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Noble Desktop

FinTech Bootcamp

via Noble Desktop

Overview

Get the skills you need for a career in finance technology with the FinTech Bootcamp. Learn Python programming, data science, financial analysis, data visualization, and machine learning to become a Financial Analyst, Data Scientist, or Data Analyst.

Syllabus

  • Python for Data Science Bootcamp
    • 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
  • SQL Bootcamp
    • 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 a database
    • Combine information from multiple tables with JOIN statements
    • Filter data, group it, and sort it to extract the specific info you need
    • Advanced techniques like Subqueries, String Functions, and IF‑Else logic with CASE
    • How to use Views and Functions with parameters instead of directly querying tables
  • Python for Automation
    • Scrape (extract) text and images from websites
    • Schedule Python scripts to run automatically
    • Automate browser interactions, reporting, and messaging
  • Python Data Visualization & Interactive Dashboards
    • 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
  • Python Machine Learning Bootcamp
    • 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

Taught by

Mourad Kattan and Dan Rodney

Reviews

4 rating at Noble Desktop based on 608 ratings

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