The mission of the International Society of Data Scientists is to set the standards for professionals in the field of Data Science and Artificial Intelligence via our system of professional exams and certifications. The Professional Exam Committee and ISODS Institute of Data Science & AI create and uphold these standards. We offers the following professional exams with certificates:
Probability (P) (multiple-choice exam)
Basic probability concepts; Discrete and continuous univariate random variables (including binomial, negative binomial, geometric, hypergeometric, Poisson, uniform, exponential, gamma, normal, and mixed) and their applications. Multivariate random variables and their applications.
Statistics (STAT) (multiple-choice exam)
Discrete and continuous random variables, exponential family, joint and marginal and conditional distributions, order statistics, statistical inference: point estimation, confidence interval estimation, and hypothesis testing, the central limit theorem, sums of random variables, independence
Linear Algebra (LA) (multiple-choice exam)
Introduction to linear algebra with elementary applications. covers the following major topics: linear systems of equations, matrices, determinants, linear transformations, eigenvalues and eigenvectors.
Calculus (CAL) (multiple-choice exam)
Introduction to differential and integral calculus. The main topics it covers are limits, derivatives, integrals, the Fundamental Theorem of Calculus, and some basic applications of these ideas. Transcendental functions, formal integration, polar coordinates, infinite sequences and series, parametric equations.
Predictive Analytics (PA) (project-based)
Modeling language: RStudio/R or Python equivalence. Problem Analysis, Data Visualization, Data Types and Exploration, Data Issues and Resolutions, Regression, Generalized Linear Models, ANOVA, ANCOVA.
Database Management (DM) (project-based)
Structured data: Basic database design and implementation concepts. Database design techniques, including relational design and E-R analysis. Database queries using SQL.
Unstructured data: nonSQL. Semi-structured data.
Machine Learning (ML) (project-based)
Classification and Regression. Logistic Regression, Decision Trees, Ensembles, Neural Networks, SVM, Naïve Bayes, KNN, Clustering, Recommendation Systems
Big Data Analytics (BDA) (project-based)
Modeling language: Python/Java using Spark/Hadoop. RDD, DataFrames, and SparkSQL. Typical classification and regression models in the Big Data world.
Object-oriented Programming (PRG) (problems)
Modeling language: Python/Java. Object-oriented Concepts such as classes, objects, data abstraction, methods, method overloading, encapsulation, inheritance and polymorphism.
Data Structures and Algorithms (DSA) (problems)
Modeling language: Python/Java. Arrays, Strings, Stacks, Queues, Linked Lists, Hash Tables, Trees, and Graphs. Asymptotic analysis (Big-O notation). Recursion and Dynamic Programming. Greedy Algorithms. Divide and Conquer. Backtracking. Master Theorem. Sorting and Searching.
Time Series (TS) (project-based)
Modeling language: RStudio/R or Python equivalence. Time series regression and exploratory data analysis, ARMA/ARIMA models, model identification/estimation/linear operators, Fourier analysis, spectral estimation, and state space models.
Deep Learning 1 (DL1) (project-based)
Modeling language: Python with relevant packages. MLP foundation: layers, activations, loss functions, underfitting/overfitting, model selection, forward/backward propagation, dropout, etc. Convolutional Neural Networks foundation: Convolution, padding, stride, pooling, etc. Multiple input/output channels. Classic and modern architectures: LeNet, AlexNet, VGG, NiN, GoogleLeNet, ResNet, DenseNet, Xception, EfficientNet, etc. Reading, writing, and visualizing image data. Image Augmentation. Transfer Learning. Style Transfer. Optimization Algorithms (SGD, RMSProp, etc.). Object detection: Bounding boxes. Anchor boxes.
Deep Learning 2 (DL2) (project-based)
Modeling language: Python with relevant packages. Recurrent Neural Networks foundations. Text processing. Gated Recurrent Units (GRU). Long Short-Term Memory (LSTM). Deep Recurrent Neural Networks. Encoder-Decoder Architecture. Sequence to Sequence Learning. Beam Search. Attention Mechanisms. Word Embedding. Pretraining.
Reinforcement Learning (RL) (project-based)
Modeling language: Python with relevant packages. Markov decision processes, value functions, Monte Carlo estimation, dynamic programming, temporal difference learning, eligibility traces, and function approximation
B. Supporting Courses
In addition to university courses, exam candidates might consider online courses provided by universities on online platforms.
Dr. Pamela Thompson, Professor, Catawba College, University of North Carolina at Charlotte
Dr. Mohamed Farag, Assistant Teaching Professor, Information Networking Institute, Carnegie Mellon University
Dr. Renzhi Cao, Assistant Professor, Department of Computer Science, Pacific Lutheran University, Washington, USA
Dr. Christopher Do, ISODS President and Professor, MA, USA
Dr. Nam Nguyen, ISODS Vice President and Professor, TX, USA
Mr. Cory Wang, ISODS Vice President, Connecticut, USA
Dr. Trung Duong, Professor, Chair of Telecommunications, Queen's University, Belfast (QUB), UK
Dr. Nguyet (Moon) Nguyen, Associate Professor at Youngstown State University, USA
Dr. Kim Phuc Tran, Associate Professor, University of Lille, France
Dr. Lan Vu, Professor, Department Head and Chair at Hanoi University of Public Health, Vietnam
Dr. Loan Nguyen, Associate Professor at International University, Vietnam
Dr. Bao The Pham, Associate Professor at Saigon University, Vietnam
Dr. Gia Nhu Nguyen, Associate Professor & Dean of School of Computer Science, Duy Tan University, Vietnam
Dr. Hang Le, Vice Provost, Duy Tan University, Vietnam
Dr. Duy Nguyen, Assistant Professor, Marist College, New York, USA
Dr. Son P Nguyen, Lecturer at University of Economics and Law, Vietnam
Dr. Vinh Dang, Information Technology Faculty, Industrial University of HCM City, Vietnam
D. Exam Locations (Tentatively)
Until further notice, all the exams will be online
- Hanoi: Hanoi University of Public Health
- Da Nang: Duy Tan University
- HCM City: Saigon University
There are three designations offered by the ISODS as follows
Associate Master (CM): P, STAT, LA, CAL, PRG, DM, DSA, PA
Fellow Master (FM): ML, BDA, TS (conditional on passing all CM exams)
Grandmaster (GM): DL1, DL2, RL (conditional on passing all CM and FM exams)
E. Equivalent Credentials
University courses with grade A- on a 4.0 scale, or 8 on a 10.0 scale might be used to replace the following exams: Linear Algebra, Calculus, and Programming. SOA exam SRM might be used in place of exam STAT; SOA exams P and PA can be used in place of the same ISODS exams.
Fees include exam fee and proctoring fee. Candidates pay proctoring fee to the institution that proctors the exams. All the fees are in USD.
G. Exam schedules
We release the schedule for exams for the year of 2023 until June. All the exams are at a discount of 50%, but are the subject to change after June 2023.
Exams are currently offered on every Saturday starting at 10AM (ET time). Candidates should be able to pay the fee to ISODS at least 1 week before the exam date to ensure a slot. All the exams are proctored online via Google Workspace – Google Meet.
|Exam Name||Exam Code||Fee||Length (hours)||Date|
|Data Structures and Algorithms||DSA||85$||2||February 5|
|Data Structures and Algorithms||DSA||85$||2||February 12|
|Data Structures and Algorithms||DSA||85$||2||February 19|
|Machine Learning||ML||$270||4||February 26|
|Machine Learning||ML||$270||4||March 5|
|Machine Learning||ML||$270||4||March 12|
|Machine Learning||ML||$270||4||March 26|
|Data Structures and Algorithms||DSA||85$||2||April 2|
|Machine Learning||ML||$270||4||April 9|
|Data Structures and Algorithms||DSA||85$||2||April 16|
|Machine Learning||ML||$270||4||April 23|
|Machine Learning||ML||$270||4||May 7|
|Data Structures and Algorithms||DSA||85$||2||May 21|
|Machine Learning||ML||$270||4||May 28|
|Data Structures and Algorithms||DSA||85$||2||June 11|
|Machine Learning||ML||$270||4||June 18|
For DSA exams, candidates will be given up to 3 problems. Candidates will be expected to write the solutions (preferred Python), test cases based on a template, analyze time and space complexity (see leetcode.com for examples).
For ML exams, candidates will be given a dataset, and a problem. Candidates will be expected to write code that walks through steps required in a machine learning project.
For P exams, candidates will be given 20 questions. The format of the questions is similar as in P exams offered by the Society of Actuaries (see sample questions and solutions).
H. Exams hosted at Universities Worldwide