Cloudera Data Scientist Course Overview

Target Audience for Cloudera Data Scientist

The Cloudera Data Scientist course equips participants with essential skills for leveraging big data using Cloudera's platform.

Target Audience:

• Aspiring Data Scientists
• Current Data Analysts looking to upskill
• Software Engineers aiming to transition into data science roles
• IT Professionals with an interest in machine learning and big data
• Data Engineers who want to understand data science processes
• Business Analysts seeking to apply data science in decision-making
• Data Science Consultants who want to expand their service offerings
• BI Developers needing to incorporate big data analytics into their skillset
• System Administrators responsible for maintaining data science platforms
• Product Managers looking to leverage data science for product improvement
• Research Scientists who want to apply data science techniques to their research data
• Cloudera Platform Users who need to understand the data science capabilities of the platform

Learning Objectives - What you will Learn in this Cloudera Data Scientist?

Introduction to the Course's Learning Outcomes and Concepts Covered

This Cloudera Data Scientist course equips participants with the practical skills and knowledge needed to analyze, process, and model big data using Cloudera's tools, with an emphasis on Apache Spark and machine learning techniques.

Learning Objectives and Outcomes

• Understand the role and processes used by data scientists to extract insights from large datasets.
• Gain proficiency in Cloudera Data Science Workbench (CDSW) for developing and deploying data science solutions.
• Learn to perform data manipulation, summarization, and exploration using Apache Spark’s SQL and DataFrames.
• Develop skills in writing and optimizing Spark applications for big data processing.
• Master the use of window functions for advanced analytical queries on structured data.
• Acquire the ability to preprocess text data and build topic modeling with Latent Dirichlet Allocation (LDA).
• Design, train, and evaluate recommender systems and regression models using Spark MLlib.
• Construct and deploy end-to-end machine learning pipelines in Cloudera's environment.
• Gain familiarity with complex data types and user-defined functions to extend Spark SQL capabilities.
• Understand the process of tuning machine learning models through hyperparameter optimization using grid search.