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.

home-icon

The 1-on-1 Advantage

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

Flexible Dates

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

4-Hour Sessions

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

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
  • 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)
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
Yes, we do.
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.
Yes, this is our official email address which we use if a recipient is not able to receive emails from our @koenig-solutions.com email address.
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.
Yes you can.
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.
Yes, you can pay from the course page and flexi page.
Yes, the site is secure by utilizing Secure Sockets Layer (SSL) Technology. SSL technology enables the encryption of sensitive information during online transactions. We use the highest assurance SSL/TLS certificate, which ensures that no unauthorized person can get to your sensitive payment data over the web.
We use the best standards in Internet security. Any data retained is not shared with third parties.
You can request a refund if you do not wish to enroll in the course.
To receive an acknowledgment of your online payment, you should have a valid email address. At the point when you enter your name, Visa, and other data, you have the option of entering your email address. Would it be a good idea for you to decide to enter your email address, confirmation of your payment will be emailed to you.
After you submit your payment, you will land on the payment confirmation screen.It contains your payment confirmation message. You will likewise get a confirmation email after your transaction is submitted.
We do accept all major credit cards from Visa, Mastercard, American Express, and Discover.
Credit card transactions normally take 48 hours to settle. Approval is given right away; however,it takes 48 hours for the money to be moved.
Yes, we do accept partial payments, you may use one payment method for part of the transaction and another payment method for other parts of the transaction.
Yes, if we have an office in your city.
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

Yes of course.
Yes, We are

Travel and Visa

Yes we do after your registration for course.

Food and Beverages

Yes.

Others

Says our CEO-
“It is an interesting story and dates back half a century. My father started a manufacturing business in India in the 1960's for import substitute electromechanical components such as microswitches. German and Japanese goods were held in high esteem so he named his company Essen Deinki (Essen is a well known industrial town in Germany and Deinki is Japanese for electric company). His products were very good quality and the fact that they sounded German and Japanese also helped. He did quite well. In 1970s he branched out into electronic products and again looked for a German name. This time he chose Koenig, and Koenig Electronics was born. In 1990s after graduating from college I was looking for a name for my company and Koenig Solutions sounded just right. Initially we had marketed under the brand of Digital Equipment Corporation but DEC went out of business and we switched to the Koenig name. Koenig is difficult to pronounce and marketeers said it is not a good choice for a B2C brand. But it has proven lucky for us.” – Says Rohit Aggarwal (Founder and CEO - Koenig Solutions)
All our trainers are fluent in English . Majority of our customers are from outside India and our trainers speak in a neutral accent which is easily understandable by students from all nationalities. Our money back guarantee also stands for accent of the trainer.
Medical services in India are at par with the world and are a fraction of costs in Europe and USA. A number of our students have scheduled cosmetic, dental and ocular procedures during their stay in India. We can provide advice about this, on request.
Yes, if you send 4 participants, we can offer an exclusive training for them which can be started from Any Date™ suitable for you.