DATA SCIENCE AND BIG DATA ANALYTICS

About the Course

This five-day, foundational program is designed to transform technical professionals into effective contributors to big data projects. Taking a technology-neutral or “Open” approach, the course focuses on the conceptual and practical frameworks required to turn massive datasets into actionable business intelligence. You won’t just learn how to code; you’ll learn the Data Analytics Lifecycle, a structured methodology used by practicing data scientists to ensure their models solve real-world business problems.

The curriculum balances rigorous statistical theory with hands-on technical training. You will explore advanced analytical methods—ranging from clustering and regression to machine learning algorithms like Decision Trees and Naïve Bayes. Additionally, the course introduces the “Big Data” stack, providing an essential grounding in the Hadoop Ecosystem and MapReduce. The experience culminates in a comprehensive final lab where you apply the entire lifecycle to a complex data challenge, preparing you for the Proven Professional Data Scientist Associate (DECA-DS) certification.

Audience Profile

This course is intended for:

• Aspiring Data Scientists looking for a structured entry point into the field.

• Data & Business Analysts wanting to scale their skills from traditional BI to predictive analytics.

• Database Professionals interested in exploiting their SQL knowledge within Big Data environments.

• Managers who lead teams of data professionals and need to understand the lifecycle of an analytics project.

• Recent Graduates with a quantitative background seeking to enter the data science job market.

Learning Objectives

The Data Analytics Lifecycle

• Master the six phases of the lifecycle: Discovery, Data Prep, Model Planning, Model Building, Communicating Results, and Operationalization.

• Learn how to frame a business problem as an analytics challenge.

Statistical Modeling and Machine Learning

• Build and evaluate predictive models using Linear and Logistic Regression.

• Implement unsupervised learning techniques like K-means Clustering and Association Rules.

• Utilize classification algorithms including Decision Trees and Naïve Bayes.

• Perform Time Series Analysis and Text Analytics on unstructured data.

Big Data Technology Stack

• Understand the architecture of the Hadoop Ecosystem and how it enables distributed processing.

• Explore In-database analytics using SQL and MADlib to process data where it resides.

• Use R and RStudio for data exploration, visualization, and statistical modeling.

Communication and Visualization

• Apply data visualization techniques to make complex findings accessible to stakeholders.

• Learn how to operationalize a model and present project results to drive business decisions.

Prerequisites

• Quantitative Skills: A strong foundation in basic statistics (e.g., hypothesis testing, probability).

• Programming: Experience with a scripting language (Python, Java, or Perl). Note: Labs primarily use R.

• Database: Fundamental knowledge of SQL is required for the in-database analytics modules.