What you'll get
  • 4+ Hours
  • 1 Courses
  • Course Completion Certificates
  • Self-paced Courses
  • Technical Support
  • Case Studies

Synopsis

  • Key methods and steps in forecasting
  • Common problems in forecasting and how to address them
  • Simple and multiple regression techniques for prediction
  • Time series decomposition for trend and seasonality analysis
  • Exponential smoothing methods for short-term forecasting
  • ARIMA models for advanced time series forecasting

Content

Courses No. of Hours Certificates Details
Business Analytics - Forecasting using R4h 34m✔View Curriculum

Description

This course offers a practical, easy-to-follow introduction to business analytics and forecasting using R and Excel. It prepares beginners, data science learners, and business analysts to use real-world techniques to analyze data and answer important business questions.

Course Highlights

  • Understand the fundamentals and importance of forecasting in business analytics.

  • Explore simple and advanced forecasting techniques, including simple/multiple regression, exponential smoothing, and ARIMA models.

  • Learn time series decomposition to identify trends, seasonality, and residuals.

  • Work with time-series objects using the ts() function and R's powerful built-in packages to model and forecast data.

  • Identify common forecasting challenges and learn strategies to overcome them.

  • Follow a structured workflow from data preparation to model fitting and evaluation.

Time series data—such as sales, weather, or stock prices—are collected over regular intervals. This course teaches you how to transform raw data into a time-series format, apply appropriate forecasting models, and extract actionable insights. By the end of this training, you will be able to analyze, model, and forecast time-dependent data with confidence, just like professional data analysts.

Requirements

  • Basic knowledge of statistics, mathematics, and programming

  • Basic familiarity with R and Excel

  • Ability to work with datasets 

  • Comfort with logical thinking and problem-solving

  • Willingness to learn quantitative and analytical concepts.

Target Audience

  • Students

  • Marketing professionals

  • Market researchers

  • Product managers

  • Entrepreneurs and small business owners

  • Anyone running or promoting a business.