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Time Series Analysis with Python

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Time Series Analysis with Python

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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.
 

 

Programme outline

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.


Contact us to discuss your training needs
If you require more information about this specific training course or would like to discuss other details not covered above, please contact us directly.
Contact Us

Time Series Analysis with Python

What did previous delegates say about our courses?

“A very clear and practical overview of R and how it can be used in real world data sets. Learning from an expert!” - G's Group Holdings - Applied Data Science with R
“Excellent course for beginners and not-so-beginners in data analysis with Python. Simon brings real-world experience to the table and is a patient, knowledgeable and personable instructor.” - Roxana Lupu, Medallia - Applied Data Science with Python
“Thank you for sharing great knowledge with us which will be used for and will contribute a lot towards our business.” - Cabot Financial Ltd - Machine Learning with R
“Highly recommended, best place to start for machine learning with R!” - Max Fennelly, Pearson Ham - Machine Learning with R
"Great introduction to Hadoop from a clearly knowledgable demonstrator." - Feargal Egan, G-Research - Introduction to Hadoop with Java
“Simon is very knowledgeable and provided lots of solutions to consider in dealing with Big Data.” - Aidan Mews, Ministry of Justice - Big Data Methods with R
“The course has given me a greater understanding of Python and its capabilities.” - Mike Watts, Adactus Housing - Applied Data Science with Python
“This course was a great way to get an introduction to R and how it can be used to perform statistical analysis and run data visualisations.” - Daria Kuznetsova, Director of Strategy and Research at Teach First - Applied Data Science with R
“A really good, hands-on introduction that left me aware of and confident to use Hadoop stack. The practical writing of MapReduce and querying Hive was extremely useful.” - Chris Mainey, University College London & University Hospital Birmingham - Introduction to Hadoop with Java
“This course teaches you everything you need to know about the basic operations of R and useful exploratory data analysis tools. The content is both challenging and engaging, and the trainer is extremely supportive.” - Thomas Weeks, NHS Improvement - Applied Data Science with R
“The Machine Learning with R course by Mind Project provides a fantastic overview of supervised and unsupervised ML models and their applications. The course offers details on important topics such as advanced model evaluation metrics which are not emphasised on other ML courses.” - James Patten, Deloitte UK - Machine Learning with R
“I don't think I will ever open Excel again. The course has confirmed my perception of the applicability of R in the NHS.” - Paul Stroner, Deputy Director of Information and Analytics, NHS - Applied Data Science with R
“Really enjoyed the course! Simon had a great way of taking us from simple R topics to more complex Hadoop related concepts, which opened the window into Big Data methods.” - Sofia Diamanti, Deloitte UK - Big Data Methods with R
“This was a great course – very practical and taught in a relaxed style.” - Sophie Reid, Design Council - Applied Data Science with R
“The best organised data science course I have found so far.” - David Metcalfe, Doctoral Candidate, University of Oxford - Applied Data Science with Python
“Enjoyed the course. The tutor is in command of his subject and patient with his students.” - Dean Allsopp, DA Consulting - Machine Learning with R
“Simon is a great teacher – his workshops flow well and move at a good pace. I’ve really enjoyed both courses with Mind Project and I would definitely recommend them.” - Ellen Talbot, University of Liverpool - Machine Learning with R and Big Data Methods with R
“Excellent course for the intro to R, I highly recommend it.” - Dr Edward Barker, Institute of Psychiatry, King's College London - Applied Data Science with R
“The course provides a beginner enough knowledge to start analysis using R. I appreciated the instructor’s knowledge and the willingness to give as much explanation as possible.” - Prof. Sunil Sahadev, Salford Business School - Applied Data Science with R
“Informative and very useful, interactive course.” - Kuldeep Sohal, Bradford Institute for Health Research - Applied Data Science with R
“Course excellently laid out. Very rich content. It will provide material and food for thought for years!” - Ernesto Addiego-Guevara, Imperial College London - Applied Data Science with Python
“Simon was exceptional in conducting this course. The course covered a nice mixture of theory and practicals related to R, databases and Hadoop. It has really helped me to understand a lot of concepts and has motivated me to work on Hadoop.” - Sushant Chatufale, University of Portsmouth - Big Data Methods with R
“This is a great course, which I would recommend to anyone with an interest in data science no matter your ability.” - Clair Cooper, Durham University - Applied Data Science with R
“Great course, it covers a lot of material. Good balance between maths, theory, R and stats.” - Gordon Izatt - Machine Learning with R
“Expert tuition in Python and Machine Learning. I am now inspired and equipped to apply what I've learnt.” - John Taylor, TR Control Solutions Ltd - Machine Learning with Python
“This is a unique face-to-face course on data analysis using Python. Very rich learning experience in 3 days.” - Hamad Al Jassmi, Assistant Professor at UAE University - Applied Data Science with Python
“A great starter course. Exercises were provided so that we could practice and we could easily communicate with the course teacher.” - Iqqra Aziz, Get The Data - Applied Data Science with R
“Excellent and thorough intro to R, exceeded my expectations.” - Shahrooz Aliabadi, Lapinco Ltd - Applied Data Science with R
“Very interesting course. Really useful tutorials that progressed understanding of machine learning concepts.” - Talia Beech, Canadian Department of National Defence - Machine Learning with R
“The course provided me with a strong knowledge base to build upon.” - Tobias Koelsch, REWE Group - Machine Learning with Python
“Very informative, made me think of the potential Python holds in many fields. The trainer was excellent, he made everyone comfortable to contribute.” - Naeema Al Darmaki, UAE University - Applied Data Science with Python
“Excellent course. Very well structured and very professional. Simon catered for all abilities. Everyone gained a lot of information. Value for money.” - Dr. Noreen Quinn, Lecturer in Statistics, Cork Institute of Technology - Machine Learning with R
“Excellent course, a lot to cover and take back. Perfectly suited to analysts improving their skills in Python.” - Ben Amos, Argos - Applied Data Science with Python
"The course is very informative and Simon has been a great tutor. The ML steps he taught us is quite clear from setting up the python environment, running the scripts and analysis. He taught us new techniques in ML and how to do it properly." - Farraen Mohd Azmin Senior Project Engineer: Machine Learning Specialist - Machine Learning with Python
"Extremely useful introduction course to ML. It's really great to have the basics so clearly laid out, will definitely make use of the learnings and material in the future!" - Siem van Limpt, Senior Consultant, Element Energy - Machine Learning with Python
"The course provided an incredible overview of regression, clustering and classification methods. The amount of material we covered in three days was substantial, but Simon kept it clear, and the homeworks were a really helpful opportunity to bed in learning" - Harry Lloyd, Marine Data Scientist - Machine Learning with Python

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Time Series Analysis with Python