DATA SCIENCE AND BIG DATA ANALYTICS Course Details

About the Course

This comprehensive technical program provides a practical, foundational transition into the world of data science and advanced analytics. As organizations increasingly rely on data-driven decision-making, this course equips participants with the skills to identify, evaluate, and solve complex business challenges using large-scale datasets. The curriculum follows an “Open” or technology-neutral approach, ensuring that the concepts mastered are applicable across various platforms and industry tools.

Students will gain a deep understanding of the Data Analytics Lifecycle, moving from initial data discovery and preparation to sophisticated model building and operationalization. The course balances theoretical grounding in statistics and machine learning with hands-on laboratory exercises using industry-standard tools like R and Hadoop. By completing this program, learners are prepared to contribute immediately as productive members of a data science team and are positioned for professional certification in the field.

Audience Profile

This course is intended for:

• Business and Data Analysts looking to expand their skill sets into big data environments.

• Managers of Analytics Teams who need to understand the technical workflows of their specialists.

• Database Professionals interested in leveraging their SQL knowledge for advanced predictive modeling.

• Graduates and Academic Researchers aiming to pivot into a professional data science career.

• Candidates preparing for associate-level data scientist professional certifications.

Learning Objectives

Project Management and Methodology

• Apply the Data Analytics Lifecycle to structure and manage large-scale projects from inception to deployment.

• Define business challenges and translate them into actionable data science problems.

• Prepare project presentations and utilize data visualization techniques to communicate complex findings to stakeholders.

Statistical and Analytic Methods

• Utilize the R programming language for data exploration, statistical analysis, and model evaluation.

• Implement advanced analytic methods, including K-means clustering, association rules, and decision trees.

• Build and validate predictive models using linear and logistic regression, Naïve Bayes, and time series analysis.

Technology and Tool Proficiency

• Work with large datasets within the Hadoop ecosystem, including MapReduce and HDFS.

• Execute in-database analytics using advanced SQL and MADlib.

• Perform text analysis to derive insights from unstructured data sources.

Certification Exam

This course serves as the primary preparation for the Associate – Data Science Version 2.0 (D-DS-AS-01) exam. Achieving this certification validates a candidate’s fundamental knowledge of data science and their ability to participate effectively in big data projects as a practicing data scientist.

Prerequisites

• A strong quantitative background equivalent to a college-level introductory statistics course.

• Basic experience with a scripting language (Python, Perl, or Java); specific familiarity with R is highly beneficial as it is used in lab exercises.

• Fundamental knowledge of SQL for data querying and manipulation.