• Twitter

Certified Data Scientist Certification (CDS – DS2050)

  • Boost Your Career: Gain the globally recognized Certified Data Scientist Certification in data science and open doors to exciting opportunities.
  • Practical Learning: Master data science, machine learning, AI, and predictive analytics with hands-on training and expert guidance.
  • Career Support: Receive resume help, interview preparation, and job placement assistance to land top roles in the data science field.
image

Download Syllabus

Book Exam
Enquire Now
 200/-
(Including all taxes)
Eligibility Test

The Certified Data Scientist (CDS) is a well-known certification in data science, earned by 30,000+ people to show their skills and gain respect in the industry. The certification follows the EDISON European Commission framework, ensuring that the skills learned are perfectly aligned with what the industry needs. It covers key areas such as statistics, big data, programming, machine learning, and other important topics in data analysis. Data is a powerful asset, often compared to oil, because it’s essential for driving success. But to make the most of data, we need experts who can understand, analyze, and use it effectively. This is where the Certified Data Scientist comes in. These professionals have the ability to solve complex problems by using advanced skills in data analysis, machine learning, and statistical modeling. They know how to take large data sets, find meaningful patterns, and turn those insights into decisions that help organizations grow.

A Data Science Certification like the CDS gives you a valuable skill set. You will learn how to work with big data, perform statistical analysis, and apply data mining techniques to extract useful information. This knowledge helps companies understand their customers, markets, and business performance better, leading to smarter decisions. One of the key areas of this certification is machine learning. Here, you will learn about algorithms and predictive models that can forecast trends and behavior. This skill is important because it allows businesses to make decisions based on future predictions, which can have a major impact on growth and profitability.
Another essential part of the Certified Data Scientist program is statistical modeling. By mastering this skill, you will be able to analyze and predict data more effectively. In today’s world, where data is everywhere, being good at statistical modeling gives you the ability to make better decisions based on facts rather than assumptions.

The certification also teaches data visualization, which is the ability to present complex data in a clear and easy-to-understand way. This is crucial because being able to communicate data insights visually helps teams and decision-makers quickly grasp important information and make faster decisions. The Certified Data Scientist program provides a well-rounded education in data science. From data analysis and predictive analytics to machine learning and AI, it prepares you for a career in one of the most in-demand fields today. With this Data Science Certification, you will be equipped to turn big data into valuable insights, making you a highly sought-after professional in the world of business and technology.

image

Not sure about your success rate?

image

COURSE SYLLABUS

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.
  • SQL JOINs
  • 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

Find Authorized Training Providers

Steps to achieve CDS Certification

1. Check Eligibility: Before starting, make sure you meet the basic requirements. There are no strict rules, but having some knowledge of data science and statistics is recommended.

2. Prepare for the Exam: You can join a CDS training program from an IABAC-approved institute or study on your own using books, online resources, and practice exams. Create a study plan and make sure you cover all topics in the syllabus.

3. Book the Exam: Once you are ready, book your exam. If you joined a training program, your provider can help with booking. You can also book it yourself through the IABAC website. Give yourself enough time to revise before the test.

4. Take the Web-Proctored Exam: The exam is online and supervised remotely. Make sure you have a good internet connection, a quiet place, and a working computer. Learn how the exam system works ahead of time and follow all rules during the test.

5. Get Results and Certification: Results are usually available within 10 working days. If you pass, you will receive your e-certificate by email. If not, review your results, improve where needed, and try again after more preparation.

Prerequisite Certification

To become a successful data scientist, there are several key areas to focus on. Here’s a simple guide to help you get started:

  • Build a Strong Foundation: Start by learning the basics of math, statistics, and programming. These subjects are essential for understanding how data science works and for solving problems in the field.
  • Develop Analytical Skills: Being a good data scientist means being able to think critically and solve problems. You’ll need to analyze complex data and find patterns or insights that can help make decisions.
  • Learn Programming: You’ll need to be comfortable with programming languages like Python, R, or SQL. These tools allow you to handle and analyze data effectively. It’s important to get hands-on experience with these languages to become more efficient in your work.
  • Understand Statistics: A solid understanding of statistics is crucial in data science. You'll need to know how to analyze data, test hypotheses, and draw meaningful conclusions based on your findings.
  • Gain Data Handling Experience: Data scientists often work with large datasets, so it’s important to become familiar with tools like Hadoop, Spark, or SQL databases. These tools help manage and process data quickly and efficiently.

If you're serious about pursuing a career in data science, earning a Data Scientist Certification or other Data Science Certifications can be a great way to prove your skills to potential employers. These certifications can show that you have the knowledge and experience needed to succeed in the field.

Who can pursue this certification?

  • Professionals Switching to Data Science: If you already work in fields like IT, software development, business analysis, or math and are looking to move into data science, a
  • Data Scientist Certification can help you build the skills and credentials you need to make that change.
  • Graduates with a Quantitative Background: If you have a degree in areas like mathematics, statistics, computer science, economics, or other similar subjects, a Data Science Certification can deepen your knowledge and give you a competitive edge when applying for jobs in data science.
  • Working Professionals Looking to Move Up: If you're already in a data-related role and want to advance in your career, getting a Data Science Certification can show your expertise and help you open up new opportunities, whether it’s a promotion or a different job in the field.
  • Analysts and Researchers: If you work as a data analyst, business analyst, market researcher, or in any other research role, a Data Science Certification can teach you advanced techniques to analyze data more effectively and gain better insights.
  • Entrepreneurs and Business Owners: If you're running a business, having data science skills can help you make better decisions, improve operations, and stay ahead of the competition. A Data Science Certification can give you the tools to make the most of your data.
  • Students and Recent Graduates: If you’re still in school or have recently graduated and want to specialize in data science, earning a Data Scientist Certification can make your resume stand out and help you get hired in this growing field.

This version uses simpler words and avoids the terms you requested, while still conveying the same ideas about who can pursue Data Science Certifications.

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.

Specialization

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

DATA SCIENCE CERTIFICATIONS

image

Data Science Foundation
image

Certified Data Science Developer
image

Certified Machine Learning Expert
image

Certified Data Scientist - Operations

image

Certified Data Scientist - Finance

image

Certified Data Scientist - HR

image

Certified Data Scientist - Marketing
image

Data Science Certified Manager
image

Certified Data Engineer
image

Certified MLOps Engineer

COURSE FAQs

Does IABAC provide training for Certified Data Scientist certification?

IABAC does not directly provide training for the Certified Data Scientist Certification. However, IABAC has a worldwide network of authorized training partners who offer courses that follow the IABAC program syllabus. These partners help learners prepare effectively for Data Science Certifications and related exams.

Does Certified Data Scientist certification require renewal?

Yes, the Certified Data Scientist (CDS) Certification is an advanced-level program, and according to IABAC guidelines, it needs to be renewed every 3 years. This helps ensure that professionals stay updated with the latest tools and practices in Data Science Certifications.

What is the process to re-take CDS exam?

The process to re-take the CDS exam is the same as booking a regular exam. If you registered through an IABAC authorized partner, your training institute can help you book the re-take at a discounted rate. This applies to all Data Science Certifications offered through IABAC and its partners.

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

It usually takes about 10 working days to receive the CDS certificate after you complete the exam. This process time is similar for other Data Science Certifications offered by IABAC.

What is IABAC CDS certification value in the global market?

The IABAC Certified Data Scientist (CDS) Certification is highly respected worldwide. IABAC is the first global Data Science certification board based on the European Commission’s EDISON framework. It is well recognized in regions such as the USA, UK, Singapore, India, and the Middle East. Many international companies mention IABAC Data Science Certifications as a preferred qualification in their job descriptions.

Where can I find IABAC CDS exam questions for practice?

You can find mock exam questions on the official IABAC website. There are also several unofficial sources that provide practice materials for the IABAC CDS exam. These resources can help you prepare well for Data Science Certifications and improve your understanding of key topics

Is it mandatory to take training from IABAC authorised institutes?

No, it’s not mandatory to take training from IABAC-authorized institutes. However, it’s highly recommended because the pass rate for learners from authorized institutes is above 95%. Their training is closely aligned with the exam requirements for Data Science Certifications, which helps you prepare more effectively.

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

The EDISON Data Science Framework (EDSF) is a European Commission program that defines the key skills, knowledge, and learning structure needed in data science. It includes important documents like the Competence Framework, the Body of Knowledge, and the Model Curriculum. The program also involves experts, educators, and researchers who keep the framework updated. This framework forms the base for many Data Science Certifications, including IABAC programs..

https://edison-project.eu/edison/

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

To change your name on the certificate, you need to send a request by email to [email protected]. Make sure to include an identity proof showing the correct name. The name change process usually takes around 15 working days. This process applies to all Data Science Certifications issued by IABAC.

Can an Indian data scientist work in the USA?

Yes, an Indian data scientist can work in the USA. Many companies hire skilled professionals from different countries for data science roles. Having a strong skill set, good project experience, and relevant certifications can improve your chances. Certifications from recognized organizations like International Association of Business Analytics Certification can help strengthen your profile and show your knowledge in data science. You will also need the right work visa, but with the right skills and preparation, working in the USA is possible.

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

 To get a Data Scientist job in the USA:

  1. Study relevant subjects: Like computer science, statistics, or data science.
  2. Learn key skills: Python, R, SQL, and basic machine learning.
  3. Gain experience: Do projects, internships, or freelance work to build a portfolio.
  4. Network: Join online groups and connect with professionals.
  5. Apply for jobs: Use job boards and company websites, and customize your resume.
  6. Completing Data Science Certifications can also help you show your skills to employers.

What is a data scientist?

A data scientist is a person who works with data to find useful information. They study data to understand patterns and help businesses make better decisions.

They clean and analyze data, build simple models, and use tools like Python, SQL, and data visualization to explain results.

In simple words, a data scientist turns raw data into clear insights that help companies solve problems and improve their work.

What does a data scientist do?

A data scientist works with data to help businesses make better decisions. They collect data, clean it, and study it to find useful patterns.

They use tools like Python and SQL to analyze data and build models. They also create charts and reports to explain their findings in a simple way.

In short, a data scientist turns data into insights that help solve problems and improve business results.

What is a data scientist course?

A data scientist course is a training program that teaches you how to work with data. It helps you learn how to collect, clean, and analyze data to find useful insights.

In this course, you learn skills like Python, SQL, data analysis, basic machine learning, and data visualization. You also work on projects to understand how data is used in real situations.

In simple terms, a data scientist course helps you build the skills needed to start a career in data science.

What do you need to be a data scientist?

To become a data scientist, you need a mix of basic skills and practical knowledge.

You should understand math and statistics, as they help you work with data and find patterns. Programming skills, especially in Python or R, are also important to analyze data and build models.

You also need to learn how to collect, clean, and study data, along with creating simple charts or reports to explain your findings.

Working on small projects can help you practice these skills and gain confidence.

What is the work of a data scientist?

A data scientist works with data to help businesses make better decisions. Their main work is to collect data, clean it, and study it to find useful information.

They analyze data, build simple models, and find patterns that can solve problems or improve results. They also create charts and reports to explain their findings in an easy way.

In simple terms, a data scientist turns data into useful insights that help companies grow and make smarter decisions.