Full stack Data scientist Road map

A full-stack data science road map typically includes a combination of skills in statistics, programming, machine learning, data visualization, and deployment. Here's a comprehensive road map that can help you become a full-stack data scientist:

1. Mathematics and Statistics:

• Linear algebra

• Calculus

• Probability theory

• Statistical inference

2. Programming and Software Engineering

• Python programming language

• Object-oriented programming (OOP)

• Version control systems (e.g., Git)

• Software development practices

3. Data Manipulation and Analysis

• NumPy for numerical computations

• Pandas for data manipulation and analysis

• SQL for database querying

• Data cleaning and preprocessing techniques

4. Data Visualization

• Matplotlib for basic visualizations

• Seaborn for statistical visualizations

• Plotly for interactive and dynamic visualizations

• Tableau or Power BI for advanced visualizations

5. Machine Learning

• Supervised learning algorithms (e.g., linear regression, decision trees, random forests, support vector machines)

• Unsupervised learning algorithms (e.g., clustering, dimensionality reduction, anomaly detection)

• Deep learning frameworks (e.g., TensorFlow, PyTorch)

• Model evaluation and validation techniques

6. Big Data Technologies

• Apache Hadoop ecosystem (e.g., HDFS, MapReduce, Hive).

• Apache Spark for distributed computing.

• Handling large datasets with tools like Apache Kafka or Apache Beam

7. Deployment and Productionization

• Containerization with Docker

• Building RESTful APIs with Flask or Django or fastAPI

• Cloud platforms like AWS, Azure, or Google Cloud Platform (GCP)

• DevOps practices for continuous integration and deployment

8. Advanced Topics

• Natural Language Processing (NLP)

• Time series analysis

• Reinforcement learning

• Graph analytics

9. Domain Knowledge

• Gain expertise in a specific domain such as finance, healthcare, or e-commerce. This involves understanding the relevant data and domain-specific challenges.

10. Continuous Learning and Collaboration

• Stay updated with the latest research papers, blogs, and conferences

• Participate in Kaggle competitions and open-source projects

• Collaborate with other data scientists and researchers to expand your knowledge

Remember, this road map is a general guide, and you can tailor it according to your specific interests and career goals. It's essential to practice hands-on projects and work on real-world datasets to gain practical experience and reinforce your learning.

Also if you have a strong background in mathematics and statistics, you can leverage those skills in the field of data science.