Data Analytics & Programming Portfolio

Driving Insights
with Python & Advanced Excel

This portfolio showcases my robust capabilities in data analytics and programming, honed through intensive training in Dr. Angela Yu's "100 Days of Code: The Complete Python Pro Bootcamp" and comprehensive Excel data analysis courses. Notably, the foundational Python skills acquired have been directly applied in the development of *all* my interactive online portfolios, showcasing a real-world, tangible application of my learning. I leverage these skills to extract actionable insights, automate processes, and build dynamic applications.

Program Overview: 100 Days of Code (Python)

I have completed a significant portion of Dr. Angela Yu's highly-rated "100 Days of Code: The Complete Python Pro Bootcamp" on Udemy. This immersive course emphasizes hands-on project-based learning, transforming beginners into professional Python developers through daily coding challenges. The curriculum covers a vast array of topics, from foundational programming concepts to advanced web development, data science, and automation.

View Course on Udemy

Excel Data Analytics Expertise

My proficiency in Excel extends beyond basic spreadsheet management to advanced data analysis, visualization, and business intelligence tools. I leverage Excel to perform complex calculations, create insightful reports, and build interactive dashboards.

Core Excel Skills & Advanced Capabilities

Spreadsheets & Fundamentals

Mastery of Excel's core interface and foundational elements for efficient data organization.

  • Excel Setup & Installation
  • Understanding Worksheets and Workbooks
  • Navigating and Utilizing the Ribbon Interface
Formulas & Functions: Dynamic Data Manipulation

Proficiency in a wide array of Excel formulas and functions for complex data calculations and logical operations.

  • Formulas Introduction & Application
  • Function Introduction & Usage
  • Logical Functions (e.g., IF, AND, OR)
  • Math Functions (e.g., SUM, AVERAGE, ROUND)
  • Statistical Functions (e.g., COUNT, MAX, MIN, STDEV)
  • Array Formulas (e.g., CSE formulas for multi-cell calculations)
  • Lookup Functions (e.g., VLOOKUP, HLOOKUP, XLOOKUP)
  • Text Functions (e.g., CONCATENATE, LEFT, RIGHT, MID)
  • Date and Time Functions (e.g., TODAY, NOW, DATEDIF)

View Documentation: Google Drive Folder

Charts & Visualization: Communicating Data Effectively

Skill in creating various chart types to visually represent data, identify trends, and communicate insights clearly.

  • Charts Introduction & Basic Chart Types
  • Advanced Charting Techniques & Customization
  • Statistical Charts for Data Distribution & Relationships
  • Sparklines for Compact Data Visualization

View Documentation: Google Drive Folder

Advanced Spreadsheets & Collaboration

Advanced techniques for data organization, formatting, and collaborative work within Excel environments.

  • Working with Tables for Structured Data
  • Advanced Formatting Techniques for Readability & Impact
  • Collaboration Features for Shared Workbooks

View Documentation: Google Drive Folder

PivotTables & PivotCharts: Summarizing & Analyzing Large Datasets

Expertise in using PivotTables and PivotCharts for dynamic data summarization, analysis, and interactive reporting.

  • PivotTable Introduction & Basic Usage
  • Advanced PivotTable Techniques (e.g., calculated fields, grouping)
  • Creating & Customizing PivotCharts for Visual Summaries

View Documentation: Google Drive Folder

Advanced Data Analysis Tools (Add-ins)

Utilizing Excel's powerful add-ins for in-depth statistical analysis and scenario modeling.

  • Analysis Add-ins Overview & Usage
  • Data Tables for What-If Analysis
  • Utilizing the Analysis ToolPak for Statistical Functions

View Documentation: Google Drive Folder

Power Query: Data Transformation & Integration

Mastery of Power Query for connecting to diverse data sources, cleaning, and transforming data for analysis.

  • Power Query Introduction & Data Import
  • Power Query Editor for Data Cleaning & Shaping
  • Advanced Transformations (e.g., unpivoting, custom columns)
  • Understanding Append vs. Merge Operations
  • Introduction to M Language for Custom Queries

View Documentation: Google Drive Folder

Power Pivot / DAX: Advanced Data Modeling & Calculations

Proficiency in Power Pivot for data modeling and DAX for creating powerful measures and calculated columns.

  • Power Pivot Introduction & Data Model Creation
  • Working with the Power Pivot Window
  • DAX (Data Analysis Expressions) Introduction
  • Advanced DAX Functions & Measures

View Documentation: Google Drive Folder

Python Programming Mastery

My journey through "100 Days of Code" has equipped me with a strong foundation in Python, enabling me to build diverse applications, automate tasks, and work with data effectively.

Core Python Concepts & Development Areas

Python Fundamentals & Control Flow

Solid understanding of Python's core syntax, data types, and control structures for building robust logic.

  • Variables & Data Types (Integers, Floats, Strings, Booleans)
  • Mathematical Operations & Type Conversion
  • Control Flow (If/Else, Elif statements)
  • Logical Operators (AND, OR, NOT)
  • Loops (For loops with lists, ranges; While loops)
  • Functions (Defining, Calling, Arguments, Return Values, Multiple Return Values)
  • Scope (Local vs. Global) & Constants
Data Structures & Comprehensions

Proficiency in organizing and manipulating data using Python's built-in structures and efficient comprehension techniques.

  • Lists (Creation, Manipulation, Nested Lists, Slicing)
  • Dictionaries (Deep Dive, Nesting Lists and Dictionaries)
  • Tuples
  • List Comprehension (Squaring Numbers, Filtering Even Numbers, Data Overlap)
  • Dictionary Comprehension
Object-Oriented Programming (OOP)

Foundational understanding and practical application of OOP principles for building structured and reusable code.

  • Classes & Objects (Creating Classes, Attributes, Methods)
  • Constructors (`__init__()` function)
  • Inheritance
Error Handling & Debugging

Proficiency in identifying, handling, and debugging errors to ensure robust and reliable code.

  • Catching Exceptions (`try-except-finally` pattern)
  • Raising Custom Exceptions
  • Handling Specific Errors (IndexError, KeyError)
  • Debugging Techniques (using `print()` statements, debuggers)
File Handling & JSON Data

Skills in reading from, writing to, and updating files, including working with JSON data for structured storage.

  • Opening, Reading, and Writing to Files (`with` keyword)
  • Understanding Relative and Absolute File Paths
  • Working with JSON Data (Write, Read, Update)
APIs & Web Requests: Connecting to External Services

Proficiency in interacting with Application Programming Interfaces (APIs) to fetch and send data, enabling integration with various web services.

  • Understanding APIs, Endpoints, and API Calls
  • Working with HTTP Codes, Exceptions, and JSON Data from API Responses
  • API Parameters & Authentication (API Keys, HTTP Headers)
  • HTTP Methods (GET, POST, PUT, DELETE)
  • Utilizing Environment Variables for Secure API Key Management
GUI Development with Tkinter

Skills in building graphical user interfaces (GUIs) for desktop applications using Python's Tkinter library.

  • Introduction to Tkinter & Creating Windows/Labels
  • Working with Widgets (Buttons, Entry, Radiobuttons, Scales, Checkbuttons)
  • Layout Managers (`pack()`, `place()`, `grid()`)
  • Canvas Widget & Image Integration
  • Dynamic Typing & Timer Mechanisms
Web Foundations: HTML & CSS

Fundamental knowledge of front-end web development, essential for understanding web-based data interactions and applications.

  • Introduction to HTML (Elements, Boilerplate, Lists, Anchors, Images)
  • Introduction to CSS (Adding CSS, Selectors, Colors, Font Properties, Box Model)
  • Understanding How the Internet & Websites Work

Key Projects & Applications

Practical application of my skills through diverse projects, demonstrating problem-solving, data manipulation, and application development.

Skills & Technologies

A comprehensive overview of the programming languages, tools, libraries, and analytical concepts mastered during my data analytics and programming journey.

Core Competencies & Technical Stack

Programming Languages
  • Python (Proficient)
  • SQL (Foundational)
  • M Language (Power Query)
  • DAX (Power Pivot)
  • HTML5
  • CSS3
Python Libraries & Frameworks
  • Data Manipulation: Pandas, NumPy
  • GUI Development: Tkinter, Turtle
  • Web Requests/APIs: Requests
  • Automation/Scripting: Smtplib, Datetime
  • Web Scraping (Intro): BeautifulSoup (Course mention, but beyond Day 44)
  • Machine Learning (Intro): Scikit-learn (Course mention, but beyond Day 44)
  • Web Frameworks (Intro): Flask (Course mention, but beyond Day 44)
Data Analytics Tools & Concepts
  • Microsoft Excel (Advanced)
  • Formulas & Functions (Logical, Math, Statistical, Array, Lookup, Text, Date/Time)
  • Charts & Data Visualization
  • PivotTables & PivotCharts
  • Analysis Add-ins & ToolPak
  • Power Query (Data Transformation)
  • Power Pivot (Data Modeling)
  • Demand Forecasting (Conceptual)
  • Data Cleaning & Preprocessing (Excel, Python)
  • Exploratory Data Analysis (Excel, Python)
Development Environments & Version Control
  • PyCharm, Jupyter Notebooks, Google Colab
  • Git, GitHub (Basic for project sharing)
Core Programming Concepts
  • Object-Oriented Programming (OOP)
  • Error Handling & Exception Management
  • Debugging Techniques
  • File I/O
  • API Integration
  • Modular Programming

Growth & Learning Approach

My approach to mastering data analytics and programming is rooted in continuous learning, practical application, and a problem-solving mindset.

Key Learning & Professional Development Traits

  • Problem Structuring: Ability to break down complex data problems into manageable components and design effective analytical solutions.
  • Logical Reasoning: Strong capacity for analytical and logical thinking, crucial for debugging code and interpreting data insights.
  • Debugging Proficiency: Developed systematic approaches to identify and resolve issues in code and data processes.
  • Attention to Detail: Meticulous approach to data cleaning, coding, and analysis to ensure accuracy and reliability.
  • Self-Learning & Adaptability: Proactive engagement with new tools and concepts in a rapidly evolving technological landscape.
  • Project Management (Self-Directed): Ability to manage multi-day coding projects, breaking them into daily tasks and ensuring completion.
  • Data Storytelling (Emerging): Developing the skill to translate complex data findings into clear, compelling narratives and visualizations.
  • Persistence & Resilience: Commitment to overcoming coding challenges and complex analytical problems through iterative refinement.