Software Engineering Foundations with Python, Data and SQL

Programming versus software engineering, algorithms, machine language, programming languages and translators.

• Brief History of Python
• Supported Python 3 versions and library compatibility
• Installing Python
• IDEs
• Environment Variables
• Python Documentation
• Hello world with python
• Modes of Programming
• What Is Colab Notebook
• Why Colab Notebook Is Important
• Accessing hosted Colab notebooks or a prepared local Jupyter environment
• Main Components of Colab Notebook
• Modes
• Exporting Notebook Documents

• Identifiers
• Reserved Words
• Lines and Indentation
• Comments
• Numbers
• Python Lists
• Python Tuples
• Python Dictionaries
• Python Sets
• Copying
• Python Strings
• String formatting
• Regular Expressions
• Arithmetic Operators
• Comparison (Relational) Operators
• Assignment Operators
• Logical Operators
• Membership Operators
• Operators Precedence
• Decision Making
• The if Statement
• The if else Statement
• For Loop
• While Loop
• Break And Continue

• Defining Your Own Functions
• Parameters
• Function Documentation
• Passing Collections to a Function
• Variable Number of Arguments
• Scope
• Map
• Filter
• Lambda
• What Are Modules
• Importing Modules
• Aliasing
• Importing Set Of Element From A Module
• Namespace
• What Are Packages
• dir function
• help function
• What Is Exception
• Exception Types
• Try Except Component
• Handle exceptions deliberately; avoid swallowing broad failures
• Handling Specific Exception
• Raise
• Access Modes
• Writing Data to a File
• Reading Data From a File
• Read Functions
• PDF reading
• XML files
• What is OOP
• Why OOP Is Important
• Classes
• Attributes
• Methods
• Constructor
• Inheritance
• Polymorphism
• Class-level attributes and static methods in Python

• The SDLC Overview.
• Pre-development phases of SDLC.
• Specific phases of SDLC.
• Post-development phases of the SDLC.

System modelling

• System Modeling Overview.
• Software Architecture.
• Use Cases.
• Data Structure and Flow.
• Interprocess Communication.
• System Scope and Scale.

Methodologies and practice

• Methodologies, Paradigms, and Practices Overview.
• Process Methodologies.
• Development Paradigms.
• Development Practices.

Consider API contracts within inter-process communication and data-acquisition examples; this is not a separate API-framework development track.

• Use external libraries, file encodings, PDF/XML extraction and permitted sample web data.
• Statistical summaries and Seaborn visualisation; covariance, correlation, conditional probability and Bayes concepts.
• Clean sample web-log data, normalise numerical features, detect outliers and impute missing values.
• Feature engineering and class imbalance, including oversampling within training data only.

• Supervised and unsupervised learning, train/test separation and polynomial regression.
• Naive Bayes spam-classifier example, K-means clustering, entropy and decision trees.
• Bias/variance trade-offs and K-fold cross-validation; evaluation estimates generalisation but does not guarantee absence of overfitting.
• Use consistent preprocessing fitted on training data, and keep evaluation data separate.
• Selected XGBoost demonstration alongside scikit-learn examples; interpret limitations of predictions.

• Linux basics, command-line use, SSH and authorised remote access.
• TCP/IP, HTTP and HTTPS; inspect prepared or authorised traffic with Wireshark.
• Symmetric encryption, hashing and HMAC; MD5 as a legacy algorithm, not a recommended security primitive.
• Public-key encryption, signatures and RSA concepts.

• Inspect sample website certificates: subject, issuer, validity, signatures and domain scope.
• OpenSSL examples, RSA key generation, root trust, certificate chains and chain verification.
• Guided certificate setup in a prepared web-server lab; TLS versions and handshake inspection.
• Distinguish symmetric record encryption, authentication and key agreement. TLS 1.3 does not use static RSA key transport.
• Conceptual Diffie-Hellman and modular arithmetic; elliptic-curve point addition, doubling, discrete-log ideas and ECDHE/ECDSA comparisons. These are demonstrations, not implementation of custom cryptography.

• RDBMS and SQL Server; SQL versus T-SQL.
• Prepared supported SQL Server container or remote instance; containers versus virtual machines and production-configuration considerations.
• Connect with SSMS, sqlcmd or Visual Studio Code with the MSSQL extension.
• Databases, tables, stored procedures, functions and access permissions.
• Import and export sample data and discuss database-security responsibilities.

• Select
• Where
• Like
• Order
• Insert
• Update
• Delete
• IN Operator
• Between
• Aggregate
• Group
• Alter
• Sub queries
• Stored Procedures
• Functions

Review the small project across successive iterations and assess programming, data handling and system-design decisions.