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ADESA Certified Data Science Professional (ACDSP) Registration

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ADESA Certified Data Science Professional (ACDSP) Syllabus The ADESA Certified Data Science Professional syllabus has the following topic areas and sub-components – 1. Accessing and Manipulating Data 1.1 Preparing data for analysis and modelling 1.2 Developing optimal data structures 1.3 Transforming data into usable datasets 1.4 Exploratory data analysis including identification of anomalies and outliers 1.5 Handling missing data - imputation and other methods 2. Data Mining and Modelling: Supervised and Unsupervised Learning Systems 2.1 Classification methods 2.2 Regression methods – linear, logistic and non-linear regression models 2.3 Time-series forecasting methods 2.4 Ensemble methods e.g. Boosted Decision Trees and Forests 2.5 Association-based data mining schemes 2.6 Unsupervised learning through clustering segmentation 3. Advanced AI and Machine Learning (ML) Systems 3.1 Designing AI and ML distributed processing architectures 3.2 Configuring AI/ML workflows and pipelines 3.3 Working with Artificial Neural Networks (ANNs) 4. Data Science Tools, Platforms and Languages 4.1 Developing R-language data mining systems 4.2 R-Studio packages for data science 4.3 Python-language data mining 4.4 Visualization tools and visual analytics 5. Commercial Applications and Productionized DS Systems 5.1 Matching technologies to business requirements 5.2 Selecting tools and toolsets to match the analysis context 5.3 Monitoring and tuning AI/ML system performance NOTE: The certification pdf with full syllabus and detailed certification rules will be dispatched electronically, via e-mail, once the registration has been completed. Non-member candidates who have paid for the certification will be required to join ADESA with a minimum membership grade of Associate Member (which is currently free) before the registration is processed and accepted. The online examination for the certification is scheduled no earlier than 2 calendar months after registration is made, to allow ample time for preparation.
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