Software Finishing School

It is almost impossible to escape the impact frontier technologies are having on everyday life. At the core of this impact are the advancements of artificial intelligence and deep learning.These technologies are ushering in a revolution that will fundamentally alter the way we live,work, and communicate akin to the industrial revolution – more specifically, AI and Deep Learning is the new industrial revolution. Companies across industries seek to use advanced computational techniques to find useful information hidden across huge swaths of data.While the field of artificial intelligence is decades old, breakthroughs in the field of Artificial Neural Networks (ANN) are driving the explosion of deep learning.

Pre-requisites of the Course

• Knowledge of python programming
• Knowledge of Machine Learning.
  1. Concept of Overfit, Underfit, Training and Test set.
  2. Regression, Classification.

Curriculum

• Understanding Deep Learning
• Understanding Neural Network and TensorFlow
• Deep dive into Neural Networks and TensorFlow
• Convutionsl Neural Network
• Recurrent Neural Network
• Restricted Boltzmann Machine RBM and Autoencoders
• Keras
• Tflearn
• Project

[Delivery Mode: Summer Training Program/Winter Training Program]

The key advantage of machine learning is that it enables computers to access hidden insights, finding patterns that can either be used by researchers to find unknown patterns (as might be used for movie recommendation) or by businesses to find insights into customer behaviour or to target potential new consumers. Machine learning not only helps find things that people may not, it also does what people do far more quickly. Machine learning algorithms tend to operate at expedited levels. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.

Pre-requisites of the course

• Good knowledge at algorithms development
• Basic knowledge of statistics and conditional probability
• Understanding of linear algebra: vector operations, matrix operation

Curriculum

• Getting started with Python
• Number Processing with Numpy
• Database operations with Pandas
• Data Visualization with Matplotlib and Seaborn
• Fundamentals of Machine Learning
• Hello World of Machine Learning :Perceptron algorithm using Numpy
• Implementing Regression using Scikit-learn Module LinearRegression
• Classification using scikit-learn : LogisticRegression
• Feature Selection : importance and implementation using Scikit-learn
• Non Linear classification using Decision Tree
• RandomForest and ideas of Bootstraping and Bagging
• Classification using Baysian Theory and Naïve-Bayes
• Non Parametric Machine Learning Algorithm : K nearest neighbour(kNN)
• Unsupervised learning using K-means cluster
• Project

[Delivery Mode: Summer Training Program/Winter Training Program]

Internet of Things can create a world where in each and every device in your home, workplace and car are connected. “Internet of things” takes the world to the next level by providing advanced interaction between devices, actuation and automation systems and services. It is not a second internet, but is a network of devices that are connected to the internet. It is said to be the evolution of internet architecture. The IoT is expected to offer incredible potential for business opportunities. It creates the potential not just for creating market transformation, but also creates market disruption. Moreover, these devices can be used to collect current and real time information that is more meaningful and actionable to the business.

Curriculum

• IOT system architecture
• IOT Phy connectivity
• Short, Medium, Long range wireless
• IOT Networking
• IOT Security
• Project

[Delivery Mode: Summer Training Program/Winter Training Program]

Cloud computing facilitates success of an enterprise where AWS is the frequently used cloud tool. Most enterprises leverages AWS that can be best customized to fit their IT environment. The reputed players like Netflix, Reddit, Expedia and even NASA run their applications on AWS. The program will enable a computer science graduates to understand various aspects of AWS in terms of its Architecture, Services and Applications. After attending this course, a fresher becomes more relevant not only to the IT industry but other sectors also.

Pre-requisites of the Course

• Knowledge of programming knowledge using C

Curriculum

• Fundamentals of Cloud Computing
• Cloud Infrastructure and Cloud Advantage
• Azure Fundamentals
• Overview of all Azure Services
• Azure Storage
• Storage Account Rep
• Fundamentals of Cloud Computing
• Cloud Infrastructure and Cloud Advantage
• Azure Fundamentals
• Overview of all Azure Services
• Azure Storage
• Storage Account Replication Techniques: LRS, ZRS, GRS & RA-GRS
• Azure Virtual Network
• Architectural difference between Azure VPNs like VNET to VNET, point-to-site and site-to-site.
• Azure Virtual Machines
• Understanding concepts of: Load Balancing, Availability Set and Auto Scaling
• Azure Web Apps
• Resource Group and App Service Plans
• Azure SQL Database
• Advance Capabilities
• lication Techniques: LRS, ZRS, GRS & RA-GRS
• Azure Virtual Network
• Architectural difference between Azure VPNs like VNET to VNET, point-to-site and site-to-site.
• Azure Virtual Machines
• Understanding concepts of: Load Balancing, Availability Set and Auto Scaling
• Azure Web Apps
• Resource Group and App Service Plans
• Azure SQL Database
• Advance Capabilities
• Project

[Delivery Mode: Corporate]

This course equips students with essential skills in computer hardware and networking. From understanding system components to mastering networking fundamentals, students gain hands-on experience vital for success in the IT industry. Students begin by exploring the foundational aspects of computer hardware, including components such as the Central Processing Unit (CPU), mainboard, and peripherals. Through hands-on activities, they learn to assemble, troubleshoot, and maintain computer systems effectively. Moving beyond hardware, the course delves into networking fundamentals, covering topics such as network topologies, devices, transmission media, and data communication principles. Students gain a deep understanding of how networks function and acquire the skills needed to set up and manage robust network infrastructures.

Pre-requisites of the Course

• Basic understanding of computers and technology

Curriculum

• Computer Fundamentals & Basics of Power System in a Computer
• Introduction to SMPS & UPS
• Introduction to Basic Input Output System
• Introduction to Operating System
• Computer Management
• Disk Partitioning
• Central Processing Unit & Main Board
• Primary and Secondary Memory
• Computer Accessories
• Virus/ Malware
• Data Communications and Networking
• Network Topologies, Networking Devices, Transmission Media, Sharing of Resources
• Internet & Internet Network Configuration

[Delivery Mode: Corporate]

This course provides an in-depth understanding of big data analytics, covering both theoretical concepts and practical applications. Students will learn how to process, analyze, and extract valuable insights from large datasets using various tools and techniques. Topics include Data Pre-processing, Machine Learning Algorithms, Distributed Computing, and Cloud-based Analytics. Project work at the end of course will enhance students’ skills in handling big data challenges.

Pre-requisites of the Course:

• Must have programming capabilities
• Proficiency in Python
• Concept of exploratory statistics

Curriculum

• Introduction to Big Data and Hadoop

–   Understanding the significance of BigData in Modern Business and Technology
–   Overview of Hadoop Ecosystem Components: HDFS, MapReduce, YARN
–   Installation and Setup of Hadoop on a Local Machine

• Hadoop Distributed File System (HDFS)

–   Deep Dive into HDFS Architecture and Data Storage
–   File Operations, Replication, and Fault Tolerance
–   Managing Large-scale Data with HDFS

• MapReduce Programming

–   MapReduce Concepts and Principles
–   Writing Mapreduce Jobs in Java
–   Hands-on Exercises for Data Processing using MapReduce

• Data Ingestion and ETL with Sqoop

–   Importing and Exporting Data between Hadoop and Relational Databases
–   Sqoop Commands and Best Practices
–   Real-world Scenarios for Data Integration

• Hive and Data Warehousing

–   Creating and Querying Tables using Hive
–   Hiveql Syntax and Optimization Techniques
–   Building Data Warehouses on Hadoop

• Apache Pig for Data Processing

–   Introduction to Pig Scripting Language
–   Writing Pig Latin Scripts for Data Transformation
–   Analyzing Large Datasets with Pig

• Apache Spark Basics

–   Spark Architecture and RDD (Resilient Distributed Datasets)
–   Spark Transformations and Actions
–   Spark SQL for Querying Structured Data

• Advanced Topics in Hadoop

–   Working with NoSQL Databases (HBase, Cassandra)
–   Introduction to Apache Kafka for Real-time Data Streaming
–   Security and authentication in Hadoop Clusters

[Delivery Mode: Summer Training Program/Winter Training Program/Corporate]

Data Analytics is important in business to understand problems facing an organisation, and to explore data in meaningful ways. Data in itself is merely facts and figures. Data analysis organises, interprets, structures and presents the data into useful information that provides context for the data. This context can then be used by decision-makers to take action with the aim of enhancing productivity and business gain. Python language provides necessary tools and techniques for the purpose of data analysis.

Pre-requisites of the Course

• Programming knowledge using C, C ++
• Knowledge of SQL will be beneficial

Curriculum

• Python Language Basics
• Build-in Data Structures, Function
• Numpy Basics: Array and Vectorized Computation
• Pandas
• Data Loading
• Data Cleaning and Preparation
• Data Wrangling: Join Combine, Reshape
• Plotting
• Data Aggregation and Group Operations
• Introductory Statistics
• StatModels : Statistical Model creation
• Project

[Delivery Mode: Summer Training Program/Winter Training Program]

According to StatCounter, market share of Android is 76.61 %. At the same time number of people using smartphone to theirs day to day work is increasing. It is expected to reach 2.7 billion by 2019. These fact underlines the importance of Android App Development as any business organization will try reach customer through Android powered app. This course will take the students through the various intricacies of Android App Development using Studio.

Pre-requisites of the Course

• Knowledge in JAVA & SQL is essential for the course

Curriculum

• Revising Java
• Android Architecture
• Developing a simple Activity
• Android Intents
• Android Menu
• Activity lifecycle
• Fragments
• List View
• Material Design Controls
• Thread
• Service
• Broadcast Receiver
• Notification
• Location  and Google Map
• Firebase
• Project

[Delivery Mode: Summer Training Program/Winter Training Program]

This comprehensive course introduces students to the world of full stack application development using the Spring framework. Full stack developers play a crucial role in building end-to-end web applications, from designing user interfaces to implementing server-side logic and integrating with databases. In this course, students will gain hands-on experience in both front-end and back-end development, equipping them with the skills needed to create robust and scalable applications.

Pre-requisites of the Course:

• Proficiency in coding
• Proficiency in Basic Web Technologies including HTML, CSS, JavaScript

Curriculum

• Introduction to Full Stack Development

–   Understanding the Role of Full Stack Developers
–   Overview of Front-end and Back-end Technologies
–   Introduction to Spring Framework and its Components

• Spring Boot Basics

–   Setting up a Spring Boot Project
–   Creating RESTful APIs using Spring Boot
–   Handling Requests and Responses

• Front-End Development with React

–   Introduction to React and its Components
–   Building User Interfaces with React
–   State Management and Component Lifecycle

• Spring MVC

–   Creating Dynamic Web Pages using Spring MVC
–   Integrating Thymeleaf Templates
–   Form Handling and Validation

• Database Interaction with Spring Data JPA

–   Connecting to Databases using Spring Data JPA
–   Defining Entities and Repositories
–   Querying Data with JPA

• Security and Authentication

–   Implementing Authentication and Authorization
–   Securing REST Endpoints
–   Using Spring Security for User Management

• RESTful Services and Microservices

–   Designing REST APIs
–   Consuming External APIs
–   Building Microservices with Spring Cloud

• Testing and Debugging

–   Writing Unit Tests for Spring Components
–   Debugging Techniques for Full Stack Applications
–   Integration Testing with Spring Boot

• Deployment and DevOps

–   Deploying Spring Applications to Cloud Platforms (e.g., AWS, Heroku)
–   Continuous Integration and Continuous Deployment (CI/CD)
–   Monitoring and Logging Best Practices

[Delivery Mode: Summer Training Program/Winter Training Program]

Cybersecurity and ethical hacking are crucial for securing online data, user authentication, and privacy protection. Ethical hackers focus on identifying and resolving security vulnerabilities, while cybersecurity experts aim to protect networks and data from unauthorized access. Cybersecurity and ethical hacking are more important than ever because of the rise in online data risks and attacks. Professionals in cyber security and ethical hackers are in high demand.

Pre-requisites Of the course

• Knowledge of basic programming in C/C++/Java

CURRICULUM

• Introduction to Cyber Security & Ethical Hacking
• Cyber Law & Case Studies
• Information Security & Cryptography
• Virtualization & Penetration Testing
• Footprinting & Reconnaissance
• Security & Hacking using C programming
• Computer Virus & Malware Threats
• Setup, Penetration Testing & Reporting of a Web Application
• Cyber Attacks & Precautions
• Computer Forensics & Investigative Process
• How to use the Kali Linux
• Project

[Delivery Mode: Summer Training Program/Winter Training Program]

Every business has a range of important data called business intelligence — financial records, manufacturing quotas, sales data and logistical information, distribution logistics, and more.
Put simply, business intelligence is any information about your business that enables you to make better decisions and optimize performance. Microsoft SQL Server is considered one of the leaders in database management and business intelligence. The SQL Server enables the user to utilize their choice of language and platform in the cloud or on-premises. The databases are also easily scalable, contributing to the SQL Server being top-of-the-line.

Pre-requisites Of the course

• Knowledge of C++ and MS SQL programming

CURRICULUM

• Introduction to RDBMS
• Data Types and Constraints
• Querying and Filtering Data
• Grouping and Summarizing Data
• Working with Subqueries
• Joining Data from Multiple Tables
• Creating Tables and modifying Data in Tables
• Other Database Objects
• System and user-defined Stored procedures
• Cursors
• Trigger
• MSBI and SSIS Architecture Overview
• SSIS Control Flow and Data Flow
• SSIS Advance Topics & DWH Basics
• SSAS Cube Design
• SSAS Deployment and MDX, DAX
• SSRS Reporting
• Project

[Delivery Mode: Summer Training Program/Winter Training Program]

In this dynamic course, learners will embark on a journey into the cutting-edge realms of Web 3.0 and Blockchain technologies. As the internet evolves beyond its current state, understanding the foundational principles and practical applications of Web 3.0 and Blockchain becomes indispensable. This course is designed to provide learners with an in-depth understanding of Web 3.0 technologies and Blockchain applications. The course will cover the principles of Web 3.0, the fundamentals of Blockchain technology, and how these technologies can be integrated for various applications.

Pre-requisites of the Course

• Basic Programming Skills: Students should have a fundamental understanding of programming concepts such as variables, data types, loops, conditionals, functions, and object-oriented programming principles. Proficiency in at least one programming language (e.g., Python, JavaScript, Java, C++) is recommended.
• Computer Science Fundamentals: A basic understanding of computer science principles is essential for comprehending the underlying technologies of Web 3.0 and Blockchain. Topics include data structures, algorithms, computational complexity, and basic networking concepts.
• Internet Technologies: Familiarity with internet protocols (e.g., HTTP, TCP/IP) and web development concepts (e.g., HTML, CSS, JavaScript) is beneficial for understanding the evolution of the internet and the transition to Web 3.0 paradigms.
• Cryptographic Fundamentals: An introductory knowledge of cryptographic principles is advantageous for grasping the security mechanisms employed in blockchain technology. Topics may include symmetric and asymmetric encryption, hash functions, digital signatures, and cryptographic hashing algorithms.
• Curiosity and Eagerness to Learn: Web 3.0 and Blockchain technologies represent cutting-edge domains that continue to evolve rapidly. Students should possess a strong desire to explore emerging technologies, engage in independent research, and adapt to new concepts and paradigms.
• Critical Thinking and Problem-Solving Skills: The ability to analyze complex problems, think critically, and formulate creative solutions is essential for understanding the implications and applications of Web 3.0 and Blockchain technologies across various domains.

Curriculum

• Introduction to Web 3.0

–   Definition and Evolution of Web 3.0
–   Characteristics and Features
–   Importance and Benefits

• Basics of Blockchain Technology

–  Introduction to Blockchain
–  Types of Blockchains
–  Decentralization and Security Aspects

• Smart Contracts and Decentralized Applications (DApps)

–   Smart Contracts Overview
–   Creating and Executing Smart Contracts
–   Introduction to Decentralized Applications

• Web 3.0 and Blockchain Integration

–   Use cases of Web 3.0 and Blockchain Integration
–   Benefits and Challenges
–   Practical examples

• Hands-on Workshops and Case Studies

–   Implementing Blockchain in Existing Systems
–   Developing DApps
–   Analyzing Case Studies of successful Blockchain Applications

• Advanced Topics in Web 3.0

–   AI Integration with Web 3.0
–   Internet of Things (IoT) and Blockchain
–   Future Trends and Possibilities

[Delivery Mode: Corporate]

In today’s data-driven world, the ability to effectively visualize and communicate insights from data is paramount. This comprehensive course on Data Visualization using PowerBI equips participants with the skills and knowledge needed to harness the full potential of Microsoft Power BI for creating compelling and insightful visualizations.

Pre-requisites of the Course

• Basic Computer Literacy: Participants should have basic computer skills, including familiarity with operating systems (Windows), file management, and software installation.
• Fundamental Data Analysis Knowledge: A foundational understanding of data analysis concepts is beneficial, including familiarity with terms such as data types, data sources, data manipulation, and basic statistical measures (mean, median, mode, etc.).
• Microsoft Excel Proficiency: Basic proficiency in Microsoft Excel is recommended as many data visualization principles translate from Excel to PowerBI. Participants should be comfortable performing tasks such as data entry, formatting, basic formulas/functions, and chart creation in Excel.
• Understanding of Business Intelligence Concepts: While not mandatory, a basic understanding of business intelligence (BI) concepts such as data warehousing, data modeling, and reporting will facilitate comprehension of PowerBI’s capabilities and terminology.
• Curiosity and Eagerness to Learn: A strong desire to explore data visualization techniques and leverage PowerBI to derive insights from data is essential. Participants should be motivated to engage actively in the learning process and apply newfound knowledge to real-world scenarios.
• Access to PowerBI Software: Participants should have access to Microsoft PowerBI software, either through a personal or organizational subscription. PowerBI Desktop is available as a free download from the Microsoft website, while PowerBI Pro may be required for certain advanced features and collaboration capabilities.

Curriculum

• Introduction to Data Visualization

–   Overview of the Course
–   Importance of Data Visualization
–   Introduction to PowerBI

• Fundamentals of Data Visualization

–  Terminology in Data Visualization
–  Principles of Effective Data Visualization
–  Examples of Good and Bad Visualizations

• Basic Visualizations in PowerBI

–   Bar Charts
–   Line Charts
–   Pie Charts
–   Scatter Plots

• Intermediate Visualizations in PowerBI

–   Treemaps
–   Heatmaps
–   Waterfall Charts
–   Combo Charts

• Creating Dashboards in PowerBI

–   Dashboard Design Principles
–   Interactivity and Drill-down Features
–   Dashboard Customization and Branding

• Advanced Data Analysis in PowerBI

–   Calculated Columns and mMasures
–   DAX Expressions
–   Time Intelligence Functions

• Data Modeling in PowerBI

–   Relationships between Data Tables
–   Hierarchies and Categorization
–   Advanced Data Modeling Techniques

• Integrating External Data Sources in PowerBI

–   Importing Data from Excel
–   Connecting to Databases
–   Web Data Sources and API Integration

• Data Visualization Best Practices

–   Color Theory and Visualization Aesthetics
–   Storytelling through Data
–   Accessibility and Inclusive Design

• Data Visualization Case Studies

–   Real-world Examples of Effective Data Visualization
–   Hands-On Exercises using Sample Datasets
–   Group Projects for Practical Application

• Project Presentations and Assessment

–   Students showcase their Final Projects
–   Peer Evaluation and Feedback Session
–   Assessment of Learning Outcomes

[Delivery Mode: Summer Training Program/Winter Training Program]

This course is designed for professionals, executives, students, and enthusiasts interested in leveraging prompt engineering techniques to enhance generative AI outcomes. Whether you’re curious about language models or want to dive deeper into AI, this course provides practical knowledge and hands-on experience. Generative AI and Prompt Engineering is a course that provides students with a comprehensive understanding of artificial intelligence models and algorithms used for generating creative and innovative outputs. The course will cover various techniques and approaches in prompt engineering, which plays a crucial role in fine-tuning the outputs generated by AI systems. Students will explore different applications of generative AI in various fields such as art, music, literature, and more.

Pre-requisites of the Course

• Students should be familiar with basic concepts of machine learning, including supervised and unsupervised learning, optimization algorithms, and model evaluation techniques.
• Deep Learning: Understanding neural networks, including feedforward, convolutional, and recurrent neural networks, is essential. Knowledge of common deep learning frameworks like TensorFlow or PyTorch is beneficial.
• Probability and Statistics: Proficiency in probability theory, statistics, and linear algebra is necessary for understanding the mathematical underpinnings of generative models and evaluating their performance.
• Programming Skills: Strong programming skills in a language commonly used for machine learning and deep learning such as Python are required. Students should be comfortable writing and debugging code, working with libraries like NumPy, Pandas, and Matplotlib, and implementing machine learning algorithms.
• Natural Language Processing (NLP) Basics (optional): Familiarity with basic concepts in natural language processing, such as tokenization, word embeddings, and sequence modeling, can be helpful for understanding text generation tasks.
• Image Processing Basics (optional): Basic knowledge of image processing techniques, such as convolutional operations, image representation, and feature extraction, can aid in understanding image generation tasks.

Curriculum

• Introduction to Generative AI and Prompt Engineering

–   Define Generative AI and Prompt Engineering
–   Discuss the History and Evolution of Generative AI

Resources:
“Artificial Intelligence: A Modern Approach” by Stuart Russell and Peter Norvig
“Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

• Techniques in Generative AI

–   Classify different Generative Models: GANs, VAEs, RNNs, etc.
–   Discuss the Strengths and Weaknesses of each Model

Resources:
“Generative Adversarial Networks” by Ian Goodfellow and Yoshua Bengio
“Recurrent Neural Networks in Python” by Thushan Ganegedara

• Applications of Generative AI

–   Analyze case studies in art, music, storytelling, etc.
–   Implement basic generative models using Python

Resources:
“Artificial Unintelligence: How Computers Misunderstand the World” by Meredith Broussard
“The Anatomy of AI” by Kate Crawford

• Prompt Engineering

–   Define Prompt Engineering and its Relevance in Generative AI
–   Explore different Prompt Engineering Techniques

Resources:
“The Art of Interactive Design” by Chris Crawford
“The Design of Everyday Things” by Don Norman

• Implementing Generative AI Models

–   Hands-on coding sessions to create generative outputs
–   Troubleshooting common issues in generative AI implementations

• Ethical Considerations in Generative AI

–   Evaluate the Impact of Generative AI on Society

[Delivery Mode: Summer Training Program/Winter Training Program/Corporate]

The focus of this course is not just on Excel skills but on formulating problems, translating them into useful models, optimizing and displaying those models, and interpreting results. It’s designed for managers who appreciate quantitative approaches to decision-making and are comfortable with trade-offs.

Pre-requisites of the Course

• This course is designed for intermediate-level learners.
• While no specific prerequisites are mandatory, it’s beneficial if you have a basic understanding of Excel for day-to-day tasks.
• The most important prerequisite is your willingness to learn and empower yourself.
• No prior knowledge of statistics, modeling, or optimization is required.
• Having an analytical mindset will enhance your learning experience.

Curriculum

• Introduction to Business Analytics

–   Course Overview
–   Introduction to EXCEL for Analytics
–   Understanding the importance of Data-driven Decision Making

• Data Collection and Cleaning in EXCEL

–  Data Types and Formats
–  Data Cleaning and Pre-processing Techniques
–  Exploring Excel Data Analysis Tools

• Descriptive Analytics using EXCEL

–   Descriptive Statistics
–   Data Visualization Techniques in EXCEL
–   Creating Dashboards and Reports for Data Presentation

• Predictive Analytics in EXCEL

–   Regression Analysis in EXCEL
–   Time Series Analysis
–   Forecasting Models in EXCEL

• Prescriptive Analytics and Optimization

–   Decision Trees and Scenario Analysis
–   Goal Seek and Solver in EXCEL
–   Linear Programming Models using EXCEL

• Advanced Data Analysis with EXCEL

–   Advanced Functions and Formulas for Data Manipulation
–   Data Mining Techniques in EXCEL
–   Cluster Analysis and Segmentation in EXCEL

• Business Intelligence and Reporting

–   Power Query and Power Pivot in EXCEL
–   Creating Dynamic Reports and Dashboards
–   Introduction to EXCEL VBA for Automation

• Machine Learning in EXCEL

–   Introduction to Machine Learning
–   Implementing ML Algorithms in EXCEL
–   Evaluating ML Models in EXCEL

• Text and Sentiment Analysis in EXCEL

–   Text Mining Techniques
–   Sentiment Analysis using EXCEL
–   Case Studies and Applications

• Real-world Applications and Case Studies

–   Integration of Analytics in Business Decision making
–   Industry-specific Applications of Business Analytics
–   Project Presentations and Discussions

[Delivery Mode: Corporate]