Applied Data Science for Finance

Learn why typing code from the screen, testing beforehand, and practicing with working examples builds real data science skills for finance rather than just downloading files.

Explore essential math symbols used in data science for finance, including limits, derivatives, gradients, integrals, transforms, and matrices, and learn how they drive optimization, probability, and analysis.

Explore essential math symbols for data analysis and optimization, including mu (mean), sigma (std dev), variance, covariance, rho, arg max/min, and set operations like union and intersection.

Explore essential math symbols—integers z, natural numbers n, complex c, rational q; logical operators, Dirac delta, sigma, lambda, alpha, beta, gamma, theta, omega, phi, epsilon, and definition.

Explore essential math symbols used in finance data science, including absolute value, floor, ceiling, infinity, limits, isomorphism, and the Big-O, theta, and omega notions of algorithmic complexity.

Learn the basics of Python, a high-level interpreted language prized for readability and versatility, its history since 1991, and its wide ecosystem of libraries and cross-language integration.

See how Anaconda, Jupyter, and VSCode streamline Python development by managing environments, notebooks, and code with pre-installed libraries and powerful editing tools.

Learn Google Colab, a browser-based cloud platform that runs Python with pre-installed libraries and Google Drive integration, offering free GPUs and collaboration for data analysis and machine learning.

Install and configure Python, Anaconda, and Visual Studio Code, ensuring Python path is set, verify installations via version checks, and prepare VS Code with Python extension for data science workflows.

Dive into Python syntax, variables, and data types, including integers, floats, strings, booleans, and none. Master type conversions, dynamic typing, arithmetic and logical operations, comparisons, and operator precedence.

Learn Python data structures: lists, tuples, and sets. Discover how lists are mutable, tuples are immutable, and how to use indexing, slicing, and set operations like union, intersection, and difference.

Explore how Python uses conditional statements and loops to control program behavior, including if, elif, else, for and while loops, range, and break and continue control statements.

Explore recursion, factorial and tail recursion, currying, partial functions, closures with state, decorators, context management, nonlocal usage, and generators for modular, efficient Python data processing.

Master Python dictionaries, a flexible key-value structure with unique keys and adaptable values, covering access with brackets or get, addition, updating, removal, and dictionary comprehensions.

Explore modules, packages, and importing libraries in Python, including the standard library and third-party tools like NumPy and pandas; learn import syntax, aliases, and focused imports.

Learn Python file handling by opening, reading, writing, and closing text and CSV files, and using pandas to load, manipulate, filter, and export data.

Develop robust Python programs using try and except blocks to catch zero division, file not found, and value errors, using finally and custom exceptions for input validation.

Explore object oriented programming in Python, covering classes, objects, attributes, and methods, and master encapsulation, inheritance, polymorphism, and the super function with practical dog and animal examples.

Explore advanced list operations in Python, including list comprehensions, enumerate, and zip, with filtering, transforming, pairing, and unzipping data to write concise, readable code.

Improve data quality by profiling, fixing, standardizing, and validating datasets to ensure accuracy, completeness, consistency, timeliness, relevance, and automation for reliable finance analytics.

Learn data cleaning techniques to remove dirty data and fix missing values, duplicates, outliers, and inconsistencies, then standardize, validate, and preprocess with pipelines.

handle missing values in data pre-processing to prevent bias and unreliable results. apply imputation techniques such as mean, median, mode, and k-nearest neighbors, with a practical age and income example.

Identify and handle outliers to protect data integrity and reliable results, using boxplots, histograms, and scatter plots, with z-score and IQR methods alongside model-based approaches like DBSCAN and isolation forest.

Learn how feature scaling and normalization ensure all data features contribute equally, improve convergence, and support distance-based algorithms like k-nearest neighbors, support vector machines, and k-means.

Standardization transforms features to zero mean and unit variance, ensuring equal contribution and faster training for models such as logistic regression, SVM, and PCA, even across diverse scales.

Learn how to encode categorical variables to prepare data for machine learning, including label, one hot, ordinal, binary, target, and frequency encodings, with use cases and risks of overfitting.

Apply feature engineering by transforming raw data into informative inputs through problem understanding, exploratory data analysis, feature construction, transformations, and selection to boost model performance.

Reduce dataset dimensionality to retain essential information while lowering costs and improving visualization. Explore feature selection and extraction techniques like PCA, LDA, t-SNE, and autoencoders, along with their trade-offs.

Explore data visualization basics in Python using Matplotlib and Seaborn to create line plots, bar charts, histograms, heatmaps, violin plots, and customize styles and palettes.

Explore practical performance analysis of trading strategies with the pyfolio library, using simulated returns to interpret charts, drawdowns, and key metrics like Sharpe ratio, Calmar ratio, volatility, and value at risk.

Create synthetic stock data and visualize it with MPL finance to plot candlestick charts, volume, and moving averages of 10-day, 20-day, and 50-day from open, high, low, and close prices.

Develop a data-driven stock portfolio using historical prices and riskfolio-lib to optimize via the Markowitz mean-variance model, maximizing Sharpe ratio and revealing weights for Apple, Microsoft, Tesla, and Amazon.

Fetch real-world stock data with yfinance, process it with pandas, and visualize price, volume, candlesticks, and a correlation heatmap in an interactive Altair dashboard.

Apply portfolio optimization to balance risk and return by analyzing historical data for Apple and Microsoft, computing mean returns and covariance, and solving for 15% target return with SciPy minimize.

Learn to model supply and demand with Python, calculate equilibrium price and quantity, visualize curves, and compute consumer and producer surplus to assess market welfare.

Learn to perform technical analysis in finance using python and the finTA library, computing moving averages, RSI, bollinger bands, and MACD on historical data from yfinance.

Master basic finance concepts, from the time value of money, present and future value, annuities and perpetuities, to risk, return, market types, financial instruments, and corporate financing decisions.

Explore how corporate finance maximizes shareholder value through profitability, growth, risk management, and capital efficiency, and examine capital budgeting, capital structure, working capital management, and governance.

Explore how financial markets enable capital formation and efficient resource allocation. Learn how price discovery, liquidity, and risk management operate across stocks, bonds, currencies, and derivatives.

explore financial ratios across liquidity, profitability, efficiency, and leverage to assess a company using current and quick ratios, margins, roa, roe, asset turnover, and debt metrics.

Explore the income statement, balance sheet, and cash flow statement to understand revenue, expenses, assets, liabilities, and equity, and assess cash flow health.

Explore macroeconomics, including GDP, inflation, unemployment, and policy tools. Learn how fiscal and monetary policies, exchange rates, and trade impact growth, stability, and living standards.

Explore bonds and fixed income securities, covering face value, coupons, maturity, and yields, plus government and corporate bonds and inflation linked bonds.

Master time value of money concepts, including present value, future value, discounting, and compounding, and apply them to annuities, npv, irr, loans, retirement planning, and bonds.

Explore how technical analysis uses price, volume, and chart patterns—line, bar, and candlesticks—to identify trends, support and resistance, and signals from indicators like moving averages, RSI, MACD, and Bollinger bands.

Explore the risk and return relationship, including market and unsystematic risks, and how standard deviation, beta, expected and real returns, and diversification guide asset allocation.

Explore portfolio management by balancing risk and return through diversification and strategic asset allocation, with active and passive approaches, rebalancing, and monitoring to align with goals.

Explore financial instruments as contracts representing assets and liabilities; cover equity, debt, derivatives, hybrid instruments, trading venues, valuation methods, and risk factors.

Explore the forex market’s role as the world’s largest 24/5 otc arena, where currency pairs, base and quote currencies, spreads, leverage, and traders shape exchange rates.

Explore fundamental analysis to assess intrinsic value through qualitative and quantitative data, financial statements, ratios, macro factors, and valuation methods like discounted cash flow and comparable company analysis.