Course: Time series analysis using Python
Unlock the power of time series data with our comprehensive "Time Series Analysis with Python" course. Designed for data professionals and analysts, this course dives deep into the unique characteristics of temporal data, from foundational concepts like seasonality and autocorrelation to advanced modeling techniques using ARIMA and machine learning models. Through hands-on exercises and real-world examples, you'll learn how to preprocess, visualize, and forecast time series data, equipping you with the skills to make accurate predictions and informed decisions. Whether you're working in finance, economics, or any field where time-based data is crucial, this course will provide you with the tools and knowledge to excel.
Course Outcomes
Upon completion of this course, participants will:
- Understand key concepts and characteristics of time series data, including seasonality, stationarity, lags, and autocorrelation.
- Handle and preprocess time-based data, and create insightful visualizations that reveal patterns and trends
- Build and assess various time series models, including auto-regressive (AR), moving average (MA), and ARIMA models, for accurate forecasting.
- Apply machine learning techniques to time series data using regression models
Who It's For
This course is ideal for:
- Business and Data Analysts
- Business, Finance, Marketing, and Sales professionals
- Data scientists and machine learning engineers
- Statisticians
Prerequisites
- A fluent in Python, and its libraries NumPy, Pandas, and Matplotlib.
- (Recommended) completed “Data Analytics using Python” course.
Course Modules
1. Foundations of Time Series Analysis
- Handling and dealing with time-based data.
- Feature engineering techniques for temporal data.
- Resampling methods and data imputation strategies.
- Visualizing time series data effectively.
2. Characteristics of Time Series Data
- Understanding seasonality in time series.
- Exploring concepts of lags and forecasting horizons.
- Identifying and applying stationarity in time series data.
- Analyzing autocorrelation and its significance.
3. Time Series Modeling
- Explaining white noise and random walk time series.
- Developing auto-regressive (AR) models.
- Implementing moving average (MA) models.
- Building and applying ARIMA models for forecasting.
4. Time Series in Machine Learning
- Introduction to machine learning and regression models
- Modeling time series as a regression problem.
- Evaluate time series model using regression metrics
Execution approach:
Long run
32 hours
5 weeks
Run over short weekly sessions allowing participants to progressively build their skills and knowledge.
