Entry-level data scientist with practical experience from a coding bootcamp, where I built projects involving data cleaning, visualization, and machine learning. I've been programming since 2022, focusing on tools like Python, Pandas, scikit-learn, and SQL.

These projects are my official HyperionDev data science submissions, reviewed by mentors and shared with partner companies. Beyond assignments, this repository demonstrates what I consistently deliver independently and professionally.

Every file here is written, tested, and submitted by me with the same level of attention I bring to real-world software projects.

Click each section below to expand

Programming

 Languages:
  • Python
  • SQL

Libraries

 Data Analysis:
  • Pandas
  • Scipy
  • NumPy

 Data Visualisation:
  • Matplotlib
  • Seaborn
  • Jupyter Notebook
  • Tableau

 Machine Learning & AI:
  • Scikit-learn

 NLP:
  • Spacy

Databases

 Relational:
  • MySQL
  • SQLite

 Data Sources:
  • JSON
  • XML

Core Skills

  • Data Visualisation
  • Data Analysis
  • Data Cleaning
  • Preprocessing
  • Supervised Machine Learning
  • Neural networks
  • NLP
  • Unsupervised Machine Learning

Task	Level	Score
Capstone Project - Variables and Control Structures	Level 1	100%
Capstone Project - Files	Level 1	94%
Capstone Project - Lists, Functions, and String Handling	Level 1	88%
Capstone Project - OOP	Level 1	100%
Capstone Project - NLP	Level 1	100%
Capstone Project - Databases	Level 2	100%
Capstone Project - Data Analysis	Level 2	100%
Capstone Project - Data Visualisation	Level 2	94%
Capstone Project - Image Processing	Level 3	100%
Capstone Project - Unsupervised Machine Learning	Level 3	100%

• Capstone Project - Variables and Control Structures

  Your code is well-structured, with clear comments and descriptive variable names, such as deposit_amount, which improves readability and documentation—well done!

• Capstone Project - Files

  You’ve done well with this code and demonstrated your understanding of the Python fundamentals covered so far in this boot camp. You’ve applied concepts like loops, and conditional statements (if, elif, and else), and worked with external data sources like text files, lists, and strings.

• Capstone Project - Lists, Functions, and String Handling

  You have done exceptionally well on this task, showcasing a solid understanding of key concepts covered so far in the boot camp. These include functions, lists, strings, loops, working with external data sources, and conditional statements.

• Capstone Project - OOP

  Well done, Christiaan, on reaching the final Capstone Project for this Level 🙌 The logic for different menu functionalities is neatly stored inside separate functions. I can confirm that all menu options execute successfully on my machine.

• Capstone Project - NLP

  Christiaan, your submission is well-researched and demonstrates a strong understanding of Natural Language Processing (NLP) and its applications. Your ability to match specific NLP models to different real-world scenarios showcases your deep comprehension of how NLP techniques can be effectively applied.

• Capstone Project - Databases

  Fantastic work, Christiaan! You've built a clean, modular, and practical command-line SQL interface that demonstrates solid understanding of databases, query execution, and user interaction. The way you’ve structured your commands and output makes it intuitiveforusers to retrieve relevant student, course, and review data.

• Capstone Project - Data Analysis

  Thank you for your submission, Christiaan. It is good to note that you explored specific parameters in order to load the dataset into a pandas DataFrame. Well done for calling the lower(), strip() and replace() methods in order to sanitise the data stored within the 'country' column. Your use of regex and compound expressions is very good.

• Capstone Project - Data Visualisation

  Fantastic work on your resubmission, Christiaan! The depth of your data exploration is evident in the insightful information you have uncovered. The EDA reports you have produced are well structured and clearly present your findings, and the methods and processes you have followed. Well done for ensuring that the datasets were free from duplicate observations, free from inconsistent values and for handling missing values. You have narrowed down your analysis by removing all features that are not required within your analyses.

• Capstone Project - Image Processing

  Thank you for your submission, Christiaan. You’ve done a great job showing how image classification works using the scikit-learn library. You’ve clearly explained each step, from loading the dataset and looking at its structure to training a Random Forest Classifier and checking how well it performs.

• Capstone Project - Unsupervised Machine Learning

  You have done a great job working through the PCA and clustering task, demonstrating both technical competence and analytical thinking. Your data exploration was thorough, you loaded the dataset correctly, inspected the first few entries, and checked for missing values and summary statistics. This shows a good understanding of the importance of initial data inspection. The use of histograms to visualise the distribution of variables was helpful and presented. Your correlation analysis using a heatmap was insightful, with accurate interpretations of the relationships between variables such as the positive correlation between assault and murder rates.

Thank you for visiting. I look forward to connecting and creating meaningful value together.

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