In continuation to the previous webinar “What is Chartered Data ScientistTM Program”, the Association of Data Scientists (ADaSci) is coming up with the next webinar on “How to Crack Chartered Data Scientist Exam?”.
Chartered Data ScientistTM (CDSTM) is the highest distinction in the field of data science. This program is organized by the Association of Data Scientists (ADaSci). This not just a professional certification, but it can also be used as a designation by its holders. To achieve this distinction, an aspirant needs to clear the exam conducted by the ADaSci. Successful candidates who meet the required conditions are awarded this charter.
In order to clear the exam and achieve the CDSTM designation, one requires a proper study plan and in a structured manner. There are study resources that are suggested by the ADaSci to prepare for this exam but it requires a strategy to prepare from these resources. However, the successful candidates have reported that they took around 200 hours on an average to prepare for this exam, but there was always a proper strategy used to crack the exam.
So in this webinar, the ADaSci will discuss the strategy to prepare and crack the Chartered Data ScientistTM exam in detail. The following key points will be covered during this session:-
- About Chartered Data ScientistTM (CDSTM) & Association of Data Scientists (ADaSci)
- Benefits of CDSTM Designation
- Steps to Achieve CDSTM Distinction
- CDSTM exam Information & Curriculum
- How to prepare, resources and best strategy to prepare for CDSTM exam
- Questions and Answers
To register for this webinar, click here.
Date: 5th September 2020
Time: 11:00 AM to 01:00 PM IST
Dr. Vaibhav Kumar
Dr Vaibhav Kumar is the Director at The Association of Data Scientists (ADaSci). He has broad experience in the field of Data Science and Machine Learning, including research and development. He holds a PhD degree in which he has worked in the area of Deep Learning for Stock Market Prediction.
Click here to register for this webinar.
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Vaibhav Kumar has experience in the field of Data Science and Machine Learning, including research and development. He holds a PhD degree in which he has worked in the area of Deep Learning for Stock Market Prediction. He has published/presented more than 15 research papers in international journals and conferences. He has an interest in writing articles related to data science, machine learning and artificial intelligence.