Time-Series Analysis & Regression Forecasting with Python
Published 4/2025
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Transform raw data into powerful forecasts with Python-learn time-series modeling, regression, real-world forecasting.
What you’ll learn
Time-series concepts, notations, and use cases. Data loading, preprocessing, and feature engineering using Python.
Visualizing time-series data for insights. Forecasting using AR, MA, ARIMA, and SARIMA models.
Handling seasonality and trend components in data. Performing train-test splits and model validation correctly.
Applying linear regression (simple and multiple) for forecasting. Interpreting regression outputs and evaluating model accuracy.
Requirements
Familiarity with basic statistics (mean, median, correlation).
Some exposure to pandas, matplotlib, or NumPy is helpful but not mandatory.
No prior experience with time-series or regression needed.
Description
Who this course is for
Aspiring data scientists and analysts looking to master forecasting.
Business intelligence professionals wanting to enhance reporting with prediction.
Students in data science or machine learning programs.
Python developers eager to apply coding skills to statistical forecasting.
Anyone curious about time-series analysis, regression, or predictive modeling.
