Time series data is one of the most common data types in the industry and you will probably be working with it in your career. Therefore understanding how to work with it and how to apply analytical and forecasting techniques are critical for every aspiring data scientist. In this series of articles, I will go through the basic techniques to work with time-series data, starting from data manipulation, analysis, and visualization to understand your data and prepare it for and then using the statistical, machine, and deep learning techniques for forecasting and classification. It will be more of a practical guide in which I will be applying each discussed and explained concept to real data.

This repository contins the codes and the data used in the Time Series In Python series of articles on medium, this series will contain ten articles as the following:

1. Manipulating Time Series Data In Python Pandas [A Practical Guide] Codes & Data | Article
2. Time Series Analysis in Python Pandas [A Practical Guide] Codes & Data | Article
3. Visualizing Time Series Data in Python [A practical Guide] Codes & Data | Article
4. Arima Models in Python [A practical Guide] [Part1] Codes & Data | Article
5. Arima Models in Python [A practical Guide] [Part2] Codes & Data | Article
6. Machine Learning for Time Series Data [A practical Guide] [Regression] Codes & Data | Article
7. Machine Learning for Time Series Data [A practical Guide] [Classifcation] Codes & Data | Article
8. Deep Learning for Time Series Data [A practical Guide] Codes & Data | Article
9. Time Series Forecasting project using statistical analysis, machine learning & deep learning Codes & Data | Article
10. Time Series Classification using statistical analysis, machine learning & deep learning Codes & Data | Article