Full Stack in Data Science

Foundational understanding of data science concepts, applications, and the data science process.

• Overview and applications of Data Science
• The role of a Data Scientist
• Understanding the Data Science process

Essential mathematical and statistical foundations required for data science applications.

• Basic algebra, calculus, and linear algebra
• Probability and statistics fundamentals
• Distributions, hypothesis testing, and statistical inference

Learn various techniques for collecting, cleaning, and preparing data for analysis.

• Data types and data structures
• Techniques for data collection (web scraping, APIs, databases)
• Data cleaning and preprocessing (handling missing data, outliers, etc.)
• Data transformation and feature engineering

Master techniques for understanding data Full Stack through statistical analysis and visualization.

• Descriptive statistics and summary statistics
• Data visualization for EDA (histograms, scatter plots, box plots)
• Identifying trends, patterns, and anomalies

Create compelling and effective visualizations to communicate insights from data.

• Principles of effective data  visualization
• Visualization tools and libraries (Matplotlib, Seaborn, Plotly)
• Creating dashboards and reports

Learn Python programming fundamentals specifically tailored for data science applications.

• Python basics: syntax, data types, and control structures
• Functions, loops, and conditional statements

Master the core Python libraries that form the foundation of Full Stack  data science work.

• NumPy for numerical computations
• Pandas for data manipulation and analysis
• SciPy for scientific computing

Introduction to machine learning concepts and the machine learning workflow.

• Introduction to machine learning concepts
• Types of machine learning (supervised, unsupervised, reinforcement)
• Data splitting (training, validation, and test sets)

Learn supervised learning algorithms for prediction and classification tasks.

• Regression algorithms (Linear Regression, Logistic Regression)
• Classification algorithms (K-Nearest Neighbors, Decision Trees, SVM)
• Ensemble methods (Random Forest, Gradient Boosting)

Explore unsupervised learning methods for pattern discovery and data exploration.

• Clustering algorithms (K-Means, Hierarchical Clustering)

Introduction to neural networks and deep learning frameworks for complex pattern recognition.

• Introduction to neural networks and deep learning
• Basics of frameworks (TensorFlow, Keras, PyTorch)

Learn comprehensive techniques for evaluating and validating machine learning models.

• Performance metrics (accuracy, precision, recall, F1 score)
• Cross-validation techniques
• Model selection and hyperparameter tuning

Master database concepts and SQL for efficient data storage and retrieval.

• Introduction to Databases
• Database Models
• Relational Databases
• Structured Query Language (SQL) Basics
• Advanced SQL Queries

Learn project management skills specific to data science projects and team collaboration.

• Defining problem statements and project objectives
• Working in Agile teams and project lifecycle
• Documentation and communication of results

Understanding ethical considerations and responsible practices in data science.

• Data privacy and security concerns
• Bias and fairness in data and models
• Ethical guidelines in data collection and decision making

Apply all learned concepts in a comprehensive real-world data science project.

• Selecting a real-world problem
• Applying data science techniques using Python
• Presenting findings and solutions in a final report or presentation

Comprehensive career preparation and industry recognition upon successful course completion.

Job Roles:

• Data Scientist
• Machine Learning Engineer
• Data Analyst
• Research Scientist
• AI/ML Consultant
• Business Intelligence Analyst
• Quantitative Analyst
• Data Engineer
• Statistical Analyst
• Product Data Scientist

Industries:

• Technology and Software
• Finance and Banking
• Healthcare and Pharmaceuticals
• E-commerce and Retail
• Telecommunications
• Automotive and Manufacturing
• Media and Entertainment
• Government and Public Sector
• Consulting and Professional Services
• Research and Development

Career Support:

• Resume Review and Portfolio Development
• Technical Interview Preparation
• Mock Interviews with Industry Experts
• LinkedIn Profile Optimization
• Job Placement Assistance and Networking
• Continuous Learning Resources and Updates
• Alumni Network and Mentorship Programs
• Industry Certification Guidance