Learn how to make a better future analysis and machine learning model for prediction.
In data science workflows, a pre-processing task is required which includes data cleaning to ensure data quality before the analysis process. The problem of missing value is quite common in many real-life datasets. Missing value can bias the results of the machine learning models and/or reduce the accuracy of the model. Missing value imputation (MVI) is the solution method most commonly used to deal with the incomplete dataset problem.
In general, MVI is a process in which some statistical or machine learning techniques are used to replace the missing data with substituted values. However, the main limitation of using statistical imputation with measures of central tendency is that it leads to biased estimates of variance and covariance. Therefore, techniques with machine learning emerged as another alternative to overcome the weaknesses of statistical methods. One of the machine learning algorithms in the imputation technique is MICE.
This 3-day online workshop is a beginner-friendly introduction to Missing Value Imputation Using Multiple Imputation by Chained Equations (MICE). By performing a more accurate missing value imputation on your data, you will be able to make a better future analysis or even machine learning model for prediction.
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:
Python Programming Basics
Multiple Imputation by Chained Equations (MICE)
Case Study: Missing Value Imputation on Obesity Level Dataset
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.
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.
If I don’t have any IT or programming skills, can I still attend this workshop?
Yes, you can still attend the workshop as it is a beginner-friendly workshop.
How to join the interactive-online learning class after I’ve done the payment & registration?
Our system will send you an email containing a link and details to join a Google Classroom.
What platform will be utilized for this online-interactive learning workshop?
Online learning will be conducted via Zoom.us, Link to join the Zoom Class will be announced via Google Classroom.
How will the participants receive the learning materials?
Learning materials can be obtain via Google Classroom
Would I receive a certificate after participating in the Workshop?
Yes, you will receive a certificate of completion.
Cut Amalia Saffiera graduated with a Master’s degree from International Islamic University Malaysia majoring in Computer Science. Previously she completed her studies with a Bachelor’s degree from the University of Sumatera Utara in Computer Science. Her study focuses on Data Science and Machine Learning.
As Data Science Instructor, she has been involved in numerous mentoring, data science projects, and consultative data science training for our clients, for example, People Analytics using Python: Python for Data Analysis for PLN (Perusahaan Listrik Negara), and many more.
PT. Algoritma Data Indonesia.
RDTX Square, 9th Floor. Setiabudi, DKI Jakarta 12930.
Office: 0816-692-471
Email: community@algorit.ma
Online DSS: Time Series Forecasting with Pytorch