Thank you for your interest in the Certified in Data Science ™ certification.
IPMC ™ AAPM Global Board of Standards issues the Certified in Data Science ™ Online Certification
Study method: Self-paced learning style with the aid of a Guide book, this book may not be provided for some courses. If a guide book is not available, you may need to browse through videos available on YouTube if necessary however individual with sufficient experience while performing his/her role as a network engineer will not face difficulty in taking the exam.
Requirements: i) Diploma / Degree ii) 3+ years experience in data analytics iii) Pass the CDS examination
To qualify for the Certified in Data Science ™certification, you need to complete the following steps:
- Complete the Mandatory Candidate Information (MCI)
- Complete the payment upon receipt of the invoice
- Take the examination when you’re ready
Examination: email to request the password to the examination questions when you are ready to take the exam. Although you can take the exam at anytime we encourage you to complete the exam within 10 days from the date you have successfully registered in this course. The examination comprised of 50 questions; Passing grade is 70%. You will be notified should you fail in the exam and a second attempt will be given at no additional charge.
Course fee: Includes Certified in Data Science certificate, membership certificate, examination fee, and shipping.
Mandatory Candidate Information : please include the Title of the Certification Course that you wish to apply in the subject line when submitting the following information and attach a latest copy of your C.V. email to email@example.com
1) Full Name:
2) Shipping Address: Country , Address Line 1, Address Line 2, City, State / Capital, Zip code
3) Name on Certificate: (indicate the name that you wish to print on the certificate, name will be printed as per the letter case indicated here)
4) Mobile number:
The AAPM AMERICAN ACADEMY OF PROJECT MANAGEMENT ® GAFM/IBS International Board of Standards is the first graduate global Board of Standards for project management industry professionals to earn Accreditation under the TUV-OE European Standards for ISO 9001 Certification and ISO 29990 Certification. AAPM is also a chartered member of the CHEA Council for Higher Education Accreditation – International Quality Group (CHEA/CIQG) Memorandum of Affiliation is designed to engage quality assurance and accrediting organizations in a shared effort to affirm and promote fundamental principles for higher education quality.with
BENEFITS OF CERTIFICATION FOR DATA SCIENTIST
- Developing Big Data Management and Leadership through the promotion of specialty certification as a vehicle for post-licensure professional development.
- Advancing Technology through the development of specialized body of knowledge for utilization during the certification process.
- Advocating Lifelong Learning through the requirement of on-going professional development in the re-certification process after achieving licensure and professional experience.
- Promoting the Data Science profession through the provision of specialty certification as a broadly recognized, specialized credential in the practice of Big data management.
- Certification is an advanced qualification beyond licensure recognized by clients, employers, peers, societies, communities, and the public.
- Certification provides tangible evidence that an individual has excelled in their specialty field of data analytics.
- Certification demonstrates attainment of a body of knowledge within a specialty area of data analytics and commitment to stay current on new technological innovations.
- Certification demonstrates a strong commitment to professionalism through its ethics and continuing professional development requirements.
- Certification allows Data Scientist to maintain significant input into the advanced credentialing process.
- Certification provides clients with an assurance that they are engaging highly qualified and certified Data Scientist in their organization.
You need knowledge on Big data, data analysis and visualization, Python programming, Python for data science and Machine learning, ETL, data processing, supervised learning (classification), unsupervised learning (clustering), supervised learning (regression), model evaluation, applications of data science.