Python Financial Analyst: Financial Analysis Zero to Advance

Explore Python basics for financial data by using hash-based comments, variables, and the print function to display values and calculate the average revenue across 2020–2022.

Master Python data types and variables in finance, exploring integers, floats, strings, lists, tuples, dictionaries, booleans, and combining types into financial summaries.

Learn to create lists, index items, and slice Python lists with and without steps using real-world stock prices, including negative indexing for end elements.

Explore creating and manipulating nested lists in Python to manage multi-dimensional stock data. Learn to access, modify, subset, and slice data, and extract price history with list comprehensions.

Learn Python list methods and functions to manage financial data, add stock prices with append, insert, remove, extend, and use len and a lambda key to find the maximum price.

Master array-based financial analysis in Python by using NumPy to perform elementwise operations, compute percentage change relative to the first day, and subset data with index-based and conditional filtering.

Explore 2D arrays with numpy to create, subset, and analyze financial data; compute mean, max, and min across columns, and build a reusable function for percentage change.

Generate numerical sequences with numpy arange and leverage arrays to analyze stock prices, compute the average, identify days above average, and flag health care sector days.

Master data manipulation with pandas data frames for financial analysis: create and inspect frames, read market history csv, clean data with dropna and datetime conversion, and visualize trends with matplotlib.

Learn how to access and manipulate data in a pandas data frame using column names and indexes, including conditional indexing and multi-level indexing with real-world stock data.

Learn to aggregate and summarize financial data in Python, compute mean and median prices, and apply grouped aggregation with multiple metrics.

Learn to create new columns, drop unused ones, and manipulate data with pandas for financial analysis. Apply categorization with cut, function application, and conditional updates to shape datasets.

Explore financial data in Python using pandas to preview with head and tail and summarize with describe, including numeric and non-numeric insights, and visualize distributions with a box plot.

Master stock data filtering in Python with pandas, applying numeric thresholds, date ranges, boolean indexing, and inclusion or exclusion to target specific stocks.

Master visualizing financial data in Python with Matplotlib and Pyplot. Create line plots and scatter plots, label axes and titles, and use legends to compare data.

Explore histograms to visualize financial data distributions, compare normal and non-normal data, and add legends for clear interpretation of stock returns, asset prices, and volatility.

Explore visualizing trends with real-world financial data by creating a histogram of p e ratios to understand valuation trends of various stocks, identify an outlier, and name it.

Learn to choose the appropriate plot type for financial data in Python, using line plots for trends, bar plots for distributions, and scatter plots for relationships with matplotlib and seaborn.

Master the weighted average cost of capital (WACC) and compare net present value and internal rate of return for projects using Python, with debt and equity financing insights.

Explore comparing projects with different lifespans by computing IRR and NPV, then standardize cash flows with the equivalent annual annuity, all implemented in Python to guide decisions.

Explore mortgage basics, learn how to calculate the monthly mortgage payment with Python, and automate scenario analyses using loan amount, annual interest rate, and loan term.

Explore how to calculate, simulate, and visualize loan repayments in Python, mastering amortization, interest, and principal payments to understand remaining balances and repayment dynamics.

Explore home ownership equity and forecasting by calculating cumulative mortgage payments and equity. Model rising and falling housing prices and underwater mortgages with Python to visualize equity dynamics.

Analyze salary and taxes, manage monthly expenses and savings, and forecast salary growth and inflation to build a robust budgeting proposal using Python.

Calculate your net worth by subtracting liabilities from assets using Python and bison. Set goals like becoming a millionaire and simulate investing a percentage of income to plan your finances.

Explore how time and compound interest grow wealth, adjust net worth for inflation, and model investments with Python to visualize future value and informed decisions.

Explore time series analysis in finance using Python, set up the environment, and build your first time series by indexing with dates in pandas and plotting trends.

Learn to create datetimes for specific dates, convert datetimes from strings, and format datetimes using strftime for time-sensitive finance data.

Learn to manipulate Python datetime objects for financial time series by accessing attributes, comparing timestamps, and creating relative datetimes to analyze temporal relationships.

Analyze time series growth rates and daily percentage change in stock prices, compare asset class performance against a benchmark, and visualize the performance difference over time.

Explore upsampling and interpolation to convert financial time series between daily, monthly, and yearly frequencies, fill gaps, and compare interpolated earnings per share with stock performance for insights.

Learn to index and resample financial time series in Python. Convert the date column to a time-based index and compare annual versus monthly stock price trends using mean values.

Explore expanding window functions in pandas to compute cumulative sums and cumulative returns in time series data, comparing 1000 invested in Google and Apple over time via hands-on coding.

Analyze stock price series using lags and changes to uncover patterns in time series. Compute price changes and multivariate returns, then plot five day returns to reveal trends for investors.

Learn the fundamentals of time series analysis in Python and visualize and forecast Bitcoin prices using data cleaning, decomposition, seasonal adjustments, transformations, and plots like line and autocorrelation.

Clean and preprocess bitcoin time series data with python, test stationarity using ADF, apply ARIMA and Shirima, exponential smoothing methods, Holt-Winters, and prophet, and evaluate using MAE, MSE, RMSE.

Visualize Bitcoin price time series with rolling averages, seasonal plots, autocorrelation, and seasonal decomposition on the Kaggle bitcoin historical dataset, and interpret trend, seasonality, and residuals for informed forecasting.

Learn to build and compare forecasting models for time series, including ARIMA, SARIMA, and Prophet, using bitcoin price data with diagnostics, model fitting, and out-of-sample forecasts.

Explore time series forecasting for bitcoin using Python, covering model evaluation with MAE and RMSE, STL decomposition, anomaly detection, and LSTM-based predictions.