How Is Chartered Data Scientist Program Different From The Data Science Training

Here we will discuss how a professional certification like Chartered Data Scientist is different from a data science training. The Chartered Data Scientist is a self-study program where the aspirant needs to prepare on their own for the exam and hence, there is no need to attend any training for this. 

Chartered Data ScientistTM is a prestigious distinction provided by the Association of Data Scientists (ADaSci) to the professionals in the field of Data Science. This charter is provided to those professionals who satisfy certain requirements. To achieve this distinction, the aspirants need to successfully pass the exam. This is a self-study program where the aspirant needs to prepare on their own for the exam and hence, there is no need to attend any training for this. 

What is Training and a Training Certificate?

There are a number of training institutes available who are engaged in providing training to candidates in the field of data science. The candidates are trained on the skills through the lectures and practical sessions. Some of those training institutes provide a certificate to the candidates on the completion of a training program. Very few training programs are followed by a test to check the knowledge candidates have gained during the training and a certificate is given to those candidates who clear the test. The important thing is that these types of certificates achieved after training are never considered as professional certificates. 

What is Professional Certification?

A professional certification program is always offered by a professional organization or a society. These certification programs aim to provide recognition to the candidates who achieve this. The certificates are offered to those who meet certain requirements such as educational qualification, the domain of working, experience etc. Almost all the professional certifications are preceded by an examination and candidates need to pass this examination with required scores. 

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The Difference between a Training and a Certification Program

Here we will discuss how a professional certification like Chartered Data ScientistTM is different from a data science training. First, we will discuss the differences between a training program and a certification program.

Training Program Certification Program
Purpose The candidates are trained on a skill or a subject The candidates are tested on a skill
Provider Any training institute, coaching, academic or professional body A professional organization or society
Approach Delivered through the lecture and practical classes Conferred after meeting certain criteria, such as exam
Who can achieve Anyone, no criteria or eligibility required For every certification, there is a fixed target audience who meets a certain requirement
Availability in the marketplace For one skill, you can find multiple training providers in the same location For every certification, there is only one unique provider
Showcasing You will have to showcase your knowledge yourself every time that you have gained in training The certificate proves your knowledge, you need not showcase your skills every time
Weightage in Job Very less weightage More weightage as it certifies the skills
Commercialization Most of the training programs are started as a business activity by the provider It aims to provide identification and recognition to the talented candidates
Cost Always high and it depends on the duration of training Very low as compared to a training

Chartered Data ScientistTM (CDSTM) – A Professional Certification Program

The Chartered Data ScientistTM is a designation provided to the professionals in the field of data science. This is not just a certificate but a designation that can be added with the name of its owner. The CDSTM charter shows that the holder is certified in a particular skill. This charter is not limited to certifying a person in one or a couple of skills. It has more open scope where the holder of this charter is proved to be an expert in all the domains of data science, technically and analytically. Along with the knowledge, the holder is also required to carry at least two years of experience as a data scientist. The holder is also required to be committed to following certain ethical standards in the field of data science. 

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Chartered Data Scientist is Not a Training Program

The CDSTM is not a training program where a candidate is provided with training on a data science skill. As it is a self-study program, to take part in this, the candidates need to prepare himself/herself. The ADaSci provides a detailed curriculum for CDSTM exam and suggests reference study materials. It does not take any charge for study material or training. 

CDSTM is used as a Designation

Finally, we will conclude that CDSTM is not a training program where the candidates are required to attend any training on a data science skillset. It is a distinction in the field of data science that is provided to the professionals. The CDSTM is used as a designation by the people who hold this charter. The designation itself proves the strong understanding of the data science profession and in-depth applied analytical skills.

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by Vijayalakshmi Anandan

The Deep Learning Curve is a technology-based podcast hosted by Vijayalakshmi Anandan - Video Presenter and Podcaster at Analytics India Magazine. This podcast is the narrator's journey of curiosity and discovery in the world of technology.

Dr. Vaibhav Kumar
Dr. Vaibhav Kumar is a seasoned data science professional with great exposure to machine learning and deep learning. He has good exposure to research, where he has published several research papers in reputed international journals and presented papers at reputed international conferences. He has worked across industry and academia and has led many research and development projects in AI and machine learning. Along with his current role, he has also been associated with many reputed research labs and universities where he contributes as visiting researcher and professor.

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