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Best Data Science Roadmap Workflow

Best Data Science Roadmap Workflow

    best data science roadmap

    Last Updated on: 30th January 2024, 06:42 pm

    Data Science is an interdisciplinary field that utilizes scientific methods, processes, algorithms, and systems to extract meaningful insights and knowledge from structured and unstructured data.

    The Data Science roadmap covers foundational topics such as mathematics and programming, progresses through data cleaning, exploratory analysis, and machine learning, and delves into deep learning, model evaluation, and deployment while emphasizing the importance of domain knowledge, soft skills, and continual learning for a comprehensive skill set in the field.


    1. Fundamentals

    Mathematics

    • Linear Algebra
    • Calculus
    • Probability and Statistics

    Programming

    • Python
    • Basics
    • Libraries
    • R

    Databases

    • SQL
    • NoSQL
    • Excel #Excel

    2. Data Cleaning and Preprocessing

    Handling Data

    • Missing data
    • Outlier detection
    • Feature scaling and normalization
    • Encoding categorical data #DataPreprocessing

    3. Exploratory Data Analysis

    Data Visualization

    • Matplotlib
    • Seaborn
    • Plotly

    Descriptive Analysis

    • Descriptive statistics
    • Correlation analysis

    Dimensionality Reduction

    • Techniques #ExploratoryDataAnalysis

    4. Machine Learning

    Supervised Learning

    • Regression
    • Linear Regression
    • Logistic Regression
    • Classification
    • Decision Trees
    • Random Forest
    • Support Vector Machines
    • Ensemble methods #SupervisedLearning

    Unsupervised Learning

    • Clustering
    • K-Means
    • Hierarchical Clustering
    • Association Rule Learning
    • Dimensionality Reduction #UnsupervisedLearning

    Reinforcement Learning #ReinforcementLearning


    5. Deep Learning

    Neural Networks

    • Artificial Neural Networks
    • Convolutional Neural Networks
    • Recurrent Neural Networks
    • Generative Adversarial Networks #DeepLearning

    6. Model Evaluation and Tuning

    Optimizing Models

    • Cross-validation
    • Grid Search
    • XGBoost
    • Handling imbalanced datasets #ModelTuning

    7. Deploying Models

    Model Deployment

    • Pickle in Python
    • ONNX
    • TensorFlow Serving
    • AWS SageMaker #ModelDeployment

    8. Domain Knowledge

    Industry Expertise

    • Understanding business problems and requirements
    • Translating business problems into data problems #DomainKnowledge

    9. Soft Skills

    Professional Skills

    • Communication skills
    • Presentation skills
    • Teamwork #SoftSkills

    10. Staying Updated

    Continual Learning

    • Reading research papers
    • Participating in competitions
    • Attending seminars and webinars #ContinualLearning

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