Data Scientist Roadmap
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|-- 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

دعوت به همکاری پاره وقت و تمام وقت:
دانشجویان یا فارغ التحصیلان آشنا به توسعه فرانت با React JS رزومه خود را به @shahram_jamali ارسال نمایند.

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