Gandhipuram, Coimbatore, India - 641012
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MM Towers 3 rd Floor
3 rd Cross cut road
Gandhipuram, Coimbatore, India - 641012
Landmark: Opposite to Mahavir Silks
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Class Location
Online (video chat via skype, google hangout etc)
At the Institute
Teaches
Java Real Time Projects, Java Full Stack Developer, J2EE, Web services, Hibernate, Spring, Core Java, Servlet, JSP (Java Server Pages)
Certification training offered
Yes
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
.Net component
.Net Web Services, Appdynamics, C# .NET, .Net XML, Microsoft .Net 4, .Net MVC, ASP.NET, .Net WF, .Net AJAX, .Net WPF, ADO.NET, ASP.NET AJAX, .Net Advanced, .Net Nuke, .Net LINQ, .Net WCF
Certification offered
Yes
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
Course Duration provided
3-6 months, 1-3 months
Seeker background catered to
Corporate company, Educational Institution, Individual
Certification provided
Yes
Python applications taught
Data Analysis with Python , Scipy Stack with Python , Web Scraping with Python , GUI (Graphical User Interfaces) with Python , Text Processing with Python, Core Python, PySpark, Regular Expressions with Python , Networking with Python , Machine Learning with Python, Game Development with Python, Data Science with Python, Web Development with Python , Data Extraction with Python , Help in assignment, Data Visualization with Python, Testing with Python
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
Data science techniques
Python, Java, Artificial Intelligence, Machine learning
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
Deep_Learning_Techniques
Tensorflow, Python
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
Teaches web designing at proficiency level
Advanced Web Designing, Basic Web Designing
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
BCA Subject
Software Engineering , Web Programming, Internet Concepts and Web Design , Programming in C++ , IT, Database Management Systems , Java Programming, C Language Programming
Type of class
Crash Course, Regular Classes
Class strength catered to
One on one/ Private Tutions
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
Training for ISTQB certification
No
Testing Types
Manual Software Testing, Automated Software Testing, Mobile Automated Software Testing
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
AWS Certification offered
AWS Certified SysOps Administrator, AWS Certified Developer, AWS Certified DevOps Engineer, AWS Certified Solutions Architect
AWS Certification Level offered
Associate, Professional
1. Do you provide Java Certification?
Yes
2. Which classes do you teach?
We teach .Net Training, Amazon Web Services, BCA Tuition, Data Science, Deep Learning, Java Training, Python Training, Software Testing and Web Designing Classes
3. Do you provide a demo class?
Yes, We provide a free demo class.
4. Where are you located?
We are located in Gandhipuram, Coimbatore.
Answered 23 hrs ago Learn IT Courses/Data Science
Data Science is a broad and interdisciplinary field that encompasses a variety of topics. Here are the key areas typically covered in Data Science:
### 1. **Mathematics and Statistics**
- **Probability Theory**: Understanding the fundamentals of probability, random variables, and probability distributions.
- **Statistical Inference**: Techniques for making inferences about populations based on sample data, including hypothesis testing and confidence intervals.
- **Linear Algebra**: Essential for understanding data structures, transformations, and many machine learning algorithms.
- **Calculus**: Used for optimizing algorithms and understanding changes in functions, especially in the context of machine learning and neural networks.
### 2. **Programming**
- **Programming Languages**: Proficiency in languages such as Python and R, which are widely used in data science for data manipulation, statistical analysis, and machine learning.
- **Software Development**: Basic principles of software development, including version control (e.g., Git), testing, and debugging.
### 3. **Data Manipulation and Analysis**
- **Data Cleaning and Preprocessing**: Techniques for handling missing data, outliers, and ensuring data quality.
- **Exploratory Data Analysis (EDA)**: Using statistical graphics and other data visualization methods to explore and summarize data sets.
### 4. **Machine Learning**
- **Supervised Learning**: Algorithms for regression and classification, such as linear regression, logistic regression, decision trees, and support vector machines.
- **Unsupervised Learning**: Clustering algorithms like k-means, hierarchical clustering, and dimensionality reduction techniques like PCA (Principal Component Analysis).
- **Deep Learning**: Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and frameworks like TensorFlow and PyTorch.
- **Model Evaluation and Validation**: Techniques for assessing the performance of machine learning models, such as cross-validation, ROC curves, and confusion matrices.
### 5. **Data Engineering**
- **Database Systems**: Understanding relational databases (SQL) and NoSQL databases (e.g., MongoDB).
- **Data Warehousing**: Concepts and tools for storing and managing large amounts of data.
- **ETL (Extract, Transform, Load)**: Processes for extracting data from various sources, transforming it into a suitable format, and loading it into a data warehouse.
### 6. **Big Data Technologies**
- **Hadoop**: Framework for distributed storage and processing of large data sets.
- **Spark**: Engine for big data processing that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.
### 7. **Data Visualization**
- **Tools**: Proficiency in visualization tools and libraries such as Matplotlib, Seaborn, Plotly, and Tableau.
- **Best Practices**: Principles for effective data visualization and storytelling with data.
### 8. **Domain Knowledge and Applications**
- **Business Acumen**: Understanding business problems and translating them into data science problems.
- **Specialized Domains**: Knowledge of specific domains such as finance, healthcare, marketing, etc., to apply data science techniques effectively.
### 9. **Ethics and Privacy**
- **Data Ethics**: Understanding the ethical implications of data collection, analysis, and use.
- **Privacy and Security**: Ensuring data privacy and security, adhering to regulations like GDPR (General Data Protection Regulation).
### 10. **Communication**
- **Data Storytelling**: Skills for presenting data insights in a compelling and understandable manner to non-technical stakeholders.
- **Reporting**: Creating clear and concise reports and dashboards that convey data findings effectively.
These topics form the foundation of data science, and expertise in these areas enables data scientists to extract meaningful insights from data, develop predictive models, and support decision-making processes in various domains.
Answered 5 days ago Learn IT Courses/Data Science
Data science is a versatile field that finds applications across various domains and industries. Some of the common domains where data scientists work include:
1. **Healthcare**: Data scientists in healthcare analyze medical records, clinical trials data, and patient demographics to improve patient care, optimize treatment plans, and develop predictive models for disease diagnosis and prognosis.
2. **Finance**: In finance, data scientists work on tasks such as risk management, fraud detection, algorithmic trading, credit scoring, and customer segmentation. They use data to identify market trends, assess investment opportunities, and enhance financial decision-making processes.
3. **Retail and E-commerce**: Data scientists help retail companies and e-commerce platforms optimize pricing strategies, forecast demand, personalize recommendations, and improve supply chain management. They analyze customer behavior, transaction data, and inventory levels to drive sales and enhance customer experience.
4. **Marketing and Advertising**: Data scientists in marketing and advertising leverage data to target the right audience, measure campaign effectiveness, and optimize marketing spend. They use techniques like customer segmentation, sentiment analysis, and attribution modeling to maximize the impact of marketing efforts.
5. **Telecommunications**: In the telecommunications industry, data scientists analyze network data, customer usage patterns, and customer feedback to improve service quality, optimize network performance, and develop predictive maintenance models for infrastructure.
6. **Manufacturing and Supply Chain**: Data scientists help manufacturing companies optimize production processes, predict equipment failures, and minimize downtime. They also work on supply chain optimization, inventory management, and logistics planning to streamline operations and reduce costs.
7. **Energy and Utilities**: Data scientists in the energy sector analyze data from sensors, smart meters, and weather forecasts to optimize energy generation, distribution, and consumption. They develop predictive maintenance models for equipment and infrastructure to improve reliability and efficiency.
8. **Government and Public Policy**: Data scientists in government agencies and public policy organizations analyze data to inform decision-making, improve public services, and address societal challenges. They work on projects related to urban planning, transportation, healthcare policy, and public safety.
9. **Technology and Internet Companies**: Data scientists in technology and internet companies work on a wide range of tasks, including user behavior analysis, recommendation systems, natural language processing, and image recognition. They help improve product features, enhance user experience, and drive innovation.
10. **Education**: In the education sector, data scientists analyze student performance data, learning outcomes, and educational resources to personalize learning experiences, identify at-risk students, and improve educational outcomes.
These are just a few examples of the diverse domains where data scientists can make valuable contributions. The skills and techniques used in data science are applicable across industries, making data scientists in high demand in today's data-driven world.
Answered 5 days ago Learn IT Courses/Data Science
Data science is a versatile field that finds applications across various domains and industries. Some of the common domains where data scientists work include:
1. **Healthcare**: Data scientists in healthcare analyze medical records, clinical trials data, and patient demographics to improve patient care, optimize treatment plans, and develop predictive models for disease diagnosis and prognosis.
2. **Finance**: In finance, data scientists work on tasks such as risk management, fraud detection, algorithmic trading, credit scoring, and customer segmentation. They use data to identify market trends, assess investment opportunities, and enhance financial decision-making processes.
3. **Retail and E-commerce**: Data scientists help retail companies and e-commerce platforms optimize pricing strategies, forecast demand, personalize recommendations, and improve supply chain management. They analyze customer behavior, transaction data, and inventory levels to drive sales and enhance customer experience.
4. **Marketing and Advertising**: Data scientists in marketing and advertising leverage data to target the right audience, measure campaign effectiveness, and optimize marketing spend. They use techniques like customer segmentation, sentiment analysis, and attribution modeling to maximize the impact of marketing efforts.
5. **Telecommunications**: In the telecommunications industry, data scientists analyze network data, customer usage patterns, and customer feedback to improve service quality, optimize network performance, and develop predictive maintenance models for infrastructure.
6. **Manufacturing and Supply Chain**: Data scientists help manufacturing companies optimize production processes, predict equipment failures, and minimize downtime. They also work on supply chain optimization, inventory management, and logistics planning to streamline operations and reduce costs.
7. **Energy and Utilities**: Data scientists in the energy sector analyze data from sensors, smart meters, and weather forecasts to optimize energy generation, distribution, and consumption. They develop predictive maintenance models for equipment and infrastructure to improve reliability and efficiency.
8. **Government and Public Policy**: Data scientists in government agencies and public policy organizations analyze data to inform decision-making, improve public services, and address societal challenges. They work on projects related to urban planning, transportation, healthcare policy, and public safety.
9. **Technology and Internet Companies**: Data scientists in technology and internet companies work on a wide range of tasks, including user behavior analysis, recommendation systems, natural language processing, and image recognition. They help improve product features, enhance user experience, and drive innovation.
10. **Education**: In the education sector, data scientists analyze student performance data, learning outcomes, and educational resources to personalize learning experiences, identify at-risk students, and improve educational outcomes.
These are just a few examples of the diverse domains where data scientists can make valuable contributions. The skills and techniques used in data science are applicable across industries, making data scientists in high demand in today's data-driven world.
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
Teaches
Java Real Time Projects, Java Full Stack Developer, J2EE, Web services, Hibernate, Spring, Core Java, Servlet, JSP (Java Server Pages)
Certification training offered
Yes
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
.Net component
.Net Web Services, Appdynamics, C# .NET, .Net XML, Microsoft .Net 4, .Net MVC, ASP.NET, .Net WF, .Net AJAX, .Net WPF, ADO.NET, ASP.NET AJAX, .Net Advanced, .Net Nuke, .Net LINQ, .Net WCF
Certification offered
Yes
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
Course Duration provided
3-6 months, 1-3 months
Seeker background catered to
Corporate company, Educational Institution, Individual
Certification provided
Yes
Python applications taught
Data Analysis with Python , Scipy Stack with Python , Web Scraping with Python , GUI (Graphical User Interfaces) with Python , Text Processing with Python, Core Python, PySpark, Regular Expressions with Python , Networking with Python , Machine Learning with Python, Game Development with Python, Data Science with Python, Web Development with Python , Data Extraction with Python , Help in assignment, Data Visualization with Python, Testing with Python
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
Data science techniques
Python, Java, Artificial Intelligence, Machine learning
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
Deep_Learning_Techniques
Tensorflow, Python
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
Teaches web designing at proficiency level
Advanced Web Designing, Basic Web Designing
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
BCA Subject
Software Engineering , Web Programming, Internet Concepts and Web Design , Programming in C++ , IT, Database Management Systems , Java Programming, C Language Programming
Type of class
Crash Course, Regular Classes
Class strength catered to
One on one/ Private Tutions
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
Training for ISTQB certification
No
Testing Types
Manual Software Testing, Automated Software Testing, Mobile Automated Software Testing
Class Location
Online (video chat via skype, google hangout etc)
At the Institute
AWS Certification offered
AWS Certified SysOps Administrator, AWS Certified Developer, AWS Certified DevOps Engineer, AWS Certified Solutions Architect
AWS Certification Level offered
Associate, Professional
Answered 23 hrs ago Learn IT Courses/Data Science
Data Science is a broad and interdisciplinary field that encompasses a variety of topics. Here are the key areas typically covered in Data Science:
### 1. **Mathematics and Statistics**
- **Probability Theory**: Understanding the fundamentals of probability, random variables, and probability distributions.
- **Statistical Inference**: Techniques for making inferences about populations based on sample data, including hypothesis testing and confidence intervals.
- **Linear Algebra**: Essential for understanding data structures, transformations, and many machine learning algorithms.
- **Calculus**: Used for optimizing algorithms and understanding changes in functions, especially in the context of machine learning and neural networks.
### 2. **Programming**
- **Programming Languages**: Proficiency in languages such as Python and R, which are widely used in data science for data manipulation, statistical analysis, and machine learning.
- **Software Development**: Basic principles of software development, including version control (e.g., Git), testing, and debugging.
### 3. **Data Manipulation and Analysis**
- **Data Cleaning and Preprocessing**: Techniques for handling missing data, outliers, and ensuring data quality.
- **Exploratory Data Analysis (EDA)**: Using statistical graphics and other data visualization methods to explore and summarize data sets.
### 4. **Machine Learning**
- **Supervised Learning**: Algorithms for regression and classification, such as linear regression, logistic regression, decision trees, and support vector machines.
- **Unsupervised Learning**: Clustering algorithms like k-means, hierarchical clustering, and dimensionality reduction techniques like PCA (Principal Component Analysis).
- **Deep Learning**: Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and frameworks like TensorFlow and PyTorch.
- **Model Evaluation and Validation**: Techniques for assessing the performance of machine learning models, such as cross-validation, ROC curves, and confusion matrices.
### 5. **Data Engineering**
- **Database Systems**: Understanding relational databases (SQL) and NoSQL databases (e.g., MongoDB).
- **Data Warehousing**: Concepts and tools for storing and managing large amounts of data.
- **ETL (Extract, Transform, Load)**: Processes for extracting data from various sources, transforming it into a suitable format, and loading it into a data warehouse.
### 6. **Big Data Technologies**
- **Hadoop**: Framework for distributed storage and processing of large data sets.
- **Spark**: Engine for big data processing that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance.
### 7. **Data Visualization**
- **Tools**: Proficiency in visualization tools and libraries such as Matplotlib, Seaborn, Plotly, and Tableau.
- **Best Practices**: Principles for effective data visualization and storytelling with data.
### 8. **Domain Knowledge and Applications**
- **Business Acumen**: Understanding business problems and translating them into data science problems.
- **Specialized Domains**: Knowledge of specific domains such as finance, healthcare, marketing, etc., to apply data science techniques effectively.
### 9. **Ethics and Privacy**
- **Data Ethics**: Understanding the ethical implications of data collection, analysis, and use.
- **Privacy and Security**: Ensuring data privacy and security, adhering to regulations like GDPR (General Data Protection Regulation).
### 10. **Communication**
- **Data Storytelling**: Skills for presenting data insights in a compelling and understandable manner to non-technical stakeholders.
- **Reporting**: Creating clear and concise reports and dashboards that convey data findings effectively.
These topics form the foundation of data science, and expertise in these areas enables data scientists to extract meaningful insights from data, develop predictive models, and support decision-making processes in various domains.
Answered 5 days ago Learn IT Courses/Data Science
Data science is a versatile field that finds applications across various domains and industries. Some of the common domains where data scientists work include:
1. **Healthcare**: Data scientists in healthcare analyze medical records, clinical trials data, and patient demographics to improve patient care, optimize treatment plans, and develop predictive models for disease diagnosis and prognosis.
2. **Finance**: In finance, data scientists work on tasks such as risk management, fraud detection, algorithmic trading, credit scoring, and customer segmentation. They use data to identify market trends, assess investment opportunities, and enhance financial decision-making processes.
3. **Retail and E-commerce**: Data scientists help retail companies and e-commerce platforms optimize pricing strategies, forecast demand, personalize recommendations, and improve supply chain management. They analyze customer behavior, transaction data, and inventory levels to drive sales and enhance customer experience.
4. **Marketing and Advertising**: Data scientists in marketing and advertising leverage data to target the right audience, measure campaign effectiveness, and optimize marketing spend. They use techniques like customer segmentation, sentiment analysis, and attribution modeling to maximize the impact of marketing efforts.
5. **Telecommunications**: In the telecommunications industry, data scientists analyze network data, customer usage patterns, and customer feedback to improve service quality, optimize network performance, and develop predictive maintenance models for infrastructure.
6. **Manufacturing and Supply Chain**: Data scientists help manufacturing companies optimize production processes, predict equipment failures, and minimize downtime. They also work on supply chain optimization, inventory management, and logistics planning to streamline operations and reduce costs.
7. **Energy and Utilities**: Data scientists in the energy sector analyze data from sensors, smart meters, and weather forecasts to optimize energy generation, distribution, and consumption. They develop predictive maintenance models for equipment and infrastructure to improve reliability and efficiency.
8. **Government and Public Policy**: Data scientists in government agencies and public policy organizations analyze data to inform decision-making, improve public services, and address societal challenges. They work on projects related to urban planning, transportation, healthcare policy, and public safety.
9. **Technology and Internet Companies**: Data scientists in technology and internet companies work on a wide range of tasks, including user behavior analysis, recommendation systems, natural language processing, and image recognition. They help improve product features, enhance user experience, and drive innovation.
10. **Education**: In the education sector, data scientists analyze student performance data, learning outcomes, and educational resources to personalize learning experiences, identify at-risk students, and improve educational outcomes.
These are just a few examples of the diverse domains where data scientists can make valuable contributions. The skills and techniques used in data science are applicable across industries, making data scientists in high demand in today's data-driven world.
Answered 5 days ago Learn IT Courses/Data Science
Data science is a versatile field that finds applications across various domains and industries. Some of the common domains where data scientists work include:
1. **Healthcare**: Data scientists in healthcare analyze medical records, clinical trials data, and patient demographics to improve patient care, optimize treatment plans, and develop predictive models for disease diagnosis and prognosis.
2. **Finance**: In finance, data scientists work on tasks such as risk management, fraud detection, algorithmic trading, credit scoring, and customer segmentation. They use data to identify market trends, assess investment opportunities, and enhance financial decision-making processes.
3. **Retail and E-commerce**: Data scientists help retail companies and e-commerce platforms optimize pricing strategies, forecast demand, personalize recommendations, and improve supply chain management. They analyze customer behavior, transaction data, and inventory levels to drive sales and enhance customer experience.
4. **Marketing and Advertising**: Data scientists in marketing and advertising leverage data to target the right audience, measure campaign effectiveness, and optimize marketing spend. They use techniques like customer segmentation, sentiment analysis, and attribution modeling to maximize the impact of marketing efforts.
5. **Telecommunications**: In the telecommunications industry, data scientists analyze network data, customer usage patterns, and customer feedback to improve service quality, optimize network performance, and develop predictive maintenance models for infrastructure.
6. **Manufacturing and Supply Chain**: Data scientists help manufacturing companies optimize production processes, predict equipment failures, and minimize downtime. They also work on supply chain optimization, inventory management, and logistics planning to streamline operations and reduce costs.
7. **Energy and Utilities**: Data scientists in the energy sector analyze data from sensors, smart meters, and weather forecasts to optimize energy generation, distribution, and consumption. They develop predictive maintenance models for equipment and infrastructure to improve reliability and efficiency.
8. **Government and Public Policy**: Data scientists in government agencies and public policy organizations analyze data to inform decision-making, improve public services, and address societal challenges. They work on projects related to urban planning, transportation, healthcare policy, and public safety.
9. **Technology and Internet Companies**: Data scientists in technology and internet companies work on a wide range of tasks, including user behavior analysis, recommendation systems, natural language processing, and image recognition. They help improve product features, enhance user experience, and drive innovation.
10. **Education**: In the education sector, data scientists analyze student performance data, learning outcomes, and educational resources to personalize learning experiences, identify at-risk students, and improve educational outcomes.
These are just a few examples of the diverse domains where data scientists can make valuable contributions. The skills and techniques used in data science are applicable across industries, making data scientists in high demand in today's data-driven world.
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