The “Time Series Analysis with Python” training course will provide your delegates with all essential knowledge to allow wrangling, processing, analysis and forecasting of time series data using specialised libraries such as pandas, NumPy, scikit-learn, statsmodels, SciPy and fbprophet for Python programming language. Whether you wish to analyse financial data, predict sales or marketing revenue, or understand temporal patterns in your social, medical or economic data, this course will provide you with theoretical and practical understanding on how to clean, visualise and model time series data in your workflows using Python programming language.
During the course, the attendees will first learn to manipulate the imported data, extract necessary date/time stamps and transform the processed data into supported time series Python data structures. They will then proceed to perform essential time series exploratory and decomposition operations, calculate selected moving/rolling single-value statistics (incl. moving averages), convert between differing time frequencies, visualise and prepare data for predictions. The forecasting part will include sessions on estimating linear, non-linear and locally-weighted trends, multiple regression models, random walks, ARMA, ARIMA and more advanced dynamic regression and neural network approaches.
Basic course information
Minimum recommended duration: 4-5 full days or 8-10 half-days (can be spread across multiple weeks)
Programming languages used: Python
Minimum number of attendees: 5
Course level: For beginners/novice/intermediate users of Python, also good as a “refresher” for more advanced analysts.
Pre-requisites: It is recommended that the attendees have practical experience in data processing or quantitative research – gathered from either professional work or university education/research. A good knowledge of statistics would be beneficial. It is advisable that the course is preceded with our “Applied Data Science with Python”.
IT recommendations: In order to benefit from the contents of the course it is recommended that attendees have the most recent version of Anaconda distribution of Python (by Continuum Analytics) installed on their laptops (any operating system). As Anaconda’s Python is a free and fully-supported distribution you can download it directly from https://www.continuum.io/downloads. Please contact us should you have any questions related to the installation process or should you wish to use a different setup for your course.
The programme for each in-house training course is discussed and agreed individually with the client. The proposed contents of the course may include (but is not limited to) the following concepts and topics:
Import, clean and pre-process time series data using standard R functionalities and its third-party libraries e.g. pandas, NumPy, scikit-learn, SciPy, statsmodels,
Manipulate time series data structures including their indexing, subsetting and slicing,
Convert date/time stamps into varying date/time units, convert between time series frequencies using different resampling methods and dealing with missing values,
Carry out time series data aggregations using pivot tables, cross tabulations and data summaries,
Decompose and visualise all components of time series data (trend, seasonality, residuals, etc.),
Calculate moving/rolling averages and other rolling single-value statistics, lagged and shifted time series, percentage changes between data points of different time series frequencies,
Assess stationarity of time series and perform varying methods of differencing,
Predict future data using simple linear trend and multiple regression models for time series data including methods of measuring model accuracy and model diagnostics,
Apply exponential smoothing and seasonal methods for time series forecasting purposes,
Estimate parameters of non-linear or locally-weighted models, regression trees and random walk models,
Perform more advanced forecasting methods using Autoregressive Moving Average (ARMA) and Autoregressive Integrated Moving Average (ARIMA) models,
Measure the ARIMA model accuracy using various accuracy metrics, compare and select the models,
Introduction to dynamic regression and advanced classification models, survival analysis and simple neural networks on time series data.
Customise the course
We can adapt our in-house training courses to address your specific needs and requirements e.g.:
The course can be designed to include your own data. If it is not possible e.g. due to data security issues, we can customise the course to contain exercises that address similar problems,
The course period can be spread across multiple weeks/months depending on your needs and availability – this will allow your delegates to revise and practise the learnt skills before the next session and provide them with additional time to internalise all presented material,
The course can include a custom project spread across several weeks/months with a follow-up session at the end of the period,
As all our in-house training courses are quoted individually, the final cost quotation will be based on several factors: the number of attendees, days of training (plus additional support/project guidance if needed), location of the training, complexity of IT setup and the extent of course customisation.
Arrange this course at your organisation
If you are interested in this in-house training course, please press Ask For Quote button in the top part of the page to enquire about and request a quote for this course based on your specific needs and desired outcomes of the training.
In your enquiry please include the following information:
contact details to a person who should receive the quote,
number of delegates you would like to train,
approximate number of days (or half-days) you would like to arrange the course for (including additional support/project guidance if needed),
location of the training venue,
any details on course customisation or specific topics you would like the course to address – most importantly, please indicate desired outcomes of the course if different then presented above,
any other questions you may have.