Data Science Certifications

Guide to Data Science

The field of data science offers exciting opportunities for individuals seeking a rewarding career. To meet the industry demands for skilled data science professionals, the IABAC Data Science certification scheme provides a comprehensive and standardized credentialing framework. This program is built upon the renowned EDISON® - European Commission initiative, which aims to establish a solid Data Science Body of Knowledge and syllabus.

The IABAC certification scheme offers a range of certifications tailored to individuals at various stages of their Data Science careers. Whether you are a beginner aspiring to enter the field or an accomplished professional looking for more challenging roles, there is a certification that suits your needs. These certifications are designed to validate your expertise in managing and delivering Data Science assignments in accordance with global best practices.

The IABAC certification scheme upholds rigorous standards throughout the certification process. This includes pre-exam validation of candidates' identity proofs and prerequisite checks. Exams are administered directly by IABAC or through authorized partners worldwide, ensuring the credibility and integrity of the certification process.

Obtaining an IABAC Data Science certification can greatly enhance your career prospects.This suits better if a professional aiming for career advancement, or someone from a non-technical background interested in data science, the IABAC certification scheme provides a comprehensive pathway to success.

Investing in a data science certification not only validates your expertise but also demonstrates your commitment to professional growth and development. It showcases your ability to apply data science methodologies and techniques to real-world problems, making you a valuable asset to organizations seeking data-driven insights. Furthermore, a career in data science offers the potential for attractive salaries, job security, and opportunities for continuous learning and advancement.


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Data Science Certifications

Data Science Foundation (DSF)

The IABAC Data Science Foundation certification is exclusively designed for those individuals who have a passion for data science and wish to make further explorations in the field.

Certified Data Science Developer (CDSD)

Data science, as a field, encompasses the art of extracting valuable insights from vast amounts of data.

Certified Machine Learning Expert (CMLE )

Machine learning has become a crucial aspect of businesses across various industries.

Certified Data Scientist (CDS)

The Certified Data Scientist (CDS) is widely considered as one of the premier certifications in the field of data science.

Certified Data Scientist - Operations (CDSOPS)

Realize your potential as a Certified Data Scientist - Operations and elevate your career to new heights.

Certified Data Scientist - Finance (CDSFIN )

The demand for skilled professionals who can harness the power of data to drive financial success is skyrocketing.

Certified Data Scientist - HR (CDSHR )

Data plays an increasingly crucial role in HR.

Certified Data Scientist - Marketing (CDSMKT )

Data analysis for marketing forms the bedrock of IABAC certification, allowing marketers to uncover hidden patterns and trends in large datasets.

Data Science Certified Manager (DSCM )

Data science management is the art of effectively harnessing the potential of data to propel organizations towards success.

Certified Data Engineer (CDE)

In the fast-paced, data-driven world of today, obtaining a Certified Data Engineer Certification can open the doors to countless professional opportunities.

Certified MLOps Engineer (CMOE)

Introducing the Certified MLOps Engineer Certification, your gateway to a thriving career in the realm of machine learning operations (MLOps).

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.

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What is Data Science?

Data science is an exciting field that combines statistics, programming, and domain expertise to extract vaImgluable insights from data. It involves analyzing vast amounts of information, identifying patterns, and making data-driven decisions. By utilizing advanced techniques, data scientists uncover hidden trends, solve complex problems, and drive innovation across industries. Whether it's predicting customer behavior or optimizing business strategies, data science empowers organizations to harness the power of data for better outcomes. Explore the fascinating world of data science and unlock endless possibilities for growth and success.



What is the Scope of Data Science?

The scope of data science is immense and promising. As the world becomes more data-driven, organizations across industries are realizing the power of data to drive decision-making and gain a competitive advantage. Data science offers a wide range of opportunities for professionals, including roles such as data scientists, data analysts, machine learning engineers, and data engineers. With the increasing availability of data and advancements in technology, data science will continue to play a crucial role in fields like healthcare, finance, marketing, technology, and more. The scope for growth, innovation, and career advancement in data science is expected to remain strong in the coming years.

What are the Tools used in Data Science?

In data science, various tools are used to analyze and interpret data effectively. Some popular tools include Python (with libraries like NumPy, Pandas, and SciPy), R programming language, SQL for database querying, Tableau for data visualization, Hadoop and Spark for big data processing, and TensorFlow or PyTorch for machine learning and deep learning. Each tool has its strengths and specific use cases, allowing data scientists to manipulate, explore, and derive insights from complex data sets.

What are the Skills required for Data Science?

Data science requires a combination of technical and soft skills. Technical skills include proficiency in programming languages (Python, R, SQL), statistical analysis, data visualization, machine learning algorithms, and data manipulation. Soft skills such as critical thinking, problem-solving, communication, and domain knowledge are also crucial. Additionally, staying updated with the latest industry trends and having a curious and analytical mindset is important for success in data science.

How can I get Certification in Data Science?

To get certified in data science, several options are available. Many renowned organizations offer certifications, such as IBM Data Science Professional Certificate, Microsoft Certified: Azure Data Scientist Associate, and Google Data Analytics Professional Certificate. Online learning platforms like Coursera, edX, and Udacity also provide comprehensive data science courses with certificates upon completion. Choose a certification program aligned with your interests and career goals to enhance your knowledge and credibility in the field.


Does data science require coding?

Yes, coding is a fundamental skill required in data science. Data scientists utilize coding to create and implement deep learning algorithms, AI models, and other analytical tools. By writing code, data scientists can manipulate and analyze data, build predictive models, and extract valuable insights from large datasets. Therefore, coding is an essential component of data science, enabling professionals to effectively work with data and develop customized solutions for various data-related challenges

Which field within data science is currently in high demand?

The field of data analysis is currently experiencing high demand. Data analysts play a crucial role in cleaning, transforming, and processing raw data to derive valuable insights that inform business decisions. Consequently, skills related to data analysis are highly sought after in the job market.

Is data science more challenging than programming?

The level of difficulty in data science compared to programming can vary depending on individual preferences and strengths. For individuals who have a strong affinity for statistical analysis and analytical thinking, data science may appear less challenging than software engineering. Conversely, those with extensive programming experience and a penchant for problem-solving may find software engineering to be comparatively easier. Ultimately, the perception of difficulty in these fields is subjective and can vary from person to person.

Is data science a challenging career?

Yes, data science is considered a challenging career due to its requirement for a strong background in mathematics, statistics, and computer programming. Pursuing a degree in data science can be demanding, but with dedication and effort, anyone can acquire the necessary skills and knowledge to excel in this field.

Is data science more math or CS?

Mathematical knowledge is highly important for a career in data science, as it forms the foundation for many data science techniques and models. However, computer science skills are also essential for working with data and developing algorithms. Both math and computer science play crucial roles in data science.