Certified Data Science Practitioner (CDSP) Certification

Objectives

• Gain a comprehensive understanding of the data science lifecycle, including data acquisition, data preparation, exploratory data analysis, model building, evaluation, and deployment.
• Learn various data science techniques, algorithms, and methodologies, such as regression analysis, classification, clustering, time series analysis, and natural language processing.
• Develop proficiency in programming languages commonly used in data science, such as Python or R, and understand how to leverage their libraries and frameworks for data analysis and machine learning.
• Master data visualization techniques to effectively communicate insights and findings derived from data analysis and modeling.
• Gain practical knowledge of data preprocessing and feature engineering techniques to clean and transform raw data for analysis and modeling purposes.
• Understand the concepts of machine learning and deep learning, and learn how to build, train, and evaluate predictive models using appropriate algorithms and methodologies.
• Learn how to evaluate the performance of machine learning models using metrics like accuracy, precision, recall, and F1 score, and understand techniques for model selection and optimization.
• Gain knowledge of statistical concepts and methods relevant to data science, including hypothesis testing, experimental design, and statistical inference.