Learn Data Science with Cloudera Data Scientist Course

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Cloudera Data Scientist Course Overview


The Cloudera Data Scientist course is a hands-on program that teaches aspiring data scientists how to use Apache Hadoop to explore, analyze and visualize large datasets. It is designed to provide a comprehensive introduction to data analysis with Hadoop, enabling you to become a proficient data scientist in a short amount of time.
The course is organized into five modules and the topics covered include topics such as: Introduction to Apache Hadoop, Apache Hive and Impala, data management and manipulation, Apache Spark, and machine learning. Students will gain experience working with large data sets and gain a better understanding of how to use tools such as Apache Hive, Apache Spark, and Impala to explore, analyze, and visualize data.
At the end of the course, participants have the knowledge and skills required to confidently extract data from large datasets, perform analysis on them, and visualize the data in meaningful, easy-to-understand formats. This is invaluable knowledge that’s essential to any aspiring data scientist.

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

Cloudera Data Scientist
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The 1-on-1 Advantage

Get 1-on-1 session with our expert trainers at a date & time of your convenience.
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Flexible Dates

Start your session at a date of your choice-weekend & evening slots included, and reschedule if necessary.
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4-Hour Sessions

Training never been so convenient- attend training sessions 4-hour long for easy learning.
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Destination Training

Attend trainings at some of the most loved cities such as Dubai, London, Delhi(India), Goa, Singapore, New York and Sydney.

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) 1600 + If you accept merging of other students. Per Participant & excluding VAT/GST
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)
Group Training
Online
05 - 08 Jun 09:00 AM - 05:00 PM CST
(8 Hours/Day)
Online
03 - 06 Jul 09:00 AM - 05:00 PM CST
(8 Hours/Day)
Cloudera Data Scientist
Course Prerequisites

The following are the prerequisites for Cloudera Data Scientist Training:
• Strong programming experience with languages such as Python or Java
• Experience using data structures and algorithms
• Experience using databases (SQL, NoSQL)
• Experience using distributed systems (Hadoop, Spark, etc.)
• Knowledge of machine learning techniques
• Familiarity with Linux/Unix systems and command-line tools
• Familiarity with text processing concepts

Target Audience


The Cloudera Data Scientist Training is geared towards professionals who have backgrounds in data science, analytics, and related technologies and who are looking to develop advanced skills in modern data science and machine learning
This includes professionals such as data scientists, software engineers, statisticians, and analysts with a keen interest in exploring and mastering the most current tools and technologies related to data management and analytics
These professionals need to be comfortable working with data models and designing, developing, and testing data science applications with Big Data technologies
A familiarity with programming languages like Python, R and Java, and experience with tools such as Spark, Kafka, Hadoop and Hive is a plus
Additionally, professionals should have strong problem-solving and communication skills to effectively partner with technical and non-technical stakeholders
Participants should have a basic understanding of the fundamentals of data science, the type of questions and business problems data science professionals are expected to solve, and the analytics methodologies utilized to draw meaningful insights

Learning Objectives of Cloudera Data Scientist


1. Understand distributed data processing concepts and technologies like HDFS, MapReduce, YARN, and HBase.
2. Gain an understanding of Cloudera's tools, including Cloudera Manager, Impala, and Hive.
3. Understand the basics of data science and machine learning, including supervised and unsupervised learning methods.
4. Apply data science to real-world scenarios using Cloudera's open-source tools, such as Apache Spark and Apache Kafka.
5. Learn to create Spark applications and to work with Hive, Impala, Kafka, and HDFS.
6. Utilize Cloudera's tools to transform data into insights through advanced analytics, including using HBase and other NoSQL databases.
7. Get an introduction to big data-driven architecture and operations and the basics of data management in the cloud.
8. Develop an understanding of real-time streaming data, real-time analytics, and machine learning techniques.
9. Create innovative applications and analytics projects, protecting user data and optimizing data pipelines.
10. Get hands-on experience with Cloudera's machine learning and data engineering tools.

Student Feedback  (Check Koenig Feedback on Trustpilot)

Q1 Say something about the Trainer? Q2 How is Koenig different from other training Companies? Q3 Will you come back to Koenig for training ?
on Trust Pilot
Student Name Feedback
Saikat Bhattacharya
United States
A1. Many thanks to Gurleen Ma'am for her patient and inspired guidance, and very grateful for getting to learn from her being a subject-matter expert.

FAQ's

Yes, course requiring practical include hands-on labs.
Yes, we do offer corporate training More details
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Yes, we also offer weekend classes.
Yes, Koenig follows a BYOL(Bring Your Own Laptop) policy.
1-on-1 Public - Select your start date. Other students can be merged.
1-on-1 Private - Select your start date. You will be the only student in the class.
It is recommended but not mandatory. Being acquainted with the basic course material will enable you and the trainer to move at a desired pace during classes.You can access courseware for most vendors.
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Buy-Now. Pay-Later option is available using credit card in USA and India only.
You will receive the digital certificate post training completion via learning enhancement tool after registration.
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Yes, we do. For details go to flexi
You can pay through debit/credit card or bank wire transfer.
Yes you can request your customer experience manager for the same.
You can buy online from the page by clicking on "Buy Now". You can view alternate payment method on payment options page.
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You can request a refund if you do not wish to enroll in the course.
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Yes, fee excludes local taxes.
Yes, we do.
Yes, Koenig Solutions is a Cloudera Learning Partner
Schedule for Group Training is decided by Koenig. Schedule for 1-on-1 is decided by you.
In 1 on 1 Public you can select your own schedule, other students can be merged. Choose 1-on-1 if published schedule doesn't meet your requirement. If you want a private session, opt for 1-on-1 Private.
Duration of Ultra-Fast Track is 50% of the duration of the Standard Track. Yes(course content is same).

Prices & Payments

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Travel and Visa

Yes we do after your registration for course.

Food and Beverages

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