Overview

This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. Dive into the dynamic world of time series forecasting with this comprehensive and hands-on Python course. You’ll gain practical skills in data manipulation, visualization, and forecasting techniques—empowering you to uncover trends, identify patterns, and make predictions using real-world datasets. Whether you're preparing stock forecasts or tracking public health trends, you'll be equipped to apply advanced forecasting tools effectively. Your journey begins with the fundamentals of time series data and gradually builds through essential processing techniques, including decomposition, noise reduction, and feature engineering. As the course progresses, you’ll explore powerful statistical models such as ARIMA and SARIMA before moving into deep learning-based forecasting using LSTM, BiLSTM, and GRU models. Hands-on projects like COVID-19 case prediction, Microsoft stock forecasting, and birth rate trend analysis reinforce theoretical knowledge and provide you with ready-to-use code and workflows. Quizzes and real datasets at every step ensure a fully immersive learning experience. This course is ideal for data enthusiasts, analysts, and aspiring machine learning engineers. A basic understanding of Python programming and fundamental statistics is recommended. The course is best suited for learners at an intermediate level.

Syllabus

Introduction

In this module, we will introduce you to the fundamental concepts of time series forecasting, the course structure, and how each section will build towards a comprehensive understanding of this field. You will also be introduced to your instructor and get an overview of what to expect by the end of this course.

Motivation and Overview of Time Series Analysis

In this module, we will dive deep into the different aspects of time series data, covering its features, types, and the stages involved in forecasting. You will also learn about the integration of machine learning and neural networks, such as RNNs, in time series prediction.

Basics of Data Manipulation in Time Series

In this module, we will focus on the essential skills needed to manipulate and visualize time series data using Python. You will learn how to slice, index, and visualize both single and multiple features to better understand time series datasets.

Data Processing for Timeseries Forecasting

In this module, we will cover key data processing tasks required to prepare your dataset for forecasting. You will work through stationarity checks, noise reduction, and resampling, all essential steps for building a reliable forecasting model.

Machine Learning in Time Series Forecasting

In this module, we will introduce machine learning approaches for time series forecasting, including ARIMA and SARIMA models. You will learn to implement these techniques using Python and assess their effectiveness through evaluation metrics.

Recurrent Neural Networks in Time Series Forecasting

In this module, we will focus on Recurrent Neural Networks (RNNs), specifically LSTM and BiLSTM models, for time series forecasting. You will explore how these deep learning models are applied and optimized for accurate predictions.

Project 1: COVID-19 Positive Cases Prediction Using Machine Learning Algorithm

In this module, we will guide you through a hands-on project predicting COVID-19 positive cases using machine learning algorithms. You will process and analyze the dataset, followed by the implementation of ARIMA and SARIMA models for future predictions.

Project 2: Microsoft Corporation Stock Prediction Using RNNs

In this project, we will focus on predicting Microsoft Corporation's stock prices using RNN models. You will learn how to preprocess the dataset, visualize data patterns, and use LSTM and BiLSTM models for stock price forecasting.

Project 3: Birth Rate Forecasting Using RNNs with Advanced Data Analysis

In this project, you will use deep learning techniques to forecast birth rates over time. You will analyze and manipulate the dataset, then apply advanced RNN models like LSTM and BiLSTM to predict future birth rate trends.