FLEXI - SELF PACED TRAINING RE-IMAGINED

Do you Need Live Online Training? Do you not have Time for Live Online? Do you Want to Start Immediately? Koenig's Flexi can help You.

Flexi is a Video Recording of Live Online + Official Courseware + Hands-On-Labs + Qubits Test

Flexi is a Unique Union of Live Online and On-Demand Learning Options.
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Cloudera Data Scientist

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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

We do not have a fresh Live Online Recording for the course. It can take 4-5 days to edit a recording. If your need is urgent, request for an un-edited version.


Cloudera Data Scientist is a Rare course. Hence priced at $199. Other Flexis are for $99.

Flexi Video

USD 199

Official Courseware

N/A

Hands-On-Labs

NA

Total

USD 199

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FAQ's

Q1. How long do you have access to Flexi after purchase?

A. 3 months from date of delivery.

Q2. Can the content be shared or is it only available for one user?

A. It is only for the self-use of the purchaser.

Q3. Do you have business packages that you offer to companies that would make use of these packages on a regular basis?

A. Yes, contact us for corporate packages.

Q4. What is the pass rate of students that used Flexi as their method of preparation?

A. We do not track the pass rate of Flexi students. We, however suspect that it will be lower than for Live Online.

Q5. If we buy Flexi today, and there are any updates in the course, do we get the updated recordings as well or do we need to repurchase Flexi?

A. If the latest update comes within 3 months after the sale we can give the updated version.

Q6. Can we download the videos or we stream them online?

A. Videos can only be streamed and not downloaded.

Q7. Do you have courses in languages other than English?

A. Presently Flexi is only available in English.

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