Time Series Analysis for Business Forecasting

By performing a time series analysis on your historical business data and comparing it with current trends, you will be able to make a more informed decision.

  • Schedule

    17 – 19 May 2022

    18.30 – 21.30 (WIB)

  • Online-Interactive Learning

    Via Zoom

  • Investment

    Rp. 1.500.000

Course Summary

This 3-day online workshop is a beginner-friendly introduction to Time Series Analysis for Business Forecasting. By performing a time series analysis on your historical business data and comparing it with current trends, you will be able to make a more informed decision. 

Throughout the online course, we will provide participants with a rich interactive experience. One Instructor and two Teaching Assistants will help participants to troubleshoot or help with any difficulties encountered by participants.

Upon completion of this workshop, you will be able to:

  • Work with the Python language and open source packages for data cleansing and manipulation process
  • Identify and analyze underlying patterns for time series components such as trend and seasonality
  • Perform data forecasting for time series using Facebook’s prophet package in Python
  • Produce explanatory report using Jupyter notebook tools incorporating analysis results, such as: time series components, model performance, and business recommendation

NOTE: The workshop will be delivered in Bahasa Indonesia


  • Working with Conda Environment.
  • Introduction to Python for Data Science.
  • Data manipulation and processing with Python Pandas.
  • Time Series Frequency
  • Data Aggregation
  • General Additive Model
  • Extracting Trend
  • Extracting Seasonality
  • Saturating Trend and Multiple Seasonalities
  • Holiday Effect
  • Time Series Fitting and Forecasting
  • Forecasting Error
  • Prediction Interval
  • Model Components Visualization
  • Forecasting Assumption and Limitation
  • Forecasting Sales Demand
  • Analyzing the Holiday Effect Application in Real Case Data
  • Giving Production Recommendation based on Time Series Analysis


This testimonial video is taken after our previous Online Data Science Series: Time Series Analysis for Business Forecasting.


Our learning format is online-interactive, you will feel the interactive experience as if you were present in a physical classroom. You can access the class using your Zoom account on pre-defined dates.


    Zoom recording, course Books (PDF & HTML files), the dataset for practice, reference notes, and working files are accessible through our Learning Management System account.


    Show current and prospective employers of your mastery in computer vision with a signed certificate of completion.


    Be a part of our data-passionate community with 5000+ members and 1000+ alumni.


Workshops in this series are tailored to casual programmers and non-programmers that are taking their first steps into data science. It assumes no prior knowledge or academic background, and attendees will be introduced to the beautiful art of writing R / Python code to produce data visualization and build machine learning models. The workshop has a gentle learning slope that is designed with non-technical professionals and academics in mind.

Yes, you can still attend the workshop as it is a beginner-friendly workshop.

Our system will send you an email containing a link and details to join a Google Classroom.

Online learning will be conducted via Zoom.us, Link to join the Zoom Class will be announced via Google Classroom.

Learning materials can be obtain via Google Classroom

Yes, you will receive a certificate of completion.


Dyah Nurlita - Sr. Data Science Instructor

Dyah Nurlita

A Sr. Data Science Instructor at Algoritma Data Science School. She is a passionate Instructor with expertise in Data Visualization and Machine Learning. Dyah Nurlita has been involved in numerous mentoring, data science projects, and consultative data science training for our clients, to name a few:

  • Citibank
  • Bank Rakyat Indonesia
  • Inalum
  • Bank Central Asia
  • Indosat
  • Tokopedia