LIVE TRAINING: July 13th
10 AM EST
Price: $189
Regular price $210 , discounted 10%
4 hour immersive session
Hands-on training with Q&A
Recording available on-demand
Certification of Completion
10% Discount Ends in:
Subscribe and get an additional 10% to 35% off ALL live training session
Meet Your Instructor
Marta Markiewicz
Currently Senior (Big) Data Scientist at InPost and Lecturer at Wroclaw University of Economics and Business, previously Head of Data Science at Objectivity, with background in Mathematical Statistics. For almost 10 years, she has been discovering the potential of data in various business domains, from medical data, through retail, HR, finance, aviation, real estate, logistics, … She deeply believes in the power of data in every area of life. Articles’ writer, conference speaker and privately – passionate dancer and hand-made jewellery creator.
Course Overview
Despite being not the youngest branch of data analysis, time series forecasting still poses a great challenge to both researchers and practitioners. As Niels Bohr said years ago “Prediction is very difficult, especially when it’s about the future”.
Fortunately, plurality of approaches have been proposed to address this commonly appearing challenge. This course introduces the users to the most prominent and widely used solutions, explaining their advantages and disadvantages together with tips and recommendations on the suited-for-purpose model usage.
To facilitate rapid transition of time series theory into actual business applications that students may encounter and profit from in real life, the course is equipped with hands-on code run-throughs provided in python. At the end of the course, prediction will for sure be less difficult.
Why Enroll?
By the end of the course, participants will be able to:
Understand the essential theory of both basic and advanced time series models
Build production-ready time series forecasts with python libraries
Interpret the output of time series models to transform them into business insights
Build pipelines and GridSearch over NLP hyperparameters
Course Outline
Module 1: Time Series Introduction
- Course agenda
- Time series definition
- Real life examples
- Example in python
Module 2: Exponential smoothing
- Single exponential smoothing
- Holt’s linear trend model
- Holt-Winters exponential smoothing
- Example in python
Module 3: (S)AR(I)MA(X)
- AR
- MA
- ARIMA
- SARIMA
- SARIMAX
- Example in python
Module 4: (Linear) regression
- Linear regression
- SVR
- Trees: Random Forests and XGBoost
- Example in python
Module 5: Neural Networks
- Artificial Neural Networks
- Recurrent Neural Networks
- LSTM
- TCN
- Example in python
Module 6: Prophet
- Prophet
- Example in python
Module 7: Performance evaluation techniques
- Time series split vs cross validation
- Example in python
Module 8: Tricks that improve model performance
- Outliers types and removal
- Fourier series
- Hierarchical reconciliation
- Time reconciliation
Module 9: Course wrap-up
- Summary of covered methods and libraries
Real-world use-cases
Time series forecasting is used by various companies to predict the revenu – either globally or for certain branches / regions. Accurate revenue forecasting supports tough decision making and makes the planning easier.
Based on historical sales and additional phenomena like promotions or the current market situation, it’s possible to accurately predict short or long-term sales, estimate impact of promotion and check what-ifs scenarios.
Time series supports HR with the ability to forecast and as a bonus – to better understand staff turnover. It helps the company to prepare for, or even prevent, employees leaving. As a consequence, the cost associated with the necessity of recruiting new staff as well as onboarding is decreasing.
Key Details
DATE
TIME:
DURATION:
LEVEL:
JULY 13TH, 2021
TIME: 10 AM EST, 7 AM PST
4 HOURS
INTERMEDIATE
Prerequisites
Some experience with machine learning would make this workshop easier to follow, but is by no means necessary.
All code demos during the training will be in Python, so experience with it or another similar programming language would be helpful.
Who should attend?
Software developers, data scientists, analysts, statisticians and other data-related professionals are the core target audience for this training. This training is for anyone who would like to create best possible forecasts for real life time series, regardless of business domain.
Upcoming Live Training
July 20th
Reinforcement Learning for Game Playing and More
In recent years, there has been increased interest in the field of deep reinforcement learning (DRL), fuelled mainly by its performance in Atari Games and the win of AlphaGo over Mr. Lee Sedol, a Dan 9 Go player. In this interactive training, you will be introduced to some of the most popular and successful DRL algorithms. We will start with an introduction to different learning paradigms and how DRL differs from them. We will introduce the OpenAI reinforcement learning environment and learn how to use the OpenAI Gym to design your (custom) environments. We will cover the Deep Q Network and use it to solve a discrete action space environment. Policy gradient methods will also be explored with a special emphasis on continuous action space and multi-agent environment. Finally, we will cover the pros and cons of different algorithms and proposed variations in them. The training session will include a hands-on session where you will build an RL agent for playing Atari. Additionally, we will cover how to use RL agents in other applications like robotics, the financial sector, etc.