Data Science Fundamentals

Machine Learning



Course details :

This 3-day workshop is a careful combination of statistical theory, hands-on coding and programming exercises to help students understand — and implement — some of the most widely used, and fundamental, machine learning algorithms.

By building regressors and classifier algorithms from scratch, the student will go beyond applying machine learning models to actually developing their own models — and learn the right approach to fine-tuning the model performance as well as evaluating model fit against unseen data. Upon completion of the workshop, the student will be well versed in an array of important, versatile machine learning algorithms and equipped with the right knowledge to apply them to future datasets in their daily job.

Data are becoming the new raw material of business ~ Craig Mundie, Microsoft

Please bring along:

  • 1x Laptop
  • Purchased ticket (from organizer’s website)


  • Data Science Toolkit

    Day 1

  • Machine Learning Algorithms

    Day 1

  • Evaluating and Optimizing Model Performance

    Day 2

  • Interpreting Regression Models

    Day 2

  • Classification: Logistic Regression, k-NN

    Day 3

  • Decision Tree, Random Forest

    Day 3

Event Ended
Explore other data science workshops


Viska Aprillia


Detailed Syllabus

Syllabus: Data Science Fundamentals Machine Learning

Data Science Explained

  • Description of course materials and the learning environment
  • A comprehensive view on the roles of data science, the relating professions, career prospects and outlook.
  • Description of the workflow, tools, setup and programming languages in the course

R Programming Basics

  • Setting up the Workspace and Environment
  • Working with data types: scalar, vector, list, matrix, data frame
  • R’s built-in functions
  • Inspecting data using built-in functions
  • R’s plotting capabilities
  • R Markdown and reproducible research

Statistics Fundamental

  • Demonstrate the use of various statistics in exploratory data analysis: 5-number summary, mean, mode, interquartile range, variance, standard deviation and correlation
  • Plots: scatterplots, scatterplot matrices, line graphs, histogram, ab-line, x and y-axis styling, plot title, tips and tricks for plotting in R
  • Quick way to get a “sense” of the distribution of our dataset
  • Confidence intervals and Hypothesis Testing

Machine Learning Fundamental

  • Prediction with linear models
  • Precision and Recall
  • Prediction on unseen data

Data Wrangling

  • Continuous variables and Categorical variables
  • Factors and levels
  • Description of the workflow, tools, setup and programming languages in the course
  • Reading from different data formats: CSVs, JSON, webpages, API
  • Various data preprocessing and data cleansing techniques

Linear Regression

  • Code examples of linear regression
  • Statistical principles behind least squares regression
  • Linearity assumption
  • Dependent and Independent variables
  • Inspecting data using built-in functions
  • R-squared
  • Interpreting coefficients

Improving Model’s Performance

  • Limitations of common machine learning techniques
  • Preventing overfitting
  • Bias-Variance Tradeoff
  • k-fold Cross Validation

Multivariate Regression

  • Interaction term
  • Confounding variables
  • Measures of fit

Classification in Machine Learning

  • k Nearest Neighbors and distance function
  • Logistic Regression and the sigmoid curve
  • Decision Tree
  • Random Forest
  • Bootstrap Aggregation and Boosting
  • Multiclass classification
  • Evaluating model’s performance

Building a classification algorithm

  • Finding datasets
  • Feature engineering
  • Training on unseen data

This workshop will cost 3 workshop credits for subscribers. Non-subscribers are welcomed to participate at a cost of IDR 3,000,000.

Workshop Receivables:

  • Workshop Lecturer’s Notes

    Including 2x Course Books (PDF), HTML files, course transcripts (if any).

  • Highly-accelerated Learning

    Learn under the assistance of mentorship of our lead instructor and a band of qualified teaching assistants throughout the 3 day course.

  • Certification of Completion

    Show current and prospective employers that you’ve completed the course with a signed certificate of completion.

  • Quality Learning Environment

    We pay meticulous attention to the logistical details of our workshops: quality audio and visual setups, comfortable sitting arrangements, small group size. Dinners are included for evening workshops.

  • Supplement Materials

    Receive supplement datasets to practice on, reference notes, working files (R Notebook or Jupyter Notebook), and other materials that will help you master the topics.

Data Science Fundamentals Series

Workshops in our Data Science Fundamentals series are tailored to casual learners, working professionals and non-programmers that are taking their first steps into data science and machine learning.

Students are not assumed to have a working knowledge of R or prior proficiency in statistics / mathematics / algebra. At such the workshop follows a gentle learning curve and emphasize on hands-on, one-to-one tutoring from our team of instructors and teaching assistants.

Consider taking our Data Science Intermediate workshops instead for more advanced-level materials in statistical programming and machine learning.

Past Workshops in this Series:

Students work through tons of real-life examples using sample datasets donated by our team of mentors and corporate partners. We believe in a learn-by-building approach, and we employ instructors who are uncompromisingly passionate about your growth and education.