FinTech Bootcamp
IDCFT

• Learn Python fundamentals, including variables, data types, functions, loops, and control flow, for building robust programs
• Work with complex data structures such as dictionaries and lists to efficiently organize and access data
• Use NumPy and Pandas to import, clean, and manipulate datasets for analysis and exploration
• Generate descriptive statistics and apply filtering, grouping, and pivoting techniques to gain deeper insights
• Visualize data using Matplotlib and create clear, customized charts, including bar graphs, histograms, and scatter plots
• Gain the practical skills needed to transition into machine learning with a solid understanding of data science workflows

Learn how to extract, filter, and manipulate data using SQL. This course covers PostgreSQL fundamentals, database querying, table joins, and advanced techniques for handling large datasets.

• Explore information stored in a database (tables, columns, rows, etc.) using the graphical interface of DBeaver, a popular free database app.
• Write SQL queries to retrieve data from tables in the database
• Combine information from multiple tables using JOIN statements
• Filter data, group it, and sort it to extract the specific information you need
• Master advanced techniques, including subqueries, string functions, and IF‑Else logic with CASE
• Learn how to use views and functions with parameters instead of directly querying tables

• Understand how websites are structured with HTML and CSS to identify elements for data extraction
• Learn Python fundamentals, such as variables, data types, conditionals, loops, and list manipulation
• Use the Requests and Beautiful Soup libraries to perform web scraping and target specific content
• Write loops to automate web scraping across multiple pages and streamline repetitive tasks
• Store scraped data in different formats, such as text files and CSVs, for analysis and reporting
• Schedule Python scripts to run on a regular basis, enabling continuous data collection and automating workflows

Transform raw data into interactive visual insights by building dashboards with Python’s top visualization tools. This course blends analysis, design, and deployment to help you showcase data professionally.

• Work with real-life datasets using Python’s core libraries, including NumPy for numerical computing and Pandas for data manipulation
• Create static and interactive visualizations using Matplotlib, Seaborn, and Plotly to clearly communicate trends and patterns
• Build multi-component dashboards using Dash Enterprise, incorporating callbacks, sliders, date pickers, and more
• Practice hands-on development by applying new skills to personalized projects with guided instructor support
• Publish your dashboards online using GitHub and Heroku to demonstrate your work to potential employers or clients
• Explore best practices for styling and structuring visual narratives that are clear, persuasive, and engaging

• Explore foundational techniques like linear and logistic regression for modeling numerical and categorical data
• Understand the difference between regression and classification problems and when to apply each approach
• Build and evaluate models using k-nearest neighbors, decision trees, and ensemble methods like random forest
• Learn key concepts such as cross-validation, training vs. test sets, and performance metrics like mean squared error
• Apply feature engineering techniques to improve model accuracy while managing overfitting and bias-variance tradeoffs
• Use Python's essential data science libraries, NumPy, Pandas, and scikit-learn, to structure data and implement algorithms
• Gain insights into how machine learning powers systems at companies like Netflix, Spotify, and Amazon
• Complete a final portfolio project that demonstrates your ability to apply machine learning to solve real problems

Learn how to extract, filter, and manipulate data using SQL. This course covers PostgreSQL fundamentals, database querying, table joins, and advanced techniques for handling large datasets.

• Write SQL queries to retrieve, filter, and sort data efficiently.
• Use joins to combine information from multiple tables and establish relationships.
• Apply aggregate functions like SUM, COUNT, AVG, and GROUP BY to summarize data.
• Work with subqueries, conditional logic (CASE statements), and advanced string functions.
• Optimize queries using indexes, data type conversions, and best practices.
• Explore views and user-defined functions to streamline database management.

• Handle different types of data such as integers, floats, and strings
• Control the flow of your programs with conditional statements, loops, and functions
• Reuse and simplify code with object-oriented programming
• Analyze tabular data with NumPy and Pandas
• Create graphs and visualizations with Matplotlib
• Make predictions with linear regression, using scikit-learn

• How to clean and balance your data using the Pandas library
• Applying machine learning algorithms such as logistic regression and random forest using the scikit-learn library
• Choosing good features to use as input for your algorithms
• Properly splitting data into training, test and cross-validation sets
• Important theoretical concepts like overfitting, variance and bias
• Evaluating the performance of your machine learning models

• Scrape (extract) text and images from websites
• Schedule Python scripts to run automatically
• Automate browser interactions, reporting, and messaging

• Plan & present a data story
  
• Gather and manipulate data from different sources
• Find data stories through exploratory data analysis 
• Manipulate data with NumPy and Pandas.
• Use advanced Python visualization libraries Plotly and Dash
• Build a dashboard
• Apply the rules of effective dashboard design to create professional data science solutions
• Go live with your project & deploy the dashboard on a live server

Throughout this program, you will complete three capstone projects to showcase in your portfolio: 

Machine Learning & AI Capstone

• Choose, clean, and engineer features from a structured dataset to train machine learning models (e.g., logistic regression, random forest), evaluate performance, and visualize results clearly.
• Deliver a professional presentation detailing your data processing workflow, modeling techniques, and insights discovered using Python libraries like pandas, scikit-learn, and Matplotlib.

Python for AI Capstone (Choose One of Two)

• AI Chat Assistant: Build an interactive chat assistant embedded on a webpage, using Flask and JavaScript to integrate with OpenAI’s API for context-aware user interactions.
• Collectibles Identification App: Develop a Flask-based web app allowing image uploads of collectible items, leveraging OpenAI to identify items, generate descriptive metadata, and dynamically display logged session history.

Python Data Visualization Capstone

• Clean, analyze, and visualize global CO₂ emissions alongside GDP and population data, highlighting trends and correlations through insightful visualizations with Matplotlib, seaborn, and plotly.
• Build a responsive Dash dashboard enabling interactive exploration of emissions data, clearly communicating insights such as regional trends, GDP-emission correlations, and emission anomalies.

You will work on your capstone projects both in and outside of class, using scheduled mentoring sessions to review your progress, ask questions, and get personalized feedback from your instructor.