Radicals.in

DATASCIENCE WITH S-A-S & PYTHON TRAINING IN KOCHI

Unleash the power of data science with our comprehensive training in Kochi, covering SAS and Python. Dive into advanced analytics techniques and machine learning algorithms using both platforms. Gain practical experience in data manipulation, statistical analysis, and predictive modeling. Learn from industry experts who offer insights into real-world applications and best practices. Collaborate with peers in hands-on projects and workshops to enhance your skills. Prepare to excel in leveraging SAS and Python for extracting actionable insights from diverse datasets. Join us and elevate your proficiency in data science to drive innovation and business success.

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Why Radical Technologies

Live Online Training

Highly practical oriented training
Installation of Software On your System
24/7 Email and Phone Support
100% Placement Assistance until you get placed
Global Certification Preparation
Trainer Student Interactive Portal
Assignments and Projects Guided by Mentors
And Many More Features
Course completion certificate and Global Certifications are part of our all Master Program

Live Classroom Training

Weekend / Weekdays / Morning / Evening Batches
80:20 Practical and Theory Ratio
Real-life Case Studies
Easy Coverup if you missed any sessions
PSI | Kryterion | Redhat Test Centers
Life Time Video Classroom Access ( coming soon )
Resume Preparations and Mock Interviews
And Many More Features
Course completion certificate and Global Certifications are part of our all Master Program

Self Paced Training

Self Paced Learning
Learn 300+ Courses at Your Own Time
50000+ Satisfied Learners
Course Completion Certificate
Practical Labs Available
Mentor Support Available
Doubt Clearing Session Available
Attend Our Virtual Job Fair
10% Discounted Global Certification
Course completion certificate and Global Certifications are part of our all Master Program

Live Online Training

Highly practical oriented training
Installation of Software On your System
24/7 Email and Phone Support
100% Placement Assistance until you get placed
Global Certification Preparation
Trainer Student Interactive Portal
Assignments and Projects Guided by Mentors
And Many More Features
Course completion certificate and Global Certifications are part of our all Master Program

Live Classroom Training

Weekend / Weekdays / Morning / Evening Batches
80:20 Practical and Theory Ratio
Real-life Case Studies
Easy Coverup if you missed any sessions
PSI | Kryterion | Redhat Test Centers
Life Time Video Classroom Access ( coming soon )
Resume Preparations and Mock Interviews
And Many More Features
Course completion certificate and Global Certifications are part of our all Master Program

Self Test Training

300+ Technologies - Learn at your Convenience
4500+ High Quality Videos
Self Paced Training By Experts
Self Paced Hands-On Practical LABS
Cloud Sand Boxes
Do Projects and Assignments with Live LABS
100+ Exam Simulators & Discounted Vouchers
Live Mentor Support - By 10+ Years Experts
Course completion certificate and Global Certifications are part of our all Master Program

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Course Benefits

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Designations

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WhyTerraform ?

Multi-Cloud Support

Terraform supports multiple cloud providers, including AWS, Azure, Google Cloud Platform (GCP), and others. This allows you to use a single tool and consistent workflow across different cloud environments, enabling hybrid and multi-cloud deployments.

Declarative Configuration

Terraform uses a declarative language to define infrastructure configurations. This means you specify the desired state of your infrastructure, and Terraform handles the provisioning and management to achieve that state. This approach is intuitive and allows for easier understanding and maintenance of infrastructure code.

Resource Graph

Terraform builds a dependency graph of your infrastructure resources based on their relationships and dependencies. This enables Terraform to determine the correct order of resource provisioning and manage complex infrastructures with ease.

Modularity and Reusability

Terraform encourages modularization and code reuse through the use of modules. Modules are self-contained units of Terraform configurations that can be reused across projects, teams, and environments. This promotes consistency, reduces duplication, and speeds up development.

State Management

Terraform maintains a state file that tracks the current state of your infrastructure. This state file is used to plan and apply changes, track resource attributes, and manage updates. Terraform's state management ensures consistency and facilitates collaboration among team members.

Plan and Apply Workflow

Terraform follows a plan and apply workflow, where changes to infrastructure are first planned and previewed before being applied. This allows you to review the proposed changes, identify potential issues, and apply changes safely, minimizing the risk of unintended consequences.

Extensibility

Terraform is highly extensible and integrates with a wide range of third-party tools and services. This includes integrations with configuration management tools, CI/CD pipelines, monitoring solutions, and more, allowing you to build comprehensive infrastructure automation workflows. While other IaC tools offer similar capabilities, Terraform's multi-cloud support, declarative configuration, resource graph, modularity, state management, workflow, and extensibility make it a popular choice for infrastructure automation in diverse environments. Additionally, Terraform's active community, robust documentation, and frequent updates contribute to its widespread adoption and continued development.

About your Terraform Certification Course

Terraform Skills Covered

  • State Management

  • Terraform Modules

  • Dependency Management

  • Terraform CLI

  • Terraform Configuration Language (HCL)

  • Infrastructure as Code (IaC) Principles

  • Resource Provisioning

  • Terraform Providers

  • Terraform Workspaces

  • Terraform Best Practices

Curriculum Designed by Experts

DATASCIENCE WITH S-A-S & PYTHON TRAINING IN KOCHI Course Syllabus

Introduction To Analytics
  • Analytics World
    • Introduction to Analytics
    • Concept of ETL
    • S-A-S in advanced analytics
  • Global Certification: Induction and walk through
    • Getting Started
    • Software installation
    • Introduction to GUI
    • Different components of the language
    • All programming windows
    • Concept of Libraries and Creating Libraries
    • Variable Attributes – (Name, Type, Length, Format, In format, Label)
    • Importing Data and Entering data manually
  • Understanding Datasets
    • Descriptor Portion of a Dataset (Proc Contents)
    • Data Portion of a Dataset
    • Variable Names and Values
    • Data Libraries
Base S-A-S – Accessing The Data
  • Understanding Data Step Processing
    • Data Step and Proc Step
    • Data step execution
    • Compilation and execution phase
    • Input buffer and concept of PDV
  • Importing Raw Data Files
    • Column Input and List Input and Formatted methods
    • Delimiters, Reading missing and non standard values
    • Reading one to many and many to one records
    • Reading Hierarchical files
    • Creating raw data files and put statement
    • Formats / Informat
  • Importing and Exporting Data (Fixed Format / Delimited)
  • Proc Import / Delimited text files
  • Proc Export / Exporting Data
  • Datalines / Cards;
  • Atypical importing cases (mixing different style of inputs)
    • Reading Multiple Records per Observation
    • Reading “Mixed Record Types”
    • Sub-setting from a Raw Data File
    • Multiple Observations per Record
    • Reading Hierarchical Files
  • Concept of SAS library and SAS Catalog
  • Variable Types in SAS
  • Reading Data stored external to SAS
  • Importing Data by using Proc Import
  • Data Step SAS statements
  • SAS Functions
  • Appending and Merging using SAS
  • SAS Procedures like proc means, proc Univariate, proc append, proc freq, and proc export.
  • SAS SQL
  • SAS Macros
Hypothesis Testing and ANOVA / Measure Model Performance

Hypothesis Testing and ANOVA

  • One Sample t-test of comparing means
  • Two Sample t-test of comparing means
  • One Way ANOVA
  • Assumptions of ANOVA Modeling
  • n-way ANOVA
  • ANOVA Post Hoc Studies

Measure Model Performance

  • Apply the principles of honest assessment to model performance measurement
  • Assess classifier performance using the confusion matrix
  • Model selection and validation using training and validation data
  • Create and interpret graphs (ROC, lift, and gains charts) for model comparison and selection
  • Establish effective decision cut-off values for scoring
Data Understanding, Managing And Manipulation
  • Understanding and Exploration Data
    • Introduction to basic Procedures – Proc Contents, Proc Print
  • Understanding and Exploration Data
    • Operators and Operands
    • Conditional Statements (Where, If, If then Else, If then Do and select when)
    • Difference between WHERE and IF statements and limitation of WHERE statements
    • Labels, Commenting
    • System Options (OBS, FSTOBS, NOOBS etc…)
  • Data Manipulation
    • Proc Sort – with options / De-Duping
    • Accumulator variable and By-Group processing
    • Explicit Output Statements
    • Nesting Do loops
    • Do While and Do Until Statement
    • Array elements and Range
  • Combining Datasets (Appending and Merging)
    • Concatenation
    • Interleaving
    • Proc Append
    • One To One Merging
    • Match Merging
    • IN = Controlling merge and Indicator