Coding and FMCG Startup Marketing

Configure Git locally and initialize a repository for an existing project folder.

Stage focused changes, write informative commit messages, and inspect repository history.

Create and merge a feature branch without changing the main branch prematurely.

Connect a local repository to GitHub and synchronize changes with push, pull, and clone.

Resolve a simple merge conflict while preserving the intended changes.

Use a README, issues, and a basic task board to communicate a project's purpose and progress.

Define a focused software problem based on an FMCG sales, inventory, or operations need.

Translate the chosen problem into user stories with testable acceptance criteria.

Design a maintainable repository structure that separates data, source code, tests, and documentation.

Create a reproducible Python environment and record the project's dependencies.

Refactor a Python script into small functions with clear inputs, outputs, and responsibilities.

Write automated tests for the project's core Python functions.

Identify entities, attributes, and relationships for a small FMCG sales and inventory system.

Normalize a relational schema to third normal form and justify the resulting table structure.

Implement the schema in PostgreSQL using appropriate keys, constraints, and data types.

Load a clean CSV dataset into PostgreSQL and verify the imported records.

Answer business questions using joins, filters, grouping, and aggregate SQL queries.

Use an index and an execution plan to investigate the performance of a selected query.

Execute parameterized PostgreSQL queries safely from Python.

Formulate one measurable data-science question using sales, inventory, or customer-demand data.

Create or anonymize a dataset that can be shared publicly without exposing customer or business secrets.

Clean missing values, duplicate records, and inconsistent data types with pandas.

Produce summary statistics and visualizations that reveal patterns relevant to the project question.

Create training and test sets while preventing information leakage.

Train a simple baseline prediction or classification model with scikit-learn.

Evaluate the baseline model using a metric appropriate to the project question.

Explain the model's main limitations, possible biases, and unsuitable uses.

Classify a small business system's critical assets using confidentiality, integrity, and availability.

Create a concise threat model that links assets, likely threats, vulnerabilities, and controls.

Identify a string-built SQL query that is vulnerable to injection and replace it with a parameterized query.

Move database credentials out of source code and load them from environment variables.

Configure PostgreSQL roles according to the principle of least privilege.

Validate untrusted input and return errors without exposing sensitive system details.

Define useful security audit events for authentication and database activity.

Define a structured format for synthetic authentication logs containing both normal and suspicious activity.

Generate a small synthetic log dataset with labeled failed-login and brute-force patterns.

Parse authentication logs in Python and reject malformed records safely.

Detect repeated failed-login activity within a configurable time window.

Summarize suspicious events by account, source address, and time period.

Measure the detector's false positives and missed detections against labeled examples.

Write automated tests for log parsing and detection rules.

Document the analyzer's assumptions, ethical boundaries, and known limitations.

Compare cybersecurity and data-science work using a rubric covering interest, aptitude, and desired career tasks.

Write a project README that clearly presents the problem, architecture, setup steps, results, and limitations.

Make each portfolio project reproducible from a fresh clone by following its documented setup instructions.

Create a GitHub profile README and pin repositories that best demonstrate technical growth.

Write evidence-based résumé bullet points describing the technical decisions and results of each project.

Review each repository for technical depth, originality, documentation quality, and responsible data handling.

Calculate semester grade targets needed to improve CGPA using the institution's credit and grading rules.

Create an adaptable twelve-week plan balancing university assessments, portfolio work, and specialization exploration.