Description Exam DP-100: Designing and Implementing a Data Science Solution on Azure Skills measured Set up an Azure Machine Learning workspace (30-35%) Run experiments and train models (25-30%) Optimize and manage models (20-25%) Deploy and consume models (20-25%) Detail Skills Define and prepare the development environment (15-20%) Select development environment assess the deployment environment constraints analyze and recommend tools that meet system requirements select the development environment Set up development environment create an Azure data science environment configure data science work environments Quantify the business problem define technical success metrics quantify risks Prepare data for modeling (25-30%) Transform data into usable datasets develop data structures design a data sampling strategy design the data preparation flow Perform Exploratory Data Analysis (EDA) review visual analytics data to discover patterns and determine next steps identify anomalies, outliers, and other data inconsistencies create descriptive statistics for a dataset Cleanse and transform data resolve anomalies, outliers, and other data inconsistencies standardize data formats set the granularity for data Perform feature engineering (15-20%) Perform feature extraction perform feature extraction algorithms on numerical data perform feature extraction algorithms on non-numerical data scale features Perform feature selection define the optimality criteria apply feature selection algorithms Develop models (40-45%) Select an algorithmic approach determine appropriate performance metrics implement appropriate algorithms consider data preparation steps that are specific to the selected algorithms Split datasets determine ideal split based on the nature of the data determine number of splits determine relative size of splits ensure splits are balanced Identify data imbalances resample a dataset to impose balance adjust performance metric to resolve imbalances implement penalization Train the model select early stopping criteria tune hyper-parameters Evaluate model performance score models against evaluation metrics implement cross-validation identify and address overfitting identify root cause of performance results Data Scientist is most demanded skill of this era. Certified Data Scientist get more chance to get hired than non-certified candidate. Who this course is for: Anyone who want to write DP-100 Certification exam Continue reading...
