Mathew K AnalyticsPython For Time Series
A free 30-lesson course. Every lesson has a video walkthrough and a Jupyter notebook you can download or open in Google Colab; 1 lessons include real datasets. Tools covered: pandas, Matplotlib, NumPy, scikit-learn, statsmodels, SciPy.
11 minLesson 1
01 Introduction to Python in Jupyter Notebook
28 minLesson 3
Master Python Data Structures: Lists, Tuples, Sets & Dictionaries Explained Clearly
24 minLesson 5
Comprehensive Guide to Pandas Series and DataFrames for Data Analysis in Python
11 minLesson 6
How to Import and Export CSV, Excel, and JSON Files in Python for Data Management
10 minLesson 9
Understanding Probability Distributions in Python with SciPy for Statistical Analysis
14 minLesson 10
Understanding Correlation and Covariance in Python for Statistical Analysis
34 minLesson 12
Data Visualization with Matplotlib
12 minLesson 13
Mastering Resampling and Aggregation of Time Series Data with Pandas in Python
16 minLesson 14
Master Rolling Mean & Exponential Moving Average in Python for Time Series Analysis
16 minLesson 15
Foundations of Time Series Analysis: Understanding Trend and Seasonality in Data
19 minLesson 16
Understanding Trend, Seasonality, Cyclicity, and Noise in Time Series Analysis
12 minLesson 17
Resampling & Aggregation in Pandas: Master Time Series Data Analysis with Python
14 minLesson 18
Handling Missing Data in Time Series: Proven Techniques for Accurate Analysis
14 minLesson 19
Understanding Stationarity and the Augmented Dickey-Fuller Test in Time Series Analysis
13 minLesson 20
Understanding Autocorrelation and Partial Autocorrelation in Time Series Analysis
10 minLesson 21
Seasonal Decomposition in Python: Techniques for Time Series Analysis and Interpretation
15 minLesson 22
Understanding Autoregressive, Moving Average, and ARMA Models for Time Series Forecasting
17 minLesson 24
Seasonal ARIMA and SARIMAX in Python: A Guide to Time Series Forecasting
12 minLesson 25
Understanding Exponential Smoothing and Holt-Winters Forecasting Models in Python
14 minLesson 26
Understanding Model Diagnostics and Residual Analysis for Time Series Forecasting in Python
12 minLesson 27
Creating Lag and Rolling Features for Time Series Analysis in Python
12 minLesson 28
Encoding Seasonality in Time Series Data Using Fourier Terms for Accurate Modeling
11 minLesson 29
Supervised Learning for Time Series in Python: Data Preparation and Model Setup
14 minLesson 30
Linear Regression and Regularization Techniques for Accurate Forecasting in Machine Learning
14 minLesson 31
Using Random Forest and XGBoost for Accurate Time Series Forecasting
15 minLesson 32
Walk-Forward Validation in Python: A Clear Guide for Time Series Model Evaluation
16 minLesson 33
Understanding 7 Key Forecast Accuracy Metrics for Evaluating Models in Python
10 minLesson 36
Understanding Convolutional Neural Networks for Time Series Analysis in Christian Data Studies
11 minLesson 38
Sequence-to-Sequence Forecasting in Python: Deep Learning for Time Series Prediction
10 minLesson 42