Python for Data Analytics - Beginner to Advanced

Download and install Python from Python.org for your OS, then install Visual Studio Code. Verify Python in the terminal, and install the Python extension and Jupyter notebooks to start coding.

Explore Python files and Jupyter notebooks, comparing .py and .ipynb formats, and run code cell by cell with code and markdown cells, outlines, and kernels.

Install and import pandas for python data analytics, then create series and dataframes, and handle missing data. Load and save csv and excel files in python using jupyter notebooks.

Master pandas data manipulation with loc and iloc indexing, conditional filtering and query, sorting and ranking, merging and concatenating, cleaning duplicates and outliers, encoding categoricals, reshaping, and date/time analysis.

Explore pandas groupby for data aggregation, apply multiple and custom aggregations, and visualize time series, while mastering multi-index dataframes, apply and map transformations, and text manipulations.

Import pandas, set the date index for time series data, filter by date ranges, compute rolling 3-month and 12-month averages, and plot line charts.

Identify seasonality by using pandas to limit data to 2010–2017, plot with matplotlib, and draw black dotted lines to reveal repeating patterns.

Apply the Dickey-Fuller test to a price series to assess stationarity, using the ADF test statistic and p-value with a 0.05 threshold to decide unit root presence.

Examine autocorrelation in monthly prices by reading csv data with pandas and computing 1, 3, 6, and 9 month lags to reveal strong autocorrelation.

Import and analyze time series data by using pandas and matplotlib to read data, apply statsmodels' seasonal decomposition, and visualize trend, seasonality, and residuals.

Explore numpy, a Python library for arrays that enables efficient calculations, supports multi dimensional objects, masks, and tensors, and provides high level functions for linear algebra and Fourier transform.

Learn numpy array indexing in Python by exploring one dimensional and two dimensional arrays, using zero based indexing, and accessing elements like a table of rows and columns.

Learn to slice one dimensional and two dimensional NumPy arrays using indexing and slicing to select all values or specific ranges, and practice iterating with for loops and simple operations.

Explore matplotlib, a python data visualization library, with hands-on examples of plt dot plot, scatter, bar, stem, and step plots, and learn plot parameters.

Explore Matplotlib basics by importing matplotlib.pyplot as plt, creating data lists, and visualizing with plot, scatter, bar, stem, and step plots; customize color, line style, labels, and title.

Import the seaborn library, create data lists, and visualize distributions with plots by adjusting bins, bandwidth, and hue, then generate x, y, and joint plots.

Visualize statistical relationships by importing libraries and creating lists, then build a dot plot with x equals A and y equals B, and try a line plot.

Learn to plot categorical data with cat plots by building a dataframe from a list and choosing box, violin, or point plots to visualize categories.

Import libraries, read wine quality csv, inspect head and tail, check shape, and clean duplicates. Compute quality statistics and highlight alcohol's positive and volatile acidity's negative correlations with a scatterplot.

Explore a 1000-row data analysis project that imports libraries, cleans data, drops user_id, and shows older, higher-earning customers are likelier to purchase.

Learn to predict gold prices from past values by reading a CSV with a date index, parsing dates, filtering ten years, and plotting monthly and yearly 12-month rolling averages.