Cloudera Data Scientist Course Overview

Cloudera Data Scientist Course Overview

The Cloudera Data Scientist course is a comprehensive training program designed to equip learners with the essential skills and knowledge to embark on a career in data science. Focused on the Cloudera Data Science Workbench (CDSW), the course covers a wide array of topics, from the basics of data science, the processes, and tools used by data scientists, to in-depth tutorials on Apache Spark, machine learning, and working with big data ecosystems.

Throughout the course, learners will delve into modules that explore how to process, analyze, and draw insights from large datasets using various Cloudera technologies. The hands-on lessons include working with data frames, executing Spark applications, building machine learning pipelines, and even deploying these models. Those who complete the Cloudera Data Scientist training will have the practical experience and theoretical knowledge to tackle real-world data challenges and harness the power of big data using Cloudera Data Science tools and methodologies.

This is a Rare Course and it can be take up to 3 weeks to arrange the training.

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  • Live Online Training (Duration : 32 Hours)
  • Per Participant

♱ Excluding VAT/GST

Classroom Training price is on request

You can request classroom training in any city on any date by Requesting More Information

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Course Prerequisites

To successfully undertake the Cloudera Data Scientist course, students should have the following minimum prerequisites:

  • Basic understanding of programming concepts, preferably in Python, as it is commonly used for data science tasks.
  • Familiarity with command-line operations in Linux, as data scientists often interact with systems and software through the command line.
  • Knowledge of fundamental statistics, as they form the basis for many data science algorithms and analytical processes.
  • Experience with SQL and relational databases, as data scientists need to retrieve and manipulate data stored in these systems.
  • An introductory level understanding of machine learning concepts and algorithms, which will be built upon throughout the course.
  • Some exposure to data handling and processing, including working with large datasets, which is a core part of a data scientist's role.

These prerequisites are meant to ensure that participants can effectively grasp the course material and practical applications. However, motivated learners with a strong desire to immerse themselves in the field of data science are encouraged to take the course, as foundational skills can be developed along the way with additional effort and study.

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.