Cloudera Data Scientist

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Cloudera Data Scientist Course Overview

Enroll for our 4-day Cloudera Data Scientist training from Koenig Solutions accredited by Cloudera.  In this course you will learn enterprise data science and machine learning using Apache Spark in Cloudera Data Science Workbench (CDSW).

Through a blend of hands-on labs and interactive lectures, you will learn to use Spark SQL to load, explore, cleanse, join, and analyze data and Spark MLlib to specify, train, evaluate, tune, and deploy machine learning pipelines. They dive into the foundations of the Spark architecture and execution model necessary to effectively configure, monitor, and tune their Spark applications. Participants also learn how Spark integrates with key components of the Cloudera platform such as HDFS, YARN, Hive, Impala, and Hue as well as their favorite Python or R packages.

Target Audience:

This course is intended for Data scientists, Data engineers, data analysts, developers, and solution architects

Learning Objectives:

After completing this course, you will be able to:

  • How to use Apache Spark to run data science and machine learning workflows at scale
  • How to use Spark SQL and DataFrames to work with structured data
  • How to use MLlib, Spark’s machine learning library
  • How to use PySpark, Spark’s Python API
  • How to use sparklyr, a dplyr-compatible R interface to Spark
  • How to use Cloudera Data Science Workbench (CDSW)
  • How to use other Cloudera platform components including HDFS, Hive,Impala, and Hue

 

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

  • 1. Do you have limited Window for training?
  • 2. Can you only spend 4-hours per day?
  • 3. Do you want to start training immediately?
  • If your answer is yes to any one of the above, you need 1-on-1- Training
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You will learn:

Module 1: Data Science Overview
  • What Data Scientists Do
  • What Process Data Scientists Use
  • What Tools Data Scientists Use
  • Introduction to Cloudera Data
  • How Cloudera Data Science
  • How to Use Cloudera Data Science
  • Entering Code
  • Getting Help
  • Accessing the Linux Command Line
  • Working with Python Packages
  • Formatting Session Output
  • DuoCar
  • How DuoCar Works
  • DuoCar Datasets
  • DuoCar Business Goals
  • DuoCar Data Science Platform
  • DuoCar Cloudera EDH Cluster
  • HDFS
  • Apache Spark
  • Apache Hive
  • Apache Impala
  • Hue
  • YARN
  • DuoCar Cluster Architecture
  • Apache Spark
  • How Spark Works
  • The Spark Stack
  • Spark SQL
  • DataFrames
  • File Formats in Apache Spark
  • Text File Formats
  • Parquet File Format
  • Summarizing Data with Aggregate
  • Functions
  • Grouping Data
  • Pivoting Data
  • Introduction to Window Functions
  • Creating a Window Specification
  • Aggregating over a Window Specification
  • Possible Workflows for Big Data
  • Exploring a Single Variable
  • Exploring a Categorical Variable
  • Exploring a Continuous Variable
  • Exploring a Pair of Variables
  • Categorical-Categorical Pair
  • Categorical-Continuous Pair
  • Continuous-Continuous Pair
  • DataFrame Operations
  • Input Splits
  • Narrow Operations
  • Wide Operations
  • Stages and Tasks
  • Shuffle
  • Introduction to Topic Models
  • Scenario
  • Extracting and Transforming Features
  • Parsing Text Data
  • Removing Common (Stop) Words
  • Counting the Frequency of Words
  • Specifying a Topic Model
  • Training a topic model using Latent Dirichlet Allocation (LDA)
  • Assessing the Topic Model Fit
  • Examining a Topic Model
  • Applying a Topic Model
  • Introduction to Recommender Models
  • Scenario
  • Preparing Data for a Recommender Model
  • Specifying a Recommender Model
  • Spark Interface Languages
  • PySpark
  • Data Science with PySpark
  • sparklyr
  • dplyr and sparklyr
  • Comparison of PySpark and sparklyr
  • How sparklyr Works with dplyr
  • sparklyr DataFrame and MLlib Functions
  • When to Use PySpark and sparklyr
  • Overview
  • Starting a Spark Application
  • Reading Data into a Spark SQL Data Frame
  • Examining the Schema of a Data Frame
  • Computing the Number of Rows and
  • Examining Rows of a DataFrame
  • Stopping a Spark Application
  • Overview
  • Inspecting a DataFrame
  • Inspecting a DataFrame Column
  • Inspecting a Primary Key Variable
  • Inspecting a Categorical Variable
  • Inspecting a Numerical Variable
  • Inspecting a Date and Time Variable
  • Spark SQL DataFrames
  • Working with Column
  • Selecting Column
  • Dropping Columns
  • Specifying Columns
  • Adding Columns
  • Changing the Column Name
  • Changing the Column Type
  • Monitoring Spark Applications
  • Persisting DataFrames
  • Partitioning DataFrames
  • Configuring the Spark Environment
  • Machine Learning
  • Underfitting and Overfitting
  • Model Validation
  • Hyperparameters
  • Supervised and Unsupervised Learning
  • Machine Learning Algorithms
  • Machine Learning Libraries
  • Apache Spark MLlib
  • Introduction to Regression Models
  • Scenario
  • Preparing the Regression Data
  • Assembling the Feature Vector
  • Creating a Train and Test Set
  • Specifying a Linear Regression Model
  • Training a Linear Regression Model
  • Examining the Model Parameters
  • Examining Various Model Performance Measures
  • Examining Various Model Diagnostics
  • Applying the Linear Regression Model to the Test Data
  • Evaluating the Linear Regression Model on the Test Data
  • Plotting the Linear Regression Model
  • Training a Recommender Model using Alternating Least Squares
  • Examining a Recommender Model
  • Applying a Recommender Model
  • Evaluating a Recommender Model
  • Generating Recommendations
  • Specifying Pipeline Stages
  • Specifying a Pipeline
  • Training a Pipeline Model
  • Querying a Pipeline Model
  • Applying a Pipeline Model
  • Saving and Loading Pipelines and Pipeline Models in Python
  • Loading Pipelines and Pipeline Models in Scala
  • Working with Rows
  • Ordering Rows
  • Selecting a Fixed Number of Rows
  • Selecting Distinct Rows
  • Filtering Rows
  • Sampling Rows
  • Working with Missing Values
  • Spark SQL Data Types
  • Working with Numerical Columns
  • Working with String Columns
  • Working with Date and Timestamp Columns
  • Working with Boolean Columns
  • Complex Collection Data Types
  • Arrays
  • Maps
  • Structs
  • User-Defined Functions
  • Defining a Python Function
  • Registering a Python Function as a
  • User-Defined Function
  • Applying a User-Defined Function
  • Reading and Writing Data
  • Working with Delimited Text Files
  • Working with Text Files
  • Working with Parquet Files
  • Working with Hive Tables
  • Working with Object Stores
  • Working with pandas DataFrames
  • Joining DataFrames
  • Cross Join
  • Inner Join
  • Left Semi Join
  • Left Anti Join
  • Left Outer Join
  • Right Outer Join
  • Full Outer Join
  • Applying Set Operations to
  • DataFrames
  • Splitting a DataFrame
  • Introduction to Classification Models
  • Scenario
  • Preprocessing the Modeling Data
  • Generate a Label
  • Extract, Transform, And Select Features
  • Create Train and Test Sets
  • Specify A Logistic Regression Model
  • Train the Logistic Regression Model
  • Examine the Logistic Regression Model
  • Evaluate Model Performance on the Test Set
  • Requirements for Hyperparameter Tuning
  • Specifying the Estimator
  • Specifying the Hyperparameter Grid
  • Specifying the Evaluator
  • Tuning Hyperparameters using Holdout Cross-validation
  • Tuning Hyperparameters using K-fold Cross-validation
  • Introduction to Clustering
  • Scenario
  • Preprocessing the Data
  • Extracting, Transforming, and Selecting Features
  • Specifying a Gaussian Mixture Model
  • Training a Gaussian Mixture Model
  • Examining the Gaussian Mixture Model
  • Plotting the Clusters
  • Exploring the Cluster Profiles
  • Saving and Loading the Gaussian
  • Mixture Model
  • Connecting to Spark
  • Reading Data
  • Inspecting Data
  • Transforming Data Using dplyr Verbs
  • Using SQL Queries
  • Spark DataFrames Functions
  • Visualizing Data from Spark
  • Machine Learning with MLlib
  • Collaboration
  • Jobs
  • Experiments
  • Models
  • Applications
Live Online Training (Duration : 32 Hours) Fee On Request
Group Training
Online
05 - 08 Sep GTR 09:00 AM - 05:00 PM CST
(8 Hours/Day)
03 - 06 Oct GTR 09:00 AM - 05:00 PM CST
(8 Hours/Day)
We Offer :
  • 1-on-1 Public - Select your own start date. Other students can be merged.
  • 1-on-1 Private - Select your own start date. You will be the only student in the class.
4 Hours
8 Hours
Week Days
Weekend

Start Time : At any time

12 AM
12 PM

1-On-1 Training is Guaranteed to Run (GTR)
Physical Classroom Training with 4 hours /8 hours option is available. Enquire Now!
Can’t Attend Live Online? Buy Flexi For Only $199 (Video Recording of Live Online)
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Ultra-Fast Track

If you can't spare 32 hours. We can offer you an Ultra-Fast Track for 16 hours
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Course Prerequisites

Participants should have a basic understanding of Python or R and some experience exploring and analyzing data and developing statistical or machine learning models. Knowledge of Hadoop or Spark is not required.

 

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The Fee includes:
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Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1-on-1 you can select your own schedule, other students can be merged but you select the schedule. Choose 1-on-1 if published schedule do not meet your requirement. If you also want a private session, opt for 1-on-1 Public.
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