21000.00 ₹ · Full Stack Data Science & AI Roadmap: A Step-by-Step Guide

Published date: July 26, 2024 3:17 pm

Modified date: July 26, 2024 3:43 pm

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Embarking on a journey to become a full-stack data scientist involves mastering a diverse set of skills across various domains. Here’s a comprehensive roadmap to guide you through the process:

1. Foundational Skills
1.1. Programming Languages

Python: Start with Python, the most popular language in data science, focusing on libraries like NumPy, Pandas, Matplotlib, and Seaborn.
R: Learn R for statistical analysis and visualization.
SQL: Master SQL for data manipulation and querying databases.
1.2. Mathematics and Statistics

Linear Algebra: Understand vectors, matrices, and their operations.
Calculus: Learn derivatives and integrals, especially for optimization problems.
Probability and Statistics: Grasp probability theory, distributions, hypothesis testing, and statistical inference.
2. Data Collection and Cleaning
2.1. Data Collection

APIs and Web Scraping: Learn how to collect data from web sources using APIs and web scraping tools like BeautifulSoup and Scrapy.
2.2. Data Cleaning

Data Preprocessing: Master techniques for handling missing values, outliers, and data normalization.
Data Transformation: Learn to transform raw data into a usable format for analysis.
3. Exploratory Data Analysis (EDA)
3.1. Visualization

Matplotlib and Seaborn: Create insightful visualizations to understand data distributions and relationships.
Plotly and Bokeh: Develop interactive visualizations for better data exploration.
3.2. Statistical Analysis

Descriptive Statistics: Summarize and describe data features.
Inferential Statistics: Make predictions and inferences about a population based on sample data.
4. Machine Learning
4.1. Supervised Learning

Regression: Understand linear and logistic regression.
Classification: Learn algorithms like K-Nearest Neighbors (KNN), Decision Trees, and Support Vector Machines (SVM).
4.2. Unsupervised Learning

Clustering: Explore clustering techniques like K-Means and Hierarchical Clustering.
Dimensionality Reduction: Learn PCA and t-SNE for reducing data dimensions.
4.3. Advanced Machine Learning

Ensemble Methods: Master techniques like Random Forest, Gradient Boosting, and XGBoost.
Model Evaluation: Learn to evaluate models using metrics like accuracy, precision, recall, F1 score, and ROC-AUC.
5. Deep Learning
5.1. Neural Networks

Fundamentals: Understand the basics of neural networks and backpropagation.
Libraries: Learn to use TensorFlow and Keras for building neural networks.
5.2. Advanced Topics

Convolutional Neural Networks (CNNs): For image classification and processing.
Recurrent Neural Networks (RNNs): For time series analysis and natural language processing.
6. Big Data Technologies
6.1. Data Engineering

Hadoop: Learn the basics of Hadoop for distributed storage and processing.
Spark: Master Apache Spark for large-scale data processing.
6.2. Data Storage

NoSQL Databases: Understand MongoDB, Cassandra, and other NoSQL databases for handling unstructured data.
7. Data Science Tools and Platforms
7.1. Version Control

Git: Learn version control with Git and GitHub for managing code repositories.
7.2. Integrated Development Environments (IDEs)

Jupyter Notebook: Use Jupyter for interactive coding and visualization.
VS Code/PyCharm: Explore these IDEs for Python development.
8. Soft Skills and Business Acumen
8.1. Communication Skills

Data Storytelling: Learn to present your findings effectively to non-technical stakeholders.
Visualization Tools: Use Tableau or Power BI for creating business reports and dashboards.
8.2. Domain Knowledge

Industry Insights: Gain domain-specific knowledge in industries like finance, healthcare, or e-commerce to better understand data context and applications.
Conclusion
Becoming a full-stack data scientist requires a blend of technical skills, domain knowledge, and soft skills. Follow this roadmap, stay committed to continuous learning, and you'll be well on your way to mastering full-stack data science. For comprehensive courses and hands-on training, explore Naresh i Technologies.

visit:https://nareshit.com/courses/full-stack-data-science-ai-online-training

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