bayes can be a good choice for tasks where the features are indeed independent or when the computational cost is a major concern.
* K-Means Clustering: This is an unsupervised learning algorithm that is used to group data points into k clusters. The k clusters are chosen to minimize the within-cluster sum of squares (WCSS). K-means clustering is a simple and efficient algorithm, but it is sensitive to the initialization of the cluster centers.

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

ENJOY LEARNING 👍👍

How to piss off a Data Scientist in just 7 seconds:


☑ Peek at an AB experiment early, and insist that we can ship the feature now.
☑ Discard their analyses because it doesn’t agree with your gut feeling.
☑ Ask for data to support a conclusion that you’ve already made.
☑ Request an AI solution because “leadership wants one”.
☑ Argue that Data Science isn’t the sexiest career.
☑ Insist that they’re not real scientists.

NLP Steps

1. Import Libraries:
NLP modules: Popular choices include NLTK and spaCy. These libraries offer functionalities for various NLP tasks like tokenization, stemming, and lemmatization.

2. Load the Dataset:
This involves loading the text data you want to analyze. This could be from a text file, CSV file, or even an API that provides textual data.

3. Text Preprocessing:
This is a crucial step that cleans and prepares the text data for further processing. Here's a breakdown of the sub-steps you mentioned:

Removing HTML Tags: This removes any HTML code embedded within the text, as it's not relevant for NLP tasks.

Removing Punctuations: Punctuations like commas, periods, etc., don't hold much meaning on their own. Removing them can improve the analysis.

Stemming (Optional): This reduces words to their base form (e.g., "running" becomes "run").

Expanding Contractions: This expands contractions like "don't" to "do not" for better understanding by the NLP system.

4. Tokenization:
This breaks down the text into individual units, typically words. It allows us to analyze the text one element at a time.

5. Stemming (Optional, can be done in Text Preprocessing):
As mentioned earlier, stemming reduces words to their base form.

6. Part-of-Speech (POS) Tagging:
This assigns a grammatical tag (e.g., noun, verb, adjective) to each word in the text. It helps understand the function of each word in the sentence.

7. Lemmatization:
Similar to stemming, lemmatization reduces words to their base form, but it considers the context and aims for a grammatically correct root word (e.g., "running" becomes "run").

8. Label Encoding (if applicable):
If your task involves classifying text data, you might need to convert textual labels (e.g., "positive," "negative") into numerical values for the model to understand.

9. Feature Extraction:

This step involves creating features from the preprocessed text data that can be used by machine learning models.

Bag-of-Words (BOW): Represents text as a histogram of word occurrences.

10. Text to Numerical Vector Conversion:

This converts the textual features into numerical vectors that machine learning models can understand. Here are some common techniques:

BOW (CountVectorizer): Creates a vector representing word frequencies.
TF-IDF Vectorizer: Similar to BOW but considers the importance of words based on their document and corpus frequency.

Word2Vec: This technique represents words as vectors based on their surrounding words, capturing semantic relationships.

GloVe: Another word embedding technique similar to Word2Vec, trained on a large text corpus.

11. Data Splitting:
The preprocessed data is often split into training, validation, and test sets.

12. Model Building:
This involves choosing and training an NLP model suitable for your task. Common NLP models include:

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

ENJOY LEARNING 👍👍