Time Series Forecasting Toolkit: Excel, R, Python and EViews

This course provides comprehensive training in regression analysis and forecasting techniques for data science, emphasizing Python programming. You will master time-series analysis, forecasting, linear regression, and data preprocessing, enabling you to make data-driven decisions across industries. Learning Objectives: • Develop expertise in time series analysis, forecasting, and linear regression. • Gain proficiency in Python programming for data analysis and modeling. • Analyze the techniques for exploratory data analysis, trend identification, and seasonality handling. • Figure out various time-series models and implement them using Python. • Prepare and preprocess data for accurate linear regression modeling. • Predict and interpret linear regression models for informed decision-making. There are Four Modules in this Course: Module 1: Time-Series Analysis and Forecasting Module description: The Time-Series Analysis and Forecasting module provides a comprehensive exploration of techniques to extract insights and predict trends from sequential data. You will master fundamental concepts such as trend identification, seasonality, and model selection. With hands-on experience in leading software, they will learn to build, validate, and interpret forecasting models. By delving into real-world case studies and ethical considerations, participants will be equipped to make strategic decisions across industries using the power of time-series analysis. This module is a valuable asset for professionals seeking to harness the potential of temporal data. You will develop expertise in time series analysis and forecasting. Discover techniques for exploratory data analysis, time series decomposition, trend analysis, and handling seasonality. Acquire the skill to differentiate between different types of patterns and understand their implications in forecasting. Module 2: Time-Series Models Module description: Time-series models are powerful tools designed to uncover patterns and predict future trends within sequential data. By analyzing historical patterns, trends, and seasonal variations, these models provide insights into data behavior over time. Utilizing methods like ARIMA, exponential smoothing, and state-space models, they enable accurate forecasting, empowering decision-makers across various fields to make informed choices based on data-driven predictions. You will acquire the ability to build forecasting models for future predictions based on historical data. Discover various forecasting methods, such as ARIMA models and seasonal forecasting techniques, and implement them using Python programming. Develop the ability to formulate customized time-series forecasting strategies based on data characteristics. Module 3: Linear Regression - Data Preprocessing Module description: The Linear Regression - Data Preprocessing module is a fundamental course that equips participants with essential skills for preparing and optimizing data before applying linear regression techniques. Through hands-on learning, participants will understand the importance of data quality, addressing missing values, outlier detection, and feature scaling. You will learn how to transform raw data into a clean, normalized format by delving into real-world datasets, ensuring accurate and reliable linear regression model outcomes. This module is crucial to building strong foundational knowledge in predictive modeling and data analysis. You will gain insights into various regression techniques such as linear regression, polynomial regression, and logistic regression, and their implementation using Python programming. Identify missing data and outliers within datasets and implement appropriate strategies to handle them effectively. Recognize the significance of feature scaling and selection and learn how to apply techniques such as standardization and normalization to improve model convergence and interpretability. Module 4: Linear Regression - Model Creation Module description: The Linear Regression - Model Creation module offers a comprehensive understanding of building predictive models through linear regression techniques. You will learn to choose and engineer relevant features, apply regression algorithms, and interpret model coefficients. By exploring real-world case studies, you will gain insights into model performance evaluation and acquire how to fine-tune parameters for optimal results. This module empowers you to create robust linear regression models for data-driven decision-making in diverse fields. You will understand how to identify and select relevant features from datasets for inclusion in linear regression models. Acquire the skills to interpret model coefficients, recognize their significance, and deliver the implications of these coefficients to non-technical stakeholders. Discover how to fine-tune model parameters, and regularization techniques, and perform cross-validation to enhance model generalization. Target Learner: This course is designed for aspiring data scientists, analysts, and professionals seeking to enhance their skills in regression analysis, forecasting, and Python programming. It is suitable for those looking to harness the power of temporal data and predictive modeling in their careers. Learner Prerequisites: • Basic knowledge of Python programming. • Familiarity with fundamental data analysis concepts. • Understanding statistical concepts is beneficial but not mandatory. Reference Files: You will have access to code files in the Resources section and lab files in the Lab Manager section. Course Duration: 5 hours 44 minutes Total Duration: Approximately 4 weeks • Module 1: Time-Series Analysis and Forecasting (1 week) • Module 2: Time-Series Models (1 week) • Module 3: Linear Regression - Data Preprocessing (1 week) • Module 4: Linear Regression - Model Creation (1 week)

Build practical skills in univariate time series analysis by learning how to apply and evaluate ARMA (AutoRegressive Moving Average) models using EViews. This course is designed for learners with foundational statistical knowledge who want to develop reliable time series models through hands-on analysis and model diagnostics. You will begin by exploring the fundamentals of univariate time series modeling, including the interpretation of correlograms, autocorrelation, and partial autocorrelation using real-world data in EViews. As you progress, you will learn how to estimate ARMA models, interpret estimation outputs, evaluate parameter significance, and assess model performance using residual analysis, correlograms, and the Ljung-Box Q test. Through practical demonstrations, exercises, and quizzes, you will strengthen your ability to identify suitable model structures, validate model adequacy, and refine models using statistical evidence. By the end of the course, you will be able to construct, interpret, and evaluate univariate ARMA models in EViews for forecasting and analytical applications, building a solid foundation in time series modeling.

Master the principles and practice of time series forecasting with R and build the skills to analyze historical data and generate reliable predictions. This course takes you through a structured learning journey, beginning with the fundamentals of forecasting in business analytics before progressing to regression, decomposition, and advanced forecasting models. You will learn how to distinguish between qualitative and quantitative forecasting methods, apply simple forecasting techniques, evaluate forecast accuracy, and address common forecasting challenges. As you advance, you will use simple, multiple, and non-linear regression, incorporate predictor and lagged variables, and decompose time series into trend, seasonal, cyclical, and irregular components to create more interpretable forecasting models. The course concludes with advanced techniques, including exponential smoothing, ARIMA, and Seasonal ARIMA (SARIMA), while using ACF and PACF diagnostics to support effective model selection and implementation in R. Designed for learners who want to strengthen their forecasting capabilities, this course combines foundational concepts with practical implementation in R through a logical, step-by-step progression. By the end of the course, you will be able to select appropriate forecasting methods, build and evaluate time series forecasting models, and develop accurate forecasting solutions that support data-driven decision-making across a wide range of business applications.