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Introduction to Software Engineering
Introduction to Software Engineering
Introduction to Software Engineering
This course offers an opportunity to explore the field of software engineering without requiring prior programming knowledge or qualifications. It covers essential topics such as the SDLC, Agile methodologies, programming principles, and deployment.
- Basic programming concepts
- Careers in software engineering
- Programming languages and frameworks
- The Software Development Lifecycle (SDLC)
- Software Architecture
Introduction to DevOps
Introduction to Software Engineering
Introduction to Software Engineering
This course is an introduction to DevOps, a highly sought-after skill set in the workforce. The course covers Basic software engineering skills, Continuous Integration and Continuous Delivery tools and infrastructure. It is suitable for beginners and those looking to enhance their knowledge of DevOps.
- Test-Driven Development
- CI/CD tools
- Behavior-Driven Development
- Github
Introduction to Cloud Computing
Introduction to Software Engineering
Introduction to Cloud Computing
Cloud computing has experienced a surge in popularity and importance in recent years, becoming a prominent force in both public and private sectors. Its ability to deliver speed and flexibility has greatly contributed to organizational innovation and productivity. Consequently, the demand for jobs in this field has skyrocketed, making them highly desirable. Cloud computing services are utilized by organizations of all sizes and industries for various purposes.
- Native cloud concepts
- IaaS, PaaS and SaaS concepts
- Preparation to AWS and Azure fundamentals certification
Math and Stat for Machine Learning and Data Science
This course focuses on the fundamental mathematical and statistical concepts essential for machine learning and data science. Through a rigorous curriculum, students will gain a solid understanding of the mathematical foundations that underpin these fields. The course covers topics such as linear algebra, calculus, probability theory, statistical methods, optimization, and multivariate calculus. Additionally, students will explore matrix decomposition, time series analysis, statistical learning theory, and statistical modeling. By the end of the course, students will have the necessary mathematical and statistical tools to analyze data, develop models, and make data-driven decisions.
Syllabus
Linear Algebra
Ø Vectors, matrices, and matrix operations
Ø Linear transformations and eigenvalues/eigenvectors
Calculus
Ø Differentiation, integration, and optimization techniques
Ø Gradients, partial derivatives, and multivariate calculus
Probability Theory
Ø Probability distributions and random variables
Ø Statistical inference and hypothesis testing
Statistical Methods
Ø Regression analysis and model evaluation
Ø Hypothesis testing, confidence intervals, and statistical significance
Optimization
Ø Gradient descent, stochastic gradient descent, and convex optimization
Ø Optimization algorithms for machine learning models
Matrix Decomposition:
Ø Singular Value Decomposition (SVD) and Principal Component Analysis (PCA)
Ø Factor analysis and dimensionality reduction techniques
Time Series Analysis:
Ø Autoregressive models, moving averages, and forecasting techniques
Ø Analysis of temporal data and trend identification
Statistical Learning Theory:
Ø Bias-variance tradeoff and overfitting
Ø Model evaluation metrics and generalization in machine learning
Statistical Modeling:
Ø Maximum likelihood estimation and Bayesian inference
Ø Model selection and regularization techniques
Through a combination of theoretical explanations, practical examples, and hands-on exercises, students will develop a strong mathematical foundation to understand, implement, and evaluate machine learning and data science algorithms.