Cloudera Data Science at Scale using Spark and Hadoop (CDSSH)

Quantity

$2,395.00

Add to Cart

New Age Technologies has been delivering Authorized Training since 1996. We offer Cloudera’s full suite of authorized courses including courses pertaining to Apache Spark, Hadoop, Apache HBase, MapReduce, Data Science, Big Data Applications and more. If you have any questions or can’t seem to find the Cloudera class that you are interested in, contact one of our Cloudera Training Specialists. Invest in your future today with Cloudera training from New Age Technologies.

✉ Cloudera Training Specialists | ☏ 502.909.0819

---

Cloudera Data Science at Scale using Spark and Hadoop Overview:

In the Cloudera Data Science at Scale using Spark and Hadoop course, you will learn how Spark and Hadoop enable data scientists to help companies reduce costs, increase profits, improve products, retain customers, and identify new opportunities. You will apply data science methods to real world challenges in different industries and prepare for data scientist roles in the field.

Who Should Attend:

• Developers, data analysts, and statisticians with basic knowledge of Apache Hadoop: HDFS, MapReduce, Hadoop Streaming, and Apache Hive

Cloudera Data Science at Scale using Spark and Hadoop Prerequisites:

Before attending this course, you must have the following:

• Proficiency in a scripting language; Python is strongly preferred, but familiarity with Perl or Ruby is sufficient

Cloudera Data Science at Scale using Spark and Hadoop Objectives:

After successfully completing this course, you will learn such topics as:

• How to identify potential business use cases where data science can provide impactful results
• How to obtain, clean and combine disparate data sources to create a coherent picture for analysis
• What statistical methods to leverage for data exploration that will provide critical insight into your data
• Where and when to leverage Hadoop streaming and Apache Spark for data science pipelines
• What machine learning technique to use for a particular data science project
• How to implement and manage recommenders using Spark’s MLlib, and how to set up and evaluate data experiments
• What are the pitfalls of deploying new analytics projects to production, at scale

Cloudera Data Science at Scale using Spark and Hadoop Certification:

• Cloudera Certified Professional: Data Scientist (CCP: Data Scientist)

Cloudera Data Science at Scale using Spark and Hadoop Outline:

Module 1: Data Science Overview

• What Is Data Science?
• The Growing Need for Data Science
• The Role of a Data Scientist

Module 2: Use Cases

• Finance
• Retail
• Advertising
• Defense and Intelligence
• Telecommunications and Utilities
• Healthcare and Pharmaceuticals

Module 3: Project Lifecycle

• Steps in the Project Lifecycle
• Lab Scenario Explanation

Module 4: Data Acquisition

• Where to Source Data
• Acquisition Techniques

Module 5: Evaluating Input Data

• Data Formats
• Data Quantity
• Data Quality

Module 6: Data Transformation

• File Format Conversion
• Joining Data Sets
• Anonymization

Module 7: Data Analysis and Statistical Methods

• Relationship Between Statistics and Probability
• Descriptive Statistics
• Inferential Statistics
• Vectors and Matrices

Module 8: Fundamentals of Machine Learning

• Overview
• The Three C’s of Machine Learning
• Importance of Data and Algorithms
• Spotlight: Naive Bayes Classifiers

Module 9: Recommender Overview

• What is a Recommender System?
• Types of Collaborative Filtering
• Limitations of Recommender Systems
• Fundamental Concepts

Module 10: Introduction to Apache Spark and MLlib

• What is Apache Spark?
• Comparison to MapReduce
• Fundamentals of Apache Spark
• Spark’s MLlib Package

Module 11: Implementing Recommenders with MLlib

• Overview of ALS Method for Latent Factor Recommenders
• Hyperparameters for ALS Recommenders
• Building a Recommender in MLlib
• Tuning Hyperparameters
• Weighting

Module 12: Experimentation and Evaluation

• Designing Effective Experiments
• Conducting an Effective Experiment
• User Interfaces for Recommenders

Module 13: Production Deployment and Beyond

• Deploying to Production
• Tips and Techniques for Working at Scale
• Summarizing and Visualizing Results
• Considerations for Improvement
• Next Steps for Recommenders

---

Average Salary for Skill: Data Mining / Data Warehouse