Certified Data Scientist Certification (CDS – DS2050)

  • Skill Enhancement: The Certified Data Scientist Certification boosts your career by validating your advanced proficiency in data analysis, machine learning, and predictive modeling.
  • Recognition & Credibility: Recognized globally, this certification strengthens your professional standing, endorsing your expertise in handling complex data sets and driving strategic decision-making.
  • Competitive Edge: Earning this certification provides a competitive edge in the job market, demonstrating your commitment to staying ahead of the curve in the rapidly evolving field of data science.

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The Certified Data Scientist (CDS) is widely considered as one of the premier certifications in the field of data science. With over 30,000 active professionals holding this esteemed certification, it has earned immense respect. The CDS certification syllabus follows the EDISON European commission framework, ensuring that data science skills align perfectly with industry demands. This comprehensive certification program covers crucial areas such as statistics, big data, programming, machine learning, and other essential analytics concepts.

In the rapidly evolving digital landscape, data is the new oil. It's the cornerstone upon which future successes are built. But to harness its true potential, we need experts who can effectively use, interpret, and analyze this data. Enter the Certified Data Scientist - an individual adept at data analysis, predictive analytics, artificial intelligence (AI), and much more. A Data Scientist is more than just a data analyst; they are problem solvers capable of unraveling complex issues through their prowess in machine learning and statistical modeling. This unique blend of skills enables them to extract valuable insights from big data and convert them into strategic business decisions.

The certified data scientist certification equips you with a unique skillset, including big data handling, statistical analysis, and data mining. You gain proficiency in tools and techniques designed to decode extensive data sets and decipher trends. Through such insight, you'll be capable of aiding organizations to better understand their market, customers, and business performance.

Machine learning, a core component of this certification, introduces you to algorithms and predictive models that help forecast future trends and behaviors. Leveraging this skill, you can generate a significant impact on your organization’s growth trajectory, paving the way for more informed strategic decisions.

Another key aspect of this certification is statistical modeling. This vital skill empowers you to interpret, analyze, and predict data efficiently. In a world overflowing with data, a strong foundation in statistical modeling gives you the edge to make data-driven decisions that could propel your organization ahead of competitors. The certification will also delve into the intricacies of data visualization. This skill enables you to present complex data in an easily comprehensible and visually appealing manner, facilitating better understanding and quicker decision-making within your organization.

The certified data scientist certification offers comprehensive exposure to a gamut of skills - from data analysis and predictive analytics to machine learning and Artificial intelligence. It's the stepping stone you need to become a highly sought-after professional, capable of turning big data into big insights and big decisions.


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Data Science Foundation

  • Introduction to Data Science
  • Data Science vs Business Analytics vs Big Data
  • Classification of Business Analytics
  • Data Science Project Workflow
  • Various Roles in Data Science
  • Application of Data Science in various industries

Python for Data Science

  • Introduction to Data Science with Python
  • Python Basics: Basic Syntax, Data Structures
  • Data objects, Math, Comparison Operators, Condition Statements, loops, lists, tuples, dicts, functions
  • Numpy Package
  • Pandas Package
  • Python Advanced: Data Munging with Pandas
  • Python Advanced: Visualization with Matplotlib
  • Exploratory Data Analysis: Data Cleaning, Data Wrangling
  • Exploratory Data Analysis: Case Study

Statistics for Data Science

  • Introduction to Statistics
  • Harnessing Data
  • Exploratory Analysis
  • Distributions
  • Hypothesis & Computational Techniques
  • Correlation & Regression

Visual Analytics Foundation

  • Visual Analytics Basics
  • Basic Charts, Plots

SQL for Data Science

  • Install SQL packages and Connecting to DB
  • RDBMS (Relational Database Management) Basics
  • Basics of SQL DB, Primary key, Foreign Key
  • SELECT SQL command, WHERE Condition
  • Retrieving Data with SELECT SQL command and WHERE Condition to Pandas DataFrame.
  • Left Join, Right Joins, Multiple Joins

Machine Learning Associate

  • Machine Learning Introduction
  • What is ML? ML vs AI. ML Workflow, Statistical Modelling of ML. Application of ML
  • Machine Learning Algorithms
  • Popular ML algorithms, Clustering, Classification and Regression, Supervised vs Unsupervised.
  • Choice of ML
  • Supervised Learning
  • Simple and Multiple Linear Regression, KNN, and more
  • Linear Regression and Logistic Regression
  • Theory of Linear regression, hands on with use cases
  • K-Nearest Neighbour (KNN)
  • Decision Tree
  • Naïve Bayes Classifier
  • Unsupervised Learning: K-Means Clustering

Machine Learning Expert

  • Advanced Machine Learning Concepts
  • Tuning with Hyper parameters
  • Random Forest – Ensemble
  • Ensemble Theory, Random Forest Tuning
  • Support Vector Machine (SVM)
  • Simple and Multiple Linear Regression, KNN
  • Natural Language Processing (NLP)
  • Text Processing with Vectorization, Sentiment Analysis with Text Blob, Twitter Sentiment Analysis.
  • Naïve Bayes Classifier
  • Naïve Bayes for Text Classification, New Articles Tagging
  • Artificial Neural Network (ANN)
  • Basic ANN network for Regression and Classification
  • TensorFlow Overview
  • Deep Learning Intro

Time Series Foundation

  • What is a Time-Series?
  • Trend, Seasonality, Cyclical and Random
  • White Noise
  • Auto Regressive Model (AR)
  • Moving Average Model (MA)
  • ARMA Model
  • Stationarity of Time Series
  • ARIMA Model – Prediction Concepts
  • ARIMA Model Hands on with Python
  • Case Study Assignment on ARIMA

Model Deployment

  • Basics of Application Program Interface (API)
  • API basics, loosely Coupled Architecture
  • Installing Flask
  • Installation and configuring Flask and cross domain authentication.
  • End to End ML project with API Deployment
  • Complete Project Flow with API Deployment and assessing through the website

Deep Learning Foundation

  • Introduction to Deep learning
  • What is Deep Learning?
  • Various Deep Learning models in practice and applications.
  • Convolutional Neural Network CNN Intro
  • Case Study: Keras–TensorFlow Image Classification
  • CNN hands on application for classification of images of Cats and Dogs

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Steps to achieve CDS Certification

Check the Eligibility

Before embarking on the journey to attain certified data scientist (CDS) certification, it is crucial to verify if you meet the eligibility criteria. Although there are no hard prerequisites for this certification, it is highly recommended to learn the basics of data science and statistics before starting your certification program.

Exam Preparation

It  is highly recommended to pursue a CDS training program from IABAC authorised training institutes so as to acquire required knowledge to successfully attempt this certification.  Alternatively, you may choose to self-prepare by utilising online resources, books, practice exams, and other relevant materials. Develop a study plan, allocate sufficient time, and focus on covering all the topics outlined in the CDS certification syllabus.

Book Exam

Once you feel adequately prepared, it is time to book the CDS certification exam. If you pursue training from authorised training providers, you can request them to book the exam for you. Alternatively, you can directly book an exam through the book exam section on this page. This exam requires in-depth knowledge on data science and machine learning, so ensure to schedule the exam allowing  you ample time for revision and final preparation.

Web-Proctored Exam

IABAC exams are web-proctored exams, allowing you to take the test remotely from your own location. Ensure you have a reliable internet connection, a quiet and well-lit environment, and a compatible computer or device. Familiarise yourself with the exam platform and test interface prior to the exam day. During the exam, strictly adhere to the proctoring guidelines and maintain exam integrity.

Results & Certification

After completing the exam, it usually takes about 10 working days to report the results. Once the results are out, you will be notified about your performance. If you pass the exam, congratulations! You will receive your certification in e-certification format to your registered email. In case you don't pass, take the opportunity to analyse your performance, identify areas for improvement, and consider retaking the exam after further preparation.

Certified Data Scientist

(Test Preparation Study Guide)

Boost your data science career with the Certified Data Scientist certification. Get a free study guide now!


The Benefits

International Credential

IABAC is a widely recognized credentialing framework based on European commission funded EDISON Data Science body of knowledge. This credential provides distinction as high potential certified Data Science Professionals enabling better career prospects.

Global Opportunities

IABAC certification provides global recognition of the relevant skills, thereby opening opportunities across the world.


IABAC Certification designed to cater to the job requirements of all experience levels and specializations, which suits roles aligned with the industry standards.

Relevant and updated

IABAC CPD (Continuing Professional Development) program enables credential holders to update their skills and stay relevant to the industry requirements.

Higher Salaries

On an average, a certified professional earns 30-40% more than their non-certified as per recent study by Forbes.

Summits & Webinars

In addition, IABAC members will have exclusive access to seminars and Data Science summits organised by IABAC partners across the globe.

Not sure which certification suits your goal? Get a free counselling

Certified Data Scientist Exam Structure

  • Certified Data Scientist (CDS) certification exam is an open book assessment, this means the candidates can refer any material during the exam period.

  • The exam required the candidate to submit a case-study project along with the predictive ML model and project report as per the exam objectives.

  • The project is graded for three areas: Project Summary with recommendations, Machine Learning model performance and Exploratory Data Analysis

  • Exam pass criteria:

         The candidate needs to score assessment grade A+, A, B+, B, C+, C as a PASS Criteria

         The candidate will be awarded grade F in case of failing to meet the pass criteria

         The results will be declared after validation with the project guidelines


    Download sample IABAC CDS mock exam paper



Does IABAC provide training for Certified Data Scientist certification?

IABAC doesn’t provide training for CDS certification but IABAC has a world-wide network of authorised training partners, who provide training aligned with the IABAC program syllabus.

Does Certified Data Scientist certification require renewal?

Certified Data Scientist (CDS) is an advanced level certification and thereby, as per IABAC guidelines, this certification required renewal every 3 years.

What is the process to re-take CDS exam?

The CDS exam retake process is the same as the regular booking. If you have booked through IABAC authorised partners then, your training institute can book the retake at significant discount.

How long does it take to get a CDS certificate after the exam completion?

The time taken for CDS certification after the exam completion is usually 10 working days.

What is IABAC CDS certification value in the global market?

IABAC is arguably the most respected certification and the world’s first Data Science certification board based on the European commission Edison framework.  The regions such as the USA, UK, Singapore, India and the Middle-east, IABAC is well recognised and many MNCs specifically mention IABAC requirements in their job descriptions.

Where can I find IABAC CDS exam questions for practice?

You can find the mock questions on IABAC website. Also, there are many unofficial sources for IABAC exam practice questions.

Is it mandatory to take training from IABAC authorised institutes?

No. It is not mandatory to take training from IABAC authorised institutes but it is highly recommended as the pass rate through the authorised institutes is above 95% as the training is aligned with exam requirements.

What is EDISON Data Science Framework (EDSF) - European Commission Program?

Data Science Framework (EDSF) is based around a group of tightly related documents namely the Competence FRamework, the Body of Knowledge and the Model Curriculum, the various processes that exist to keep the information within these documents are almost equally as important. Therefore the activities of the Expert Liaison Groups, the Education and Training Champions as well as the ongoing fieldwork and research should be considered as valuable components of the Framework.


What is the process to change my name on the certificate?

The name change needs to be requested over email service@iabac.org. You need to include the identity proof with name matching with the request name change. The process usually takes about 15 working days.

How to get a job in the USA as a data scientist?

To get a job as a Data Scientist in the USA, follow these steps:

  1. Gain relevant education: Obtain a degree in fields like computer science, statistics, mathematics, or data science.

  2. Build skills: Learn programming languages like Python and R, data analysis tools like SQL and Pandas, and machine learning techniques.

  3. Gain experience: Work on projects, internships, or freelance gigs to build a portfolio showcasing your skills.

  4. Network: Attend industry events, join online communities, and connect with professionals in the field.

  5. Apply for jobs: Look for openings on job boards, company websites, and professional networks, and tailor your resume and cover letter to each application.