Hi there! Welcome to Refugees Code - Data Analytics 👩💻
Welcome to Refugees Code - Data Analytics! From now on we'll try to guide you through the fundamentals of Data Analytics with the main and only purpose that you can land a job in the IT industry. We'll do our best to help you get a job, but it will also require a lot of work from your side. Bear in mind that we are here to help you, but it depends on your work and motivation. Let's coding! 💫
On this repository you will find examples and exercises about Data Analytics using Python, SQL, Excel, Tableau and Power BI.
| Topic | Content |
|---|---|
| link | Python fundamentals I |
| link | Python fundamentals II |
| link | Errors, exception handling, debugging |
| link | modules |
| link | oop |
| link | recursion |
| Topic | Content |
|---|---|
| link | NumPy |
| link | Pandas |
| link | Reading and writing files |
| link | Virtual environments |
| Topic | Content |
|---|---|
| link | Central tendency dispersion |
| link | normal distribution zscores |
| link | probability sampling |
| link | Hypothesis testing |
| link | A/B testing business |
| link | Mini project experiment |
| Topic | Content |
|---|---|
| link | Dealing with Missing Data |
| link | Detecting and Handling Outliers |
| link | Type Conversion & Date/Time Formats |
| link | Text Cleaning & Manipulation |
| link | Feature Engineering Basics |
| link | Mini-Project: HR Data Cleaning & Transformation |
Welcome to an intensive, hands-on journey into the world of data analytics. This comprehensive program is designed to transform you into a skilled data analyst capable of extracting meaningful insights from complex datasets and communicating them effectively to drive business decisions.
RefugeesCode: Data Analytics Course is a program that combines theoretical knowledge with practical, real-world applications. You'll master the essential tools, techniques, and methodologies used by professional data analysts in today's data-driven economy.
By the end of this program, you will be able to:
- 🐍 Analyze and Transform Data using Python and SQL to extract meaningful insights from complex datasets
- 📊 Apply Statistical Methods including descriptive and inferential statistics in real-world business contexts
- 📈 Build Interactive Dashboards using modern Business Intelligence tools to visualize data effectively
- 💬 Communicate Insights by presenting data-driven findings clearly and persuasively to stakeholders
- 🎯 Complete End-to-End Projects from raw data collection through analysis to actionable recommendations
This course is structured into 10 comprehensive modules, each building upon the previous to create a solid foundation in data analytics:
- Intro to the course
- CLI commands. Use of Terminal
- Version control basics (Git & GitHub)
- VS Code
- AI-assisted coding
- Jupyter Notebook introduction
- Syntax, variables, data types
- Lists, dictionaries, sets, tuples
- Conditionals (if, elif, else) + Boolean and Math Operators
- Loops (for, while)
- Functions
- Recursion
- OOP (objects, classes, and maybe interfaces)
- Debugging
- Errors and exception handling
- Virtual environments with Python
- Introduction to pandas and numpy
- Series and DataFrames
- Reading/writing CSV and Excel files
- Filtering, slicing, sorting data
- Aggregation and groupby operations
- Mini-project: Analyze a public dataset (e.g., Airbnb, Titanic) and extract descriptive insights
- Central tendency & dispersion
- Normal distribution & z-scores
- Probability & sampling
- Confidence intervals, hypothesis testing
- A/B testing in business contexts
- Mini-project: Analyze an experiment or test results and report conclusions
- Dealing with missing values and outliers
- Type conversion, date/time formats
- Text cleaning and manipulation
- Feature engineering basics
- Mini-project: Clean and reshape a raw dataset (e.g., HR or financial records)
- Charts and plots (matplotlib, seaborn, plotly)
- Chart selection: matching visuals to data types
- Data storytelling frameworks
- Visualization best practices
- Mini-project: Create a narrative visual report for a business dataset
- SELECT, WHERE, ORDER BY, GROUP BY
- JOINs, subqueries, CASE WHEN, window functions
- Writing queries for real-life datasets (e.g., orders, sales, customers)
- Mini-project: Write a SQL report for business KPIs using a mock database (PostgreSQL or SQLite)
- Dashboard design principles: filters, KPIs, layouts
- Tools: Power BI, Tableau, or Streamlit
- Interactivity and calculated fields
- Publishing and sharing dashboards
- Mini-project: Build and deploy a dashboard for operational metrics
- Univariate & multivariate exploration
- Histograms, boxplots, pairplots, heatmaps
- Time series & correlation analysis
- Funnel and segmentation logic
- Mini-project: Full EDA on a large, complex dataset with insights
- Dataset selection & problem framing
- Full workflow: clean, analyze, visualize, present
- Peer feedback & instructor mentorship
- Final project delivery: notebook, report, dashboard, and oral presentation
Throughout this program, you'll gain hands-on experience with industry-standard tools:
- 💻 Languages: Python, SQL
- 📚 Libraries: pandas, numpy, matplotlib, seaborn, plotly
- 🔧 Development Environment: Jupyter Notebooks, VS Code, GitHub
- 🗄️ Databases: PostgreSQL, SQLite
- 📊 BI Tools: Power BI, Tableau, Streamlit
- Class Schedule: Tuesdays & Thursdays
Each module includes mini-projects that allow you to apply what you've learned to realistic datasets and business problems. You'll receive personalized feedback and mentorship throughout your journey.
This isn't just about learning tools—it's about developing an analytical mindset. You'll learn to:
- ❓ Ask the right questions when faced with business problems
- 🎯 Choose appropriate analytical techniques for different scenarios
- ✅ Clean and prepare data with integrity and attention to detail
- 💡 Communicate technical findings to non-technical audiences
- 📁 Build a professional portfolio of projects