The CertNexus Certified Data Science Practitioner course is designed to empower business professionals with the knowledge and skills needed to harness the power of data science for solving business issues and driving informed decision-making. Throughout this comprehensive program, learners will delve into the fundamental processes of data science, from extracting and transforming data to analyzing and presenting it effectively.
Through a series of engaging modules, participants will gain proficiency in addressing business challenges with data science techniques, understanding the data science lifecycle, and leveraging statistical analysis methods to gain valuable insights. They will also learn to prepare and clean data for analysis, train machine learning models, and communicate results effectively.
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This course comprises modules that cover essential aspects of data science, including addressing business issues, data extraction and transformation, data analysis, machine learning model training, and finalizing a data science project.
This module is designed to equip business professionals with the skills to determine if a business issue is appropriate for a data science project and apply the data science process effectively. Participants will gain a high-level understanding of fundamental data science concepts, types of data, and the overall data science lifecycle.
Business and data professionals seeking to learn the technical phase of the data science process, known as Extract, Transform, and Load (ETL), will benefit from this module. Learners will acquire the ability to collect, prepare, clean, and load data from multiple sources for analysis and modeling.
In this module, business professionals will learn how to analyze data to gain insights, explore data distributions using statistical analysis methods, and use visualizations to analyze and preprocess data effectively. Participants will develop proficiency in preparing datasets for training.
Designed for business professionals, this module delves into the identification of basic concepts in machine learning, testing model hypothesis, and training, tuning, and evaluating models using algorithms that solve various data problems such as classification, regression, forecasting, and clustering.
In the final module, participants will learn to communicate model results via web apps, and implement and test pipelines that automate the model training, tuning, and deployment process, thereby finalizing a data science project effectively.
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