Online Course
Advanced Time Series Analysis with Python

Name: Time Series Forecasting with Python Course: Statistical, ML & Deep Learning Models
Price: 36 USD

Take your time series forecasting skills to the next level by learning how to apply advanced statistical methods, machine learning, and deep learning to real-world forecasting challenges using Python.

4.9

808 reviews on

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123 students already enrolled

Skill level:

Advanced

Duration:

6 hours

Lessons (6 hours)

CPE credits:

6

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Dynamic Regression
Generalised Additive Models
XGBoost
Recurrent Neural Networks
Long Short Term Memory
Gated Recurrent Unit
Vector Autoregression
Prophet
Vector Error Corrected Model

Topics & tools

Time SeriesForecastingMachine and Deep LearningMath & StatisticsData AnalysisProgrammingPythonTheory

Your instructor

Egor Howell

Machine Learning Engineer @ Deliveroo/DoorDash

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Physics

Ask Egor a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 6 Field of Study: Statistics
Delivery Method: QAS Self Study

This course builds on your foundational time series knowledge to help you explore more advanced concepts and models in the field.

You’ll gain hands-on experience with techniques such as:

Generalised Additive Models and Splines
Dynamic Harmonic Regression
Vector Error Correction Models
Recurrent Neural Networks
Gradient Boosted Trees

By the end of the course, you’ll have a strong understanding of complex forecasting models and the practical insight to know when and how to apply them in real-world scenarios.

Prerequisites

Basic understanding of Python
Fundamentals of time series forecasting
Basic understanding of machine learning
Familiarity with the PyData stack: Numpy, Pandas, Plotly, Sci-Kit Learn

Advanced preparation

None

Curriculum

37 lessons 43 exercises 1 exam

1. Intro

6 min

6 min

Welcome! Free

Pre-Requisites Free
2. Coding Environment

7 min

7 min

Installing Git and Github Free

Installing Anaconda and Jupyter Notebook Free

Installing Course Notebooks Free
3. Dynamic Models

55 min

55 min

Section Intro

Dynamic Regression

Exercise

Dynamic Regression Python Tutorial

Dynamic Harmonic Regression

Exercise

Dynamic Harmonic Regression Python Tutorial

Exercise
4. Advanced Statistical Models

106 min

106 min

Section Intro

Non-Linear Regression and Generalised Additive Models

Exercise

Non-Linear Regression Python Tutorial

Prophet

Exercise

Prophet Python Tutorial

Vector Autoregression

Exercise

Vector Autoregression Python Tutorial

Vector Error Correction Model

Exercise

Vector Error Correction Model Python Tutorial

Exercise
5. Machine Learning Models

109 min

109 min

Section Intro

K-Nearest Neighbour & Dynamic Time Warping

Exercise

K-Nearest Neighbour Python Tutorial

Tree Based Models

Exercise

Tree Based Models Python Tutorial

Neural Network

Exercise

Neural Network Python Tutorial

Exercise
6. Deep Learning

85 min

85 min

Section Intro

Recurrent Neural Network

Recurrent Neural Network Python Tutorial

Long-Short Term Memory

LSTM Python Tutorial

Gated Recurrent Unit

GRU Python Tutorial

Exercise
7. Further Notes

25 min

25 min

Section Intro

Approaching Forecasting Problems

Statistical vs Deep Learning Models

Taxonomy of Forecasting Models
8. Course exam

60 min

60 min

Course exam

Free lessons

1.1 Welcome!

4 min

1.2 Pre-Requisites

2 min

2.1 Installing Git and Github

1 min

2.3 Installing Course Notebooks

5 min

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365 Data Science has been an invaluable resource for refreshing my skills and expanding my expertise. The well-structured courses in Python, Machine Learning, and SQL helped me brush up on fundamentals while learning advanced ETL and data visualization techniques. This hands-on approach has directly benefited my work—improving pipelines and creating more insightful analyses. For busy professionals, 365 offers a solid foundation and a practical refresher to stay ahead in the evolving world of data analytics.

Shruti P.

After 365: Sr. Business Intelligence Engineer | Child Care Resource Center (USA)

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9 in 10

of our graduates landed a new AI & data job

after enrollment

I was kinda lost with all the data stuff, but 365 Data Science really made it click. The structured learning path, clear explanations, and especially the hands-on projects were game-changers. I didn’t just learn theory—I actually applied it and built a portfolio that helped me land a data analyst job. Now I use Python, SQL, Excel, and data viz skills every day. I’m way more confident. Before I was guessing—now I know what I’m doing.

Siriwat N.

Before 365: Engineering student

After 365: Data Analyst & Process Improvement | CJ MORE

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9 in 10

people walk away career-ready

with practical data and AI skills.

The courses at 365 Data Science are engaging, well-structured, and strike the right balance between theory and application. The end-of-course exams stand out—they’re challenging enough to ensure you’ve truly mastered the material, making the certification well-earned. For me, it was also a great refresher that helped solidify my past knowledge. If you want a comprehensive, hands-on platform that truly tests and strengthens your skills, I highly recommend 365 Data Science.