Cloudera Data Scientist Training

Cloudera Data Scientist Training is designed for professionals aiming to gain the advanced skills needed to build, analyze, and deploy machine learning models in large-scale data environments. This course covers data science methodologies using Cloudera Data Platform (CDP) and focuses on applying Apache Spark, Python, R, and other big data tools for building scalable, high-performance data science applications. Participants will explore data wrangling, statistical modeling, machine learning, and real-time data analysis, gaining hands-on experience in deploying AI/ML models within a data science workflow. This training is ideal for those looking to advance their careers in the field of data science and analytics.

Schedule & Fee
Learning Objectives
Prerequisites
Target Audience
Course Modules
FAQs

Schedule	Learners	Course Fee (Incl. of all Taxes)	Register Your Interest
December 22^nd - 25^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours) Guaranteed-to-Run		10% Off $1,600 $1,440 Fast Filling! Hurry Up.
December 27^th - 04^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		10% Off $1,600 $1,440
January 05^th - 08^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 10^th - 18^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 12^th - 15^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 19^th - 28^th 06:00 AM - 10:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours)		20% Off $1,600 $1,280
January 26^th - 29^th 09:00 AM - 05:00 PM (CST) Live Virtual Classroom (Duration : 32 Hours) Guaranteed-to-Run		20% Off $1,600 $1,280

Course Prerequisites

Strong understanding of Python or R programming
Familiarity with Apache Spark and big data frameworks
Basic knowledge of machine learning algorithms and techniques
Experience with data analysis, data wrangling, and statistics
Understanding of cloud computing and distributed computing systems is beneficial

Learning Objectives

By the end of this course, participants will be able to:

Build end-to-end data science workflows using Cloudera Data Platform (CDP)
Preprocess and wrangle large-scale datasets for machine learning and analysis
Apply machine learning algorithms to real-world big data problems
Utilize Apache Spark and Python for distributed machine learning tasks
Develop deep learning models and work with advanced AI techniques
Implement real-time analytics and deploy machine learning models at scale
Ensure ethical, responsible, and compliant AI/ML practices in data science workflows

Target Audience

This course is ideal for professionals who wish to deepen their expertise in data science and machine learning. The target audience includes:

Data Scientists
Machine Learning Engineers
Data Analysts
Big Data Engineers
AI/ML Researchers
Professionals seeking a transition into data science and machine learning

Course Modules

Introduction to Data Science with Cloudera
- Overview of Cloudera Data Platform (CDP) and its capabilities for data science
- Key concepts of data science: data preparation, statistical analysis, and predictive modeling
- Introduction to Apache Spark and its use in large-scale data science workflows
Data Wrangling and Preprocessing for Data Science
- Techniques for data cleaning, transformation, and normalization using Python and Spark
- Working with structured and unstructured data using Apache Hive and Parquet
- Managing missing data, outliers, and data imbalances
Statistical Analysis and Data Visualization
- Applying statistical methods for exploratory data analysis (EDA)
- Creating data visualizations using Python, Matplotlib, and Seaborn
- Drawing inferences from visual data analysis and presenting insights
Machine Learning with Spark and Python
- Implementing supervised and unsupervised learning models with Spark MLlib
- Model selection, evaluation, and tuning in a distributed environment
- Building classification, regression, and clustering models for big data
Advanced Machine Learning Techniques
- Working with deep learning models and frameworks like TensorFlow and Keras
- Implementing Natural Language Processing (NLP) for text data analysis
- Exploring reinforcement learning for decision-making systems
Big Data Analytics and Real-Time Processing
- Real-time data processing with Apache Spark Streaming and Kafka
- Streamlining real-time data pipelines and applying machine learning models in production
- Optimizing big data workflows for scalable and efficient analytics
Model Deployment and Monitoring
- Deploying machine learning models on Cloudera Data Platform
- Model monitoring, versioning, and continuous learning strategies
- Using MLflow for managing machine learning life cycles
Ethics and Responsible AI in Data Science
- Understanding the ethical implications of machine learning and AI
- Addressing fairness, bias, and transparency in data science applications
- Complying with industry regulations and standards for AI/ML systems

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Trustpilot

What Our Learners Are Saying

The training, courseware, and lab experience were insightful and valuable. Keep up the great work and learning experience!

Nitish A. Anand – Accenture

Course: SC-200: Microsoft Security Operations Analyst
Date: 15th Jan 2025

The instructor was professional and very content.

Justine Daudi Mlimbilah – Bank of Africa, Tanzania

Course: MD-102: Microsoft 365 Endpoint Administrator
Date: 20th Dec 2024

The instructor was so knowledgeable & humble. Rare to find someone so confident but so down to earth these days. So appreciative to him.”

Mohd. Hassan – Ministry of Finance, UAE

Course: AZ-700: Designing and Implementing Microsoft Azure Networking Solutions
Date: 31st July 2024

Instructor is experienced and knowledgeable in guiding.

Dharshini Mahalaxmi – Dr. MGR Education and Research Institute, Chennai, India

Course: SC-300: Microsoft Identity and Access Administrator
Date: 4th May 2024