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 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 contains 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 | Kaggle Notebook
2. Time Series Analysis in Python Pandas [A Practical Guide] Codes & Data | Article | Kaggle Notebook
3. Visualizing Time Series Data in Python [A practical Guide] Codes & Data | Article | Kaggle Notebook
4. Arima Models in Python [A practical Guide] [Part1] Codes & Data | Article | Kaggle Notebook
5. Arima Models in Python [A practical Guide] [Part2] Codes & Data | Article | Kaggle Notebook
6. Machine Learning for Time Series Data [A practical Guide] [Regression] Codes & Data | Article | Kaggle Notebook
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. Ten Top Time Series Courses to Proficient This Important Data Science Skills
11. Level Up Your Time Series Analysis Skills with These 5 Books
12. 13 Guided Time Series Projects to Build Your Portfolio