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PT. Algoritma Data Indonesia.
RDTX Square, 9th Floor. Setiabudi, DKI Jakarta 12930.
Office: 0816-692-471
Email: community@algorit.ma
Courses under the Weekend Track
Machine Learning: Classification 1
The science of solving classification tasks
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 11 – 14 January 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 11 – 14 January 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Course details :
Learn to solve binary and multi-class classification models using machine learning algorithms that are easily understood and readily interpretable. You will learn to write a classification algorithm from scratch, and appreciate the mathematical foundations underpinning logistic regressions and nearest neighbors algorithms.
We strongly recommend that you complete the Regression Models workshop prior to taking this course. Upon completion of this workshop, you will acquire the depth to develop, apply, and evaluate two highly versatile algorithms widely used today.
Schedule
Course Producer
Samuel Chan
An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.
Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.
Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.
4-Day Workshop Modules
Syllabus: Classification in Machine Learning 1
Module 1: Logistic Regression
Relating Probabilities to Odds
- Understanding Odds
- Understanding Log of Odds
- Plotting Odds and Log of Odds
Logistic Regression from First Principles
- Sigmoidal Logistic Function
- Key Assumptions of Sigmoid Function
- Extra Proof: Intuition Behind The
- Sigmoid Function
Logistic Regression in Action
- Binary Logistic Regression
- Interpreting Coefficients
- Interpretation Against Continuous & Discrete Variables
Practical Tips and Case Study
- Flight Delay Prediction Examples
- Customer Churn and Attrition Examples
- Risk Modeling on Loans from Quarter 4, 2017
Performance Evaluation and Model Selection
- AIC (Akaike Information Criteria)
- Null Deviance and Residual Deviance
- Hauck Donner Effect
Module 2: Nearest Neighbours
Algorithm
Closer Look at Classification
- Probabilities vs Class responses
- Cross Validation and Out-of sample error
- Bias-variance trade off
- Confusion matrix (accuracy, sensitivity, specificity, & precision)
k-NN in Action
- Characteristics of k-NN
- Positives and Negatives
- Diagnosing Breast Cancer with k-NN
Building Blocks of k-NN
- Distance Function (Euclidean, Minkowsky)
- The k Parameter
- Standardization vs Min-Max Normalization
k-NN from First Principles
- Classifying Customer Segments with k-NN
- Writing Your Own k-NN Classifier
- Predicting Using Your Own k-NN Classifier
Academy Modules
Graded Quiz
Learning-by-Building Module (3 Points)
Logistic Regression on Credit Risk
- Applying what you’ve learned, present a simple R Markdown document in which you demonstrate the use of logistic regression on the lbb_loans.csv dataset. Explain your findings wherever necessary and show the necessary data preparation steps. To help you through the exercise, consider the following questions throughout the document:
- How do we correctly interpret the negative coefficients obtained from your logistic regression?
- How do we know which of the variables are more statistically significant as predictors?
- What are some strategies to improve your model?
Customer Segment Prediction
- Applying what you’ve learned, present a simple R Markdown document in which you demonstrate the use of k-NN on the wholesale.csv dataset. Compare the k-NN to the logistic regression model and answer the following questions throughout the document:
- What is your accuracy? Was the logistic regression better than k-NN in terms of accuracy? (recall the lesson on obtaining an unbiased estimate of the model’s accuracy)
- Was the logistic regression better than our kNN model at explaining which of the variables are good predictors of a customer’s industry?
- List down 1 disadvantage and 1 strength of each of the approach (k-NN and logistic regression)
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 11 – 14 January 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 11 – 14 January 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Workshop Receivables:
This workshop is recommended for:
The Machine Learning: Classification 1 workshop is an intermediate-level programming workshop best suited to R programmers that are taking their first steps into data science and machine learning.
Students are assumed to have a working knowledge of R and have completed the necessary pre-requisites. Consider taking the pre-requisite course or a beginner-level course instead if you have no prior programming experience or statistics knowledge.
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.
Part of the Machine Learning Specialization
This workshop is part of the Machine Learning Specialization offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.
Regression Models
An in-depth look at regression models
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 4 – 7 January 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 4 – 7 January 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Course details :
This course strives for a fine balance between business applications and mathematical rigor in its treatment to regression models, one of the most essential statistical techniques in the field of machine learning. Its aim is to equip you with the knowledge to investigate relationships between variables of a data effectively and rigorously.
We strongly recommend that you complete Practical Statistics prior to taking this course. Upon completion of this workshop, you will acquire a rigorous statistical understanding of machine learning models, allowing you to extrapolate the same ideas into other, more advanced machine learning models.
Schedule
Course Producer
Samuel Chan
An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.
Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.
Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.
4-Day Workshop Modules
Syllabus: Regression Models
Module 1: Regression Models I
OLS Regression
- Understanding Least Squares
- Simple Linear Regression
Linear Models in R
- Understanding Coefficients
- Plotting Regression
- Model Construction
Interpreting Linear Models
- Residuals Manually
- Coefficients Manually
- R-Squared Manually
Module 2: Regression Models II
Interpreting Linear Models
- Estimates and Standard Errors
- t-Value and p-Value
- Adjusted R-Squared
Multiple Regression
- Multicollinearity and VIF
- Model Assumptions
- Bias-Variance Trade-off
- Outliers: Leverage and Influence
- Model Limitation and Evaluation
Dive Deeper: Regression Models
- Model Selection and Specification
- Step-wise Regression
- All-possible Regressions
- Residual Plots
- Model Diagnostics
- Limitations of Regression Models
Academy Modules
Graded Quiz
Learning-by-Building Module (3 Points)
Recommendation on Lowering Crime Rates
- Write a regression analysis report applying what you’ve learned in the workshop. Using the dataset provided by you, write your findings on the different socioeconomic variables most highly correlated to crime rates.Explain your recommendations where appropriate.
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 4 – 7 January 2021
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 4 – 7 January 2021
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Workshop Receivables:
This workshop is recommended for:
The Regression Models workshop is an intermediate-level programming workshop best suited to R programmers that are taking their first steps into data science and data visualization.
Students are assumed to have a working knowledge of R and have completed the necessary pre-requisites. Consider taking the pre-requisite course or a beginner-level course instead if you have no prior programming experience or statistics knowledge.
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.
Part of the Machine Learning Specialization
This workshop is part of the Machine Learning Specialization offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.
Data Visualization in R
Create stunning graphics for your Data Science projects
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 7 – 10 December 2020
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 7 – 10 December 2020
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Course details :
A fun, hands-on, and project-based workshop that help students gain full proficiency in data visualization systems and tools. Create compelling narratives by combining charting elements with custom aesthetics under the guidance of our instructors.
The 4-day course follows our learn-by-building approach, in that students are tasked to reproduce a series of plots applying what they’ve learned. While it covers the three main plotting systems in R, its particular focus is on ggplot2 and the additional libraries centered around it that brings interactivity and enhanced aesthetic options to the art of creating rich, powerful visualizations.
Schedule
Course Producer
Samuel Chan
An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.
Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.
Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.
4-Day Workshop Modules
Syllabus: Data Visualization in R
Module 1: Plotting Essentials
Base Plotting I
- Plots and Lines
- Built-in Plot Types
- Legends and Annotations
- Other Built-in Plotting Functionalities
Base Plotting II
- Histograms and Curves
- Cleveland’s Dot Plot
- Axis, Titles, Subtitles and Panel Styles
- The Notorious Pie Chart
Working with ggplot2
- Grammar of Graphics System
- Mapping Aesthetics
- Working with Geometries
- Background Image
Enhancing ggplot2
- Axis, Titles, and Scales
- Adding Themes to Your Plots
- Custom Aesthetics and Styles
- Working with Legends
Module 2: Richer Visualization
Techniques
Enhancing ggplot2 II
- Flipping Coordinates and Axis Rotation
- Multi-dimensional Faceting
- Text Layers and Label Layers
- Expected Values
Enhancing ggplot2 III
- Enriching: Scatterplots and Bubble Plots
- Enriching: Jitterplots
- Enriching: Boxplots and Violin Plots
- Layer Transparency
Enhancing ggplot2 IV
- Enriching: Column Plots
- Enriching: Texts and Labels
- Enriching: Horizontal and Vertical Lines
- Fills and Colors
Enhancing ggplot2 IV
- Discrete, Continuous, and Gradient Colors
- Facet with Wraps and Grids
- Visualizing Spatial Data
- Working with Leaflet and Maps
Academy Modules
Pproject: Mining Trending Videos on YouTube
- Hands-on Data Visualization
- Identifying Temporal Patterns in Trending Videos
- Combining Aesthetics and Geometries
Learning-by-Building Module (2 Points)
Creating a Publication-Grade Plot
- Applying what you’ve learned, create an economics- or social-related plot that is polished with the appropriate annotations, aesthetics, and some simple commentary. You may use the same “YouTube Trending Videos” dataset or any other dataset for this practice.
Creating an Interactive Map
- Applying what you’ve learned, create a web page with an interactive map embedded on it. Use a custom icon for the map markers to represent business locations, and show details about each location pin (“markers”) upon user’s interaction with it.
Ad-Hoc Course Registration:
ON-SITE CLASS
- Date: 7 – 10 December 2020
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
- Investment: Rp. 5.200.000
ONLINE CLASS
- Date: 7 – 10 December 2020
- Time: 18.30 – 21.30
- Investment: Rp. 2.600.000
Workshop Receivables:
This workshop is recommended for:
The Programming for Data Science workshop is designed for 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 Intermediate-level 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.
Part of the Data Visualization Specialization Track
This workshop is part of the Data Visualization specialization track offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.
Practical Statistics
An in-depth statistics course from a data science perspective
Ad-Hoc Course Registration:
ON-SITE CLASS
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
ONLINE CLASS
- Time: 18.30 – 21.30
- Venue: Google Classroom
Course details :
Pave the statistical foundation for more advanced machine learning theories later on in the specialization by picking up the key ideas in statistical thinking. Learn to interpret correlations, construct confidence intervals and other statistical principles that form the basis of many common machine learning models.
The 2-day course is optional for participation of the Data Visualization and Machine Learning Specialization and intended for learners without prior experience in statistics.
Course schedule:
Course Producer
Samuel Chan
An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.
Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.
Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.
2-Day Workshop Modules
Syllabus: Practical Statistics
Module 1: Descriptive Statistics
5-Number Summary
- Mean, Median, and Mode
- Measures of Central Tendency
- Quantiles in R
Central Tendency & Variability
- Visualizing Central Tendency
- Variance, and Covariance
Standard Score and z-Score
- Standard Normal Curve
- Central Limit Theorem
- z-Score Calculation & Student’s T-test
Module 2: Inferential Statistics
Probabilities
- Probability Mass Function
- Probability Density Function
- Expected Values
- p-Values
Intervals
- Confidence Intervals
- Prediction Intervals
Inferential Statistics in Practice
- Hypothesis Testing
- Deriving Scientific Truths from Data
- Case Study
Academy Modules
Tips & Techniques: R for Statisticians
- Density Plots
- Interpreting Box Plots (Box-and-Whisker)
- Better Summary Statistics with skimr()
Learning-by-Building Module (Not Graded)
Statistical Treatment of Retail Dataset
- Using what you’ve learned, formulate a question and derive a statistical hypothesis test to answer the question. You have to demonstrate that you’re able to make decisions using data in a scientific manner.
Examples of questions can be:
- Is there a difference in profitability between standard shipment and same-day shipment?
- Supposed there is no difference in profitability between the different product segment, what is the probability that we obtain the current observation due to pure chance alone?
Workshop Receivables:
This workshop is recommended for:
The Programming for Data Science workshop is designed for 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 Intermediate-level 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.
Part of the Data Visualization and Machine Learning Specialization Track
This workshop is part of the two specialization tracks offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.
Programming for Data Science
R programming for the modern-day data scientist
Ad-Hoc Course Registration:
ON-SITE CLASS
- Time: 18.30 – 21.30
- Venue: Menara Kadin Lantai 4, Jl. H. Rasuna Said, Jakarta Selatan
ONLINE CLASS
- Time: 18.30 – 21.30
- Venue: Google Classroom
Course details :
Programming for Data Science is a course that covers the important programming paradigms and tools used by data analysts and data scientists today. You will be guided through a series of coding exercises designed to maximize your familiarity with data science programming in RStudio, an integrated development environment for the statistical computing language R.
Upon completion of this workshop, you will be familiar with the programming language, popular tools, libraries (data science packages) and toolkits required to excel in your data analysis and statistical computing projects.
Schedule
Course Producer
Samuel Chan
An RStudio-certified instructor and machine learning practitioner in the field of marketing automation, fraud detection, finance and e-commerce. Samuel is Indonesia’s top-ranked Stack Overflow user in R (top 5% worldwide) for three years running, and boasts certifications from RStudio, Microsoft, MongoDB, Neo4J Database, Stanford University, John Hopkins University, among others.
Prior to Algoritma, he has 8 years of working experience, including a stint as in-house consultant to several public-trading companies from his time staying in China, Japan and Singapore. He is today an active trainer and consultant for various companies in the financial industry. He has guest lectured in various campuses: Binus, NUS (National University of Singapore)’s The Logistics Institute, University of Indonesia, Universitas Gadjah Mada (UGM), Binus, Institute of Technology Bandung (ITB), Telkom University etc. Courses he authored are offered also in Singapore through Ngee Ann Polytechnic.
Samuel is also among the first recipients of Microsoft Professional Program Certificate in Data Science in Southeast Asia, having demonstrated proficiency in R, Python, Microsoft Azure, SQL / T-SQL, PowerBI and a list of other technologies, and among the first to be certified in RStudio’s program. Technical committee member and competition judge on Finhacks 2018, the largest Machine Learning competition of the year organized by PT. Bank Central Asia (BCA) and DailySocial.
3-Day Workshop Modules
Module 1: Data Science in R
Data Science in R
- R Programming Basics
- Why Learn R?
- R Studio Interface
- Data Structures in R
Working with Data
- Reading & Extracting Data
- Understanding Statistics
- Exploratory Data Analytics
Data Manipulation
- Working with Your Global Environment
- Getting Familiar with Your Workspace
- Continuous and Categorical Data
Module 2: Data Manipulation
Data Manipulation II
- Vector Types and Classes
- List and Objects
- Matrix and Data Frames
Practical Data Cleansing
- The Data Transformation Process
- Reproducible Data Science Projects
- Reading and Writing from Your IDE
R in Practice
- Programming Exercise: e-Commerce Retail Datasets
- In-depth Review of Data Frame Subsetting
- Sampling and Randomization
- Cross-Tabulations
- Aggregations
Academy Modules
Graded Quiz
Working with R
- R Scripts and Functions
- R Markdown
- Why Care About Reproducibility
Learning-by-Building Module (2 Points)
Writing your code as R scripts make up for automation and integration with other tools and services, while writing a R Markdown presents your findings and recommendations in a way that is friendly to non-technical / managerial team members.
- R Script to clean & transform the data
Write a R script containing a function (name the function however way you want) that reads a dataset as input, perform the necessary transformation and export a cross-tabulation numeric result or plot as output.
- Reproducible Data Science
Create an R Markdown file that combines your step-by-step data transformation code with some explanatory text. Add formatting styles and hierarchical structure using Markdown.
Workshop Receivables:
THIS WORKSHOP IS RECOMMENDED FOR:
The Programming for Data Science workshop is designed for 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 Intermediate-level 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.
Part of the Data Visualization and Machine Learning Specialization Track
This workshop is part of the two specialization tracks offered by Algoritma Data Science Academy. Participants are rewarded with a certificate of completion upon passing criteria, and are encouraged to advance further in the respective data science specialization.