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Data Scientist

A structured learning path for data scientists covering statistics, Python, data manipulation, visualization, machine learning, and deep learning for data-driven decision making.

roleintermediate12 nodes160h total
  1. 1

    Python for Data Science

    Master Python programming with focus on data science libraries

    Completion criteria: Build data processing scripts using Python core and libraries

    Next step: Study statistics and probability fundamentals

    ⏱ 12h

  2. 2

    Statistics and Probability

    Learn descriptive and inferential statistics, hypothesis testing

    Prerequisites: ds-01

    Completion criteria: Conduct statistical analysis and hypothesis tests on a dataset

    Next step: Learn data manipulation with Pandas

    ⏱ 14h

  3. 3

    Data Manipulation with Pandas and NumPy

    Master data cleaning, transformation, and analysis with Pandas

    Prerequisites: ds-02

    Completion criteria: Clean and transform a messy real-world dataset with Pandas

    Next step: Study data visualization techniques

    ⏱ 14h

  4. 4

    Data Visualization

    Create compelling visualizations with Matplotlib, Seaborn, and Plotly

    Prerequisites: ds-03

    Completion criteria: Build an interactive dashboard with multiple chart types

    Next step: Learn exploratory data analysis

    ⏱ 10h

  5. 5

    Exploratory Data Analysis

    Conduct systematic EDA to uncover patterns and insights

    Prerequisites: ds-04

    Completion criteria: Complete a full EDA report on a complex dataset

    Next step: Study machine learning fundamentals

    ⏱ 10h

  6. 6

    Machine Learning Fundamentals

    Learn supervised and unsupervised learning with scikit-learn

    Prerequisites: ds-05

    Completion criteria: Build and evaluate classification and regression models

    Next step: Explore feature engineering and model selection

    ⏱ 16h

  7. 7

    Feature Engineering and Model Selection

    Master feature engineering, cross-validation, and hyperparameter tuning

    Prerequisites: ds-06

    Completion criteria: Improve model performance through systematic feature engineering

    Next step: Study deep learning basics

    ⏱ 12h

  8. 8

    Deep Learning with TensorFlow or PyTorch

    Build neural networks for classification and regression tasks

    Prerequisites: ds-07

    Completion criteria: Train a neural network achieving good accuracy on a dataset

    Next step: Learn natural language processing

    ⏱ 16h

  9. 9

    Natural Language Processing

    Process text data with NLP libraries and transformer models

    Prerequisites: ds-08

    Completion criteria: Build a text classification or sentiment analysis model

    Next step: Study big data tools

    ⏱ 14h

  10. 10

    Big Data Tools and Spark

    Process large datasets with Apache Spark and distributed computing

    Prerequisites: ds-09

    Completion criteria: Process a large dataset using Spark and PySpark

    Next step: Learn model deployment

    ⏱ 12h

  11. 11

    Model Deployment and MLOps

    Deploy ML models as APIs and manage model lifecycle

    Prerequisites: ds-10

    Completion criteria: Deploy a trained model as a REST API with monitoring

    Next step: Build a capstone data science project

    ⏱ 10h

  12. 12

    Capstone Data Science Project

    Complete an end-to-end data science project from data to deployment

    Prerequisites: ds-11

    Completion criteria: Deliver a deployed ML model with full documentation and report

    Next step: Specialize in NLP, CV, or MLOps and build a portfolio

    ⏱ 20h