IT Training Classes in Dubai

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C#

Learn C# Programming and digitalize your ideas!

There is nothing else in this world that can scale its problem solving capabilities to the rest of the world like software development or computer programming can.

 Moreover, we are living in the digital age and the growth of technology does not seem to be coming to a stop. As a matter of fact, the use of electronic devices is growing exponentially every year and so you can be sure that there is going to be no shortage of jobs in the computer programming industry.

Study C# Programming then. Why?

This general-purpose language was originally designed by Microsoft to be used for developing apps on the Microsoft platform, and C# developers love the language for being pleasant to use and well-designed. If you want to build apps for the Microsoft platform, you should naturally learn C#.

When C# is used to build web applications, it almost always used within a framework, or stack. So, C# and Microsoft's .NET framework go hand in hand. If you learn C# and you want to build web software, then you'll also learn the languages and tooling around NET.

Finally, C# is often the recommended language to use when making games through the Unity Game engine.

Study at Vibe and get trained by experienced mentors.

Other programming courses offered are: C, C++, Java, VB, VB.Net, ASP.Net, JSP & PHP

Feel free to call us to 045543603/ WhatsApp on 0552824466 /visit our website: www.vibe-edu.com for more details

Pay a visit to have a face to face consultation with our counselors

Vibe Education Services, Office No. 1601, Saheel-2, Near to Al Mulla Plaza, Al Nahda-1, Dubai

We also offer Courses like Spoken English, Spoken Arabic, Business English, Training for IELTS, TOEFL, OET, PTE, SAT, GED, GMAT etc and a wide range of computer courses, accounting courses, management programs, multimedia programs, QA/QC test preparatory courses.

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C

Learn C Programming and digitalize your ideas!

There is nothing else in this world that can scale its problem solving capabilities to the rest of the world like software development or computer programming can.

 Moreover, we are living in the digital age and the growth of technology does not seem to be coming to a stop. As a matter of fact, the use of electronic devices is growing exponentially every year and so you can be sure that there is going to be no shortage of jobs in the computer programming industry.

Study C Programming then. Why?

One powerful reason is memory allocation. Unlike most programming languages, C allows the programmer to write directly to memory. Key constructs in C such as structs, pointers and arrays are designed to structure and manipulate memory in an efficient, machine-independent fashion.

Study at Vibe and get trained by experienced mentors.

Other programming courses offered are: C#, C++, Java, VB, VB.Net, ASP.Net, JSP & PHP

We also offer Courses like Spoken English, Spoken Arabic, Business English, Training for IELTS, TOEFL, OET, PTE, SAT, GED, GMAT etc and a wide range of computer courses, accounting courses, management programs, multimedia programs, QA/QC test preparatory courses.

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STEM Coaching Class.

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Network Switching and Routing, Web Design, Autocad, MS WORD AND EXCEL, MS Project, Planning and Scheduling

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CyberModo has trained many individuals and corporate levels and we are proud for share of contribution we made in the lives of those who we have trained. 

Web Designing Basic: Software Covered: (HTML, CSS, DreamWeaver, Flash) Platform: MAC and Windows Software: can be provided, learning purpose only Material: Printed lesson plan, Keyboard shortcut keys and soft-copy Duration: 30 Hrs (hours can be increased if needed without any extra pay) Class strength: One-to-one or maximum of two in a batch.

Web Designing Advanced: Software Covered: (HTML, CSS, DreamWeaver, Flash) Platform: MAC and Windows Software: can be provided, learning purpose only Material: Printed lesson plan, Keyboard shortcut keys and soft-copy Duration: 40 Hrs (hours can be increased if needed without any extra pay) Class strength: One-to-one or maximum of two in a batch.

Candidates can also select the individual course for training mentioned above as per their requirement. 

Note: It is recommended to know Photoshop for Web Designing Course.

When does course begin? You can start anytime after the registration, as we do not take more than two candidates in a batch. 

Duration of class: Each class goes for two-hour session, but if needed it can be extended to 4-5 hours to finish the course quickly, Weekend (Fri and Sat) also available. 

We can assure you of the quality training and all the studies material will be provided to you. Flexible days and time are available on Weekend and Weekdays between 9am to 9pm.  If you have any further queries, please do not hesitate to call so that our consultant can provide you with more information over the phone.

Kindly visit our website www.cybermodo.com for IT and www.cybermodo.netfor Management for more details and you can also chat live with our online professionals to help you choose the right course for you. 

To protect your investment - we have a course retake guarantee... so there is absolutely no risk to you.

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Currently, Mobile traffic has a growth rate of 125%; desktop has 12%. Needless to say, Digital Marketing is in high demand. Stand out as one of the best Digital Marketing Strategist through this unique course of the renowned University of California – Irvine. This course will enable you to build a great career in the Digital Marketing Industry where marketing strategists are in high-demand. The Program is flexible to assimilate and incorporate technology updates into the modules, on the fly. Learners are required to complete 175 hours of faculty led Online mode. The following are the modules covered: Overview of Digital Marketing Introduction to Internet Marketing Online Measurement & Analytics Online Acquisition Online Conversion Online Retention Social Media Marketing Mobile Marketing Social Media and Internet Audience Profiling Networks Breaking your audience down Research and Impact Evaluating Research, Identifying Net Promoters and Fine-Tuning the Persona Predictive Analytics & Real-Time Social Breaking your audience down Bringing it all together Online Analytics & Measurement Introduction to key Web Analytics principles and terminology. Analysis for outcome Qualitative Data & Testing Competitive analysis and benchmarking. Advanced analytics principles. Analytics Vendor Selection and Managing Management Competitive analysis and benchmarking. Developing a Social Media Strategy Introduction | Social Media Basics | Strategy Overview Buy-in & Culture | Goals & Objectives | Resources | SM Usage | SWOT Analysis Target Audience | Market Research | Keywords | Monitoring/Listening Branding | Social Media Voice/Persona Content & Engagement |Social Media Categories & Channels Social Media Policy| Conversion | Measuring & Execution Mobile Marketing Introduction to Mobile Marketing Understanding and Profiling Your Audience Mobile Websites Mobile Applications Mobile Acquisition and Promotion Conversion and Retention Using Mobile Marketing Conversion and Retention Using Mobile Marketing Online Video Marketing Basic video marketing strategy Basic video production techniques Effective & Viral Videos Monetise videos Setting up YouTube channel YouTube analytics & Brand Channels Case Studies: You tube best practices Basic Analytics to measure video success Content Marketing Course Introduction what is content marketing? Content marketing examples Content marketing steps to success The Business Case: Journey & Audience Content types, storytelling and plan Channels, Promotion & Technology Measurment

University of California Irvine, known for its science & engineering programs, is a top rated university; ranked No.9 among Public universities; and No.1 among Research Universities, and has produced 3 Nobel laureates.

The skill-enhancement Program will transform you into a complete digital marketer with expertise in the top eight digital marketing domains — search engine optimization, social media, pay-per-click, conversion optimization, digital analytics, content, mobile and email marketing. This program also comes with the benefit of Placement support from 361 Degree Minds though you would not have the need since opportunities galore when you do this program.

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This Certificate Course, offered by 361 Degree Minds, educates about various principles and theories involved in using Core Java. This course prepares students to meet the vast requirements of professionals having suitable qualification as well as exposure in using Core Java. You will master the technology and be ready to take up any assignment using Core Java. Learners are required to complete 12 Weeks of faculty led Online mode. The following are the modules covered: Coverage Core Java programming as applied in IT application and product development in IT Organizations worldwide. Session Plan 80 Sessions Each session will have : Approximately 8 – 12 minutes of expert led sessions.- Approximately 5 minutes of assessment. There will be approximately 15 to 20 minutes of practice exercises Assessment

• 3 mid course assessments
• 1 End program assessments

Projects Yes. Learners will be given a choice of projects at the end of the program.Learners will choose one and work on it. Project will be evaluated and feedback given by our experts and will play a role in the assessment. Sample Programs Library Lots of sample programs in library. Practice Window While you are learning, you will have a link, that can make you run a Java Code and see the output in real time. The course is designed in a way that provides students with foundational skills to opt for various job profiles such as Software Developer, Programmer, Engineer, Java Developer, Java EE Application Engineer, Web Application Developer, Android Software Developer, Java Spring Developer on completion of this course.

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This Post Graduate Program in Business Analytics – designed by veterans in the Analytics industry; helps to establish a decent career in the growing Data and Analytics domain. This uniquely blended Program is brought to by Praxis, a Top-ranked Analytics B-School in India. Post Graduate Program in Business Analytics is one of the key requisites in any large organization. The time is at its best for someone to take up a career in this domain. Enormous opportunities and extreme dearth in getting candidates force large organizations go helter-skelter. It is imperative that career seekers grab this opportunity. Learners are required to complete 12-15 months of faculty led Online mode. The following are the modules covered"   Big Data 101 Big Data Characteristics Big Data and Business Data Relationships and Data Model Data Grouping Clustering Algorithms Getting ready for Clustering Algorithms Clustering Algorithms – UPGMA, single Link Clustering KPIs, Businesses & Data Elements Mapping for business outcomes Basic Query Advanced Query – Embedding Mathematics Modelling Introduction to key mathematical concepts Application of eigenvalues and eigenvectors Application of the graph Laplacian Application of PCA and SVD Coding in DB Environment Making Data Sets R Programming R Programming Introduction to R – I Introduction to R – II Common Data Structures in R Conditional Operation and Loops Looping in R using Apply Family Functions Creating User Defined Functions in R Graphics with R Advanced Graphics with R Text Analytics Basics of text analysis processes Web crawling Web Scraping from downloaded html files Text classification Singular Value decomposition concept Latent Semantic Analysis Document clustering Topic Modeling Class Assignments Presentation Statistics 101 Introduction to Statistics Introduction to Statistics – II Measures of Central Tendency, Spread and Shape – I Measures of Central Tendency, Spread and Shape – II Measures of Central Tendency, Spread and Shape – III Python Understanding Basics of Python Control Structures and for loop Playing with while loop | break and continue Strings and files List Dictionary and Tuples Statistics with R Introduction to Data Introduction to Probability Distributions Introduction to linear regression Foundations for inference and estimation Foundations for inference and hypothesis testing Linear Regression and Multiple Regression Data Mining 1 - Machine Learning with R & Python Introduction to NumPy Introduction to Pandas Slicing Data Exploratory Data Analysis Exploratory Data Analysis (Continue) Missing Value Imputation and Outlier Analysis Linear Regression Motivation Linear Regression optimization objective Linear Regression in Python Introduction to Regression Tree Introduction to Classification Tree Measures of Selecting the best Split Cluster Analysis – Hierarchical Clustering & k-Means Clustering Customer segmentation in Telecom Industry using Cluster Analysis k-Means clustering Association Rules mining Market Basket Analysis Advanced Statistics with R Inference and hypothesis testing on single population Analysis of difference in two populations Analysis of Variance Chi-Square Analysis Analysis of data using Non-parametric Statistics Linear regression analysis Multiple regression analysis Advanced Multiple regression analysis Logistic Regression Forecasting Data Visualization with Tableau Need for visualizing data Research methodologies Importance of Big data visualization Tableau product offerings Installation of Tableau Public Working with Tableau - Live Case study/Discussion Creating interactive dashboards with Tableau Public Case study discussion Story Boarding with Tableau Public Case study discussion Geomapping in Tableau Qlik view – Basics Google charts – Basics Dynamic charts with Google Docs Supplementary material & Case study discussion Closing session & Queries Web Analytics Introduction to Digital Media Analytics Introduction to Google Analytics Concept of Account, Property and View Concept of Sessions and Users Concept of Dimension, Metric and Segment Reading a Google Analytics Report Audience Analytics Acquisition Analytics Behaviour Analytics Real-Time Analytics Setting Up and Analysing Events Intelligent Events Setting Up and Analysing Experiments Setting Up and Measuring Conversion Goals Attribution Modelling Segment Reporting Designing Custom Reports Introduction to Google Adwords Search Marketing Display Marketing Google Adwords Analytics Managing a Google Analytics Account RDBMS with SQL and DWH Introduction to DBMS / RDBMS Data Modelling Physical Data Model Getting Started with SQL Lite DDL DML Introduction to Data Warehousing Dimensional Modelling Advanced SQL Olap Cubes Olap Cubes Practicals Artificial Intelligence & Deep Learning - Industry Practices This program is brought to you by Praxis B schoolCompanies both large and small are scrambling to hire qualified individuals with a plethora of data literacy skills.  This makes now the perfect time for the candidates to take advantage of rewarding career opportunities in developing technical solutions, analyzing & documenting requirements, managing and communication, devising solutions and blending business with technology. This program also comes with the benefit of Placement support from 361 Degree Minds though you would not have the need since opportunities galore when you do this program.

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This Post Graduate Program in Data Science and Data Visualization – designed by veterans in the Analytics industry; helps to establish a decent career in the growing Data and Analytics domain. This uniquely blended Program is brought to you by Praxis, a Top-ranked Analytics B-School in India. Post Graduate Program in Data Engineering – Visualization is one of the key requisites in any large organization. The time is at its best for someone to take up a career in this domain. Enormous opportunities and extreme dearth in getting candidates force large organizations go helter-skelter. It is imperative that career seekers grab this opportunity. Learners are required to complete 12-15 months of faculty led Online mode. The following are the modules covered"   Big Data 101 Big Data Characteristics Big Data and Business Data Relationships and Data Model Data Grouping Clustering Algorithms Getting ready for Clustering Algorithms Clustering Algorithms – UPGMA, single Link Clustering KPIs, Businesses & Data Elements Mapping for business outcomes Basic Query Advanced Query – Embedding Mathematics Modelling Introduction to key mathematical concepts Application of eigenvalues and eigenvectors Application of the graph Laplacian Application of PCA and SVD Coding in DB Environment Making Data Sets Statistics 101 Introduction to Statistics Introduction to Statistics – II Measures of Central Tendency, Spread and Shape – I Measures of Central Tendency, Spread and Shape – II Measures of Central Tendency, Spread and Shape – III R Programming R Programming Introduction to R – I Introduction to R – II Common Data Structures in R Conditional Operation and Loops Looping in R using Apply Family Functions Creating User Defined Functions in R Graphics with R Advanced Graphics with R Hadoop Introduction to Big Data and Hadoop Introduction to DBMS systems using MySQL Big Data and Hadoop EcoSystem HDFS Unix & HDFS Hands-on Map-Reduce basics Map Reduce Advanced Topics and Hands on Pig introduction and Hands on Pig Scripting Hive Introduction, Metastore, Limitations of Hive Comparison with Traditional Database and HIVE scripting Hive Data Types, Partitioning and Bucketing Hive Tables (Managed and External) Hive Continued Scoop Introduction and Hands-on Introduction to NoSql and HBASE HBASE architecture and Hands-on Access Methods Big Data with Spark and Python Python Understanding Basics of Python Control Structures and for loop Playing with while loop | break and continue Strings and files List Dictionary and Tuples Data Visualization with Tableau Need for visualizing data Research methodologies Importance of Big data visualization Tableau product offerings Installation of Tableau Public Working with Tableau - Live Case study/Discussion Creating interactive dashboards with Tableau Public Case study discussion Story Boarding with Tableau Public Case study discussion Geomapping in Tableau Qlik view – Basics Google charts – Basics Dynamic charts with Google Docs Supplementary material & Case study discussion Closing session & Queries RDBMS with SQL and DWH Introduction to DBMS / RDBMS Data Modelling Physical Data Model Getting Started with SQL Lite DDL DML Introduction to Data Warehousing Dimensional Modelling Advanced SQL Olap Cubes Olap Cubes Practicals Artificial Intelligence & Deep Learning - Industry Practices

This program is brought to you by Praxis B schoolData visualization post graduate program provides candidates a powerful way to communicate data-driven findings, motivate analyses, and detect flaws in their career phase in Engineering,  Business Intelligent Analysis and Data Analysis. This program also comes with the benefit of Placement support from 361 Degree Minds though you would not have the need since opportunities galore when you do this program.

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This Post Graduate Program in Data Science and Machine Learning has a perfect blend of Technology, Data Science and Business cases and insights; it stands out to be among the best in the world.  It is imperative that career seekers grab this opportunity.This uniquely blended Program is brought to you by Praxis, a Top-ranked Analytics B-School in India. Post Graduate Program in Data Science is one of the key requisites in any large organization. The time is at its best for someone to take up a career in this domain. Enormous opportunities and extreme dearth in getting candidates force large organizations go helter-skelter. It is imperative that career seekers grab this opportunity.Learners are required to complete 12-15 months of faculty led Online mode. The following are the modules covered"   Big Data 101 Big Data Characteristics Big Data and Business Data Relationships and Data Model Data Grouping Clustering Algorithms Getting ready for Clustering Algorithms Clustering Algorithms – UPGMA, single Link Clustering KPIs, Businesses & Data Elements Mapping for business outcomes Basic Query Advanced Query – Embedding Mathematics Modelling Introduction to key mathematical concepts Application of eigenvalues and eigenvectors Application of the graph Laplacian Application of PCA and SVD Coding in DB Environment Making Data Sets Statistics 101 Introduction to Statistics Introduction to Statistics – II Measures of Central Tendency, Spread and Shape – I Measures of Central Tendency, Spread and Shape – II Measures of Central Tendency, Spread and Shape – III R Programming R Programming Introduction to R – I Introduction to R – II Common Data Structures in R Conditional Operation and Loops Looping in R using Apply Family Functions Creating User Defined Functions in R Graphics with R Advanced Graphics with R Hadoop Introduction to Big Data and Hadoop Introduction to DBMS systems using MySQL Big Data and Hadoop EcoSystem HDFS Unix & HDFS Hands-on Map-Reduce basics Map Reduce Advanced Topics and Hands on Pig introduction and Hands on Pig Scripting Hive Introduction, Metastore, Limitations of Hive Comparison with Traditional Database and HIVE scripting Hive Data Types, Partitioning and Bucketing Hive Tables (Managed and External) Hive Continued Scoop Introduction and Hands-on Introduction to NoSql and HBASE HBASE architecture and Hands-on Access Methods Big Data with Spark and Python Python Understanding Basics of Python Control Structures and for loop Playing with while loop | break and continue Strings and files List Dictionary and Tuples Data Mining 1 - Machine Learning with R & Python Introduction to NumPy Introduction to Pandas Slicing Data Exploratory Data Analysis Exploratory Data Analysis (Continue) Missing Value Imputation and Outlier Analysis Linear Regression Motivation Linear Regression optimization objective Linear Regression in Python Introduction to Regression Tree Introduction to Classification Tree Measures of Selecting the best Split Cluster Analysis – Hierarchical Clustering & k-Means Clustering Customer segmentation in Telecom Industry using Cluster Analysis k-Means clustering Association Rules mining Market Basket Analysis Data Mining 2 - Advanced Machine Learning with R & Python Sources of Error (Irreducible error, bias and variance) Formally defining the 3 Sources of Error Linear Regression – Multicollinearity (VIF) Qualitative Predictors – Use of Dummy Variables Observing overfitting in Polynomial Regression Regularized Regression (L2 – Regularization) – To avoid overfitting Regularized Regression (L1 – Regularization) – Feature selection using regularization Regularized Regression – How does regularized regression handles multicollinearity? Decision Tree – Pruning Bagging Models Designing your own Bagged Model Random Forest Boosting (Ada Boost) K Nearest Neighbour – Concept. kNN algorithm for k=1 and k>1 Writing a K Nearest Neighbour algorithm from scratch Comparison of kNN with Linear Regression; Difference between kNN and kMeans. Revision of basics of Linear Algebra The Theory of dimension reduction Practical – Compressing an image file [Practical using R Software] Practical – Compressing an image file [Practical using R Software] (Continue) RDBMS with SQL and DWH Introduction to DBMS / RDBMS Data Modelling Physical Data Model Getting Started with SQL Lite DDL DML Introduction to Data Warehousing Dimensional Modelling Advanced SQL Olap Cubes Olap Cubes Practicals Artificial Intelligence & Deep Learning - Industry Practices

This program is brought to you by Praxis B schoolThe Program in Machine Learning is strategically designed to make the candidates competent authority in the enticing world of Machine Learning by introducing you to the fundamentals and dynamics of Machine Learning for coding, linear classification, research and development of algorithms and to predict to extract patterns. This program also comes with the benefit of Placement support from 361 Degree Minds though you would not have the need since opportunities galore when you do this program.

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Evolved and designed by veterans in the Analytics industry, this program prepares students and working professionals to establish a hi-flying globe-trotting career in the growing Data and Analytics domain.Learners are required to complete 6-12 months of faculty led Online mode. The following are the modules covered"   Big Data 101 Big Data Characteristics Big Data and Business Data Relationships and Data Model Data Grouping Clustering Algorithms Getting ready for Clustering Algorithms Clustering Algorithms – UPGMA, single Link Clustering KPIs, Businesses & Data Elements Mapping for business outcomes Basic Query Advanced Query – Embedding Mathematics Modelling Introduction to key mathematical concepts Application of eigenvalues and eigenvectors Application of the graph Laplacian Application of PCA and SVD Coding in DB Environment Making Data Sets R Programming R Programming Introduction to R – I Introduction to R – II Common Data Structures in R Conditional Operation and Loops Looping in R using Apply Family Functions Creating User Defined Functions in R Graphics with R Advanced Graphics with R Text Analytics Basics of text analysis processes Web crawling Web Scraping from downloaded html files Text classification Singular Value decomposition concept Latent Semantic Analysis Document clustering Topic Modeling Class Assignments Presentation Statistics 101 Introduction to Statistics Introduction to Statistics – II Measures of Central Tendency, Spread and Shape – I Measures of Central Tendency, Spread and Shape – II Measures of Central Tendency, Spread and Shape – III Python Understanding Basics of Python Control Structures and for loop Playing with while loop | break and continue Strings and files List Dictionary and Tuples Statistics with R Introduction to Data Introduction to Probability Distributions Introduction to linear regression Foundations for inference and estimation Foundations for inference and hypothesis testing Linear Regression and Multiple Regression Data Mining 1 - Machine Learning with R & Python Introduction to NumPy Introduction to Pandas Slicing Data Exploratory Data Analysis Exploratory Data Analysis (Continue) Missing Value Imputation and Outlier Analysis Linear Regression Motivation Linear Regression optimization objective Linear Regression in Python Introduction to Regression Tree Introduction to Classification Tree Measures of Selecting the best Split Cluster Analysis – Hierarchical Clustering & k-Means Clustering Customer segmentation in Telecom Industry using Cluster Analysis k-Means clustering Association Rules mining Market Basket Analysis Advanced Statistics with R Inference and hypothesis testing on single population Analysis of difference in two populations Analysis of Variance Chi-Square Analysis Analysis of data using Non-parametric Statistics Linear regression analysis Multiple regression analysis Advanced Multiple regression analysis Logistic Regression Forecasting Data Visualization with Tableau Need for visualizing data Research methodologies Importance of Big data visualization Tableau product offerings Installation of Tableau Public Working with Tableau - Live Case study/Discussion Creating interactive dashboards with Tableau Public Case study discussion Story Boarding with Tableau Public Case study discussion Geomapping in Tableau Qlik view – Basics Google charts – Basics Dynamic charts with Google Docs Supplementary material & Case study discussion Closing session & Queries Web Analytics Introduction to Digital Media Analytics Introduction to Google Analytics Concept of Account, Property and View Concept of Sessions and Users Concept of Dimension, Metric and Segment Reading a Google Analytics Report Audience Analytics Acquisition Analytics Behaviour Analytics Real-Time Analytics Setting Up and Analysing Events Intelligent Events Setting Up and Analysing Experiments Setting Up and Measuring Conversion Goals Attribution Modelling Segment Reporting Designing Custom Reports Introduction to Google Adwords Search Marketing Display Marketing Google Adwords Analytics Managing a Google Analytics Account RDBMS with SQL and DWH Introduction to DBMS / RDBMS Data Modelling Physical Data Model Getting Started with SQL Lite DDL DML Introduction to Data Warehousing Dimensional Modelling Advanced SQL Olap Cubes Olap Cubes Practicals Artificial Intelligence & Deep Learning - Industry Practices

This program is brought to you by Praxis B schoolCompanies both large and small are scrambling to hire qualified individuals with a plethora of data literacy skills.  This makes now the perfect time for the candidates to take advantage of rewarding career opportunities in developing technical solutions, analyzing & documenting requirements, managing and communication, devising solutions and blending business with technology.

Get More Info

Course Details

Post Graduate Program in Data Science is one of the key requisites in any large organization. The time is at its best for someone to take up a career in this domain. Enormous opportunities and extreme dearth in getting candidates force large organizations go helter-skelter. It is imperative that career seekers grab this opportunity. Learners are required to complete 6-12 months of faculty led Online mode. The following are the modules covered"   Big Data 101 Big Data Characteristics Big Data and Business Data Relationships and Data Model Data Grouping Clustering Algorithms Getting ready for Clustering Algorithms Clustering Algorithms – UPGMA, single Link Clustering KPIs, Businesses & Data Elements Mapping for business outcomes Basic Query Advanced Query – Embedding Mathematics Modelling Introduction to key mathematical concepts Application of eigenvalues and eigenvectors Application of the graph Laplacian Application of PCA and SVD Coding in DB Environment Making Data Sets Statistics 101 Introduction to Statistics Introduction to Statistics – II Measures of Central Tendency, Spread and Shape – I Measures of Central Tendency, Spread and Shape – II Measures of Central Tendency, Spread and Shape – III R Programming R Programming Introduction to R – I Introduction to R – II Common Data Structures in R Conditional Operation and Loops Looping in R using Apply Family Functions Creating User Defined Functions in R Graphics with R Advanced Graphics with R Hadoop Introduction to Big Data and Hadoop Introduction to DBMS systems using MySQL Big Data and Hadoop EcoSystem HDFS Unix & HDFS Hands-on Map-Reduce basics Map Reduce Advanced Topics and Hands on Pig introduction and Hands on Pig Scripting Hive Introduction, Metastore, Limitations of Hive Comparison with Traditional Database and HIVE scripting Hive Data Types, Partitioning and Bucketing Hive Tables (Managed and External) Hive Continued Scoop Introduction and Hands-on Introduction to NoSql and HBASE HBASE architecture and Hands-on Access Methods Big Data with Spark and Python Python Understanding Basics of Python Control Structures and for loop Playing with while loop | break and continue Strings and files List Dictionary and Tuples Data Mining 1 - Machine Learning with R & Python Introduction to NumPy Introduction to Pandas Slicing Data Exploratory Data Analysis Exploratory Data Analysis (Continue) Missing Value Imputation and Outlier Analysis Linear Regression Motivation Linear Regression optimization objective Linear Regression in Python Introduction to Regression Tree Introduction to Classification Tree Measures of Selecting the best Split Cluster Analysis – Hierarchical Clustering & k-Means Clustering Customer segmentation in Telecom Industry using Cluster Analysis k-Means clustering Association Rules mining Market Basket Analysis Data Mining 2 - Advanced Machine Learning with R & Python Sources of Error (Irreducible error, bias and variance) Formally defining the 3 Sources of Error Linear Regression – Multicollinearity (VIF) Qualitative Predictors – Use of Dummy Variables Observing overfitting in Polynomial Regression Regularized Regression (L2 – Regularization) – To avoid overfitting Regularized Regression (L1 – Regularization) – Feature selection using regularization Regularized Regression – How does regularized regression handles multicollinearity? Decision Tree – Pruning Bagging Models Designing your own Bagged Model Random Forest Boosting (Ada Boost) K Nearest Neighbour – Concept. kNN algorithm for k=1 and k>1 Writing a K Nearest Neighbour algorithm from scratch Comparison of kNN with Linear Regression; Difference between kNN and kMeans. Revision of basics of Linear Algebra The Theory of dimension reduction Practical – Compressing an image file [Practical using R Software] Practical – Compressing an image file [Practical using R Software] (Continue) RDBMS with SQL and DWH Introduction to DBMS / RDBMS Data Modelling Physical Data Model Getting Started with SQL Lite DDL DML Introduction to Data Warehousing Dimensional Modelling Advanced SQL Olap Cubes Olap Cubes Practicals Artificial Intelligence & Deep Learning - Industry Practices

This program is brought to you by Praxis B schoolThe Program in Machine Learning is strategically designed to make the candidates competent authority in the enticing world of Machine Learning by introducing you to the fundamentals and dynamics of Machine Learning for coding, linear classification, research and development of algorithms and to predict to extract patterns.

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This Short-Term Program – designed by veterans in the Analytics industry; helps you master ‘Spark with Python’, which is predominantly used in Data Science. This course will help you learn and master the Python Programming language that is widely used among software developers for build control and management, testing, and in many other ways. You can become a Software Developer once you have mastered the Python Programming language – one of the most popular tools for data analysis. Learners are required to complete 10 weeks of faculty led Online mode. The following are the modules covered Refresher Python Programming Spark Introduction and Architechture Spark RDDs Spark transformation and actions  Spark DataFrames and SparkSQL Apache Flume and Apache Kafka Spark Streaming Spark Streaming - Processing Mulitple batches Introduction to SparkMLib

This program is brought to you by Praxis B schoolThe entry-level course is a gateway to the world of analytics that enables the students to gain expertise knowledge from top instructors for high data processing, querying, data analysis operations and generating analytics reports in a faster way, paving a way to the career world. 

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This Short-Term Program – designed by veterans in the Analytics industry; helps you master the ‘R’ tool, which is predominantly used in Data Science. This course will help you learn and master the R language specifically for Data Science, wherein it is widely used to build methods, processes, and systems to extract knowledge or insights from data in various forms. You can become a Data Science Expert once you have mastered R – one of the most popular tools for data science. Learners are required to complete 10 weeks of faculty led Online mode. The following are the modules covered Machine Learning with R & Python Introduction to NumPy Introduction to Pandas Slicing Data Exploratory Data Analysis Exploratory Data Analysis (Continue) Missing Value Imputation and Outlier Analysis Linear Regression Motivation Linear Regression optimization objective Linear Regression in Python Introduction to Regression Tree Introduction to Classification Tree Measures of Selecting the best Split Cluster Analysis – Hierarchical Clustering & k-Means Clustering Customer segmentation in Telecom Industry using Cluster Analysis k-Means clustering Association Rules mining Market Basket Analysis

This program is brought to you by Praxis B schoolThe course is designed to enable the candidates to be responsible for business analytics, building data products and software platforms, along with developing visualizations and machine learning algorithms.

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This Short-Term Program – designed by veterans in the Analytics industry; helps you master the ‘Access Method’ tool, which is predominantly used in Data Science. This course will help you learn and master Access Methods used to access data on disk, tape or other external devices. It will help you understand how Access Methods is used technically and its future prospects. Learners are required to complete 10 weeks of faculty led Online mode. The following are the modules covered Flume introduction Data streaming demo through Flume Scala Programming - Intro Scala Programming Spark Shell, Spark Context, RDD Spark Streaming Architecture Oozie Components, Scheduling with Oozie, Demo on Oozie Workflow, Oozie Co-ordinator, Oozie Commands.

The course enables the canThis program is brought to you by Praxis B school

candidates to be valuable expertise to any organization that uses the database program. They may be database users, Access trainers, database creators, user-supporters, teachers, or professors. Because of the level of knowledge required to obtain certification, they often become the Access experts within their organizations.

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This course will enable you to build a great career in the Digital Marketing Industry where marketing strategists are in high-demand. The Program is flexible to assimilate and incorporate technology updates into the modules, on the fly. This program also comes with the benefit of Placement support from 361 Degree Minds though you would not have the need since opportunities galore when you do this program. Content is not the king, the Expert Writer is the king. The internet is full of content and not all of them are worth seeing. Businesses need creative and relative content that can talk to their potential customers. Learn how to strategize your content and tell effective stories on the web! Learners are required to complete 45 hours of faculty led Online mode. The following are the modules covered

• Content Marketing
• Course Introduction: what is content marketing?
• Content marketing examples Content marketing steps to success
• The Business case,Journey & Audience
• Content types, storytelling and plan
• Channels, Promotion & Technology
• Measurment

University of California Irvine, known for its science & engineering programs, is a top rated university; ranked No.9 among Public universities; and No.1 among Research Universities, and has produced 3 Nobel laureates.

The course clearly defines the responsibilities of a content marketer including content creation, proof reading, SEO promotion, following the market trend, impeccable language for jobs titles such as brand journalist, content marketing specialist, conversion rate optimization, advertising copywriter and more.

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This course will help you learn and master the tools used in Web Analytics which are widely used for measurement, collection, analysis and reporting of web data for purposes of understanding and optimizing web usage. You can become a Web Analytics Expert once you have mastered the tools like Google Analytics, Spring Metrics, Woopra, etc. Learners are required to complete 6 weeks of faculty led Online mode.

The following are the modules covered

• Web Analytics
• Introduction to Digital Media Analytics
• Introduction to Google Analytics
• Concept of Account, Property and View
• Concept of Sessions and Users
• Concept of Dimension, Metric and Segment
• Reading a Google Analytics Report
• Audience Analytics
• Acquisition Analytics
• Behaviour Analytics
• Real-Time Analytics
• Setting Up and Analysing Events
• Intelligent Events
• Setting Up and Analysing Experiments
• Setting Up and Measuring Conversion Goals
• Attribution Modelling
• Segment Reporting
• Designing Custom Reports
• Introduction to Google Adwords
• Search Marketing
• Display Marketing
• Google Adwords Analytics
• Managing a Google Analytics Account

This program is brought to you by Praxis B school

The course is designed in such a way that the candidates equip and master the critical elements of the web, mobile and other social media platform to make apt business decisions. The candidates will be highly expertise at performing qualitative & quantitative searches, gathering and working with multi-channel data sources.

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This course will help you learn and master the R language that is widely used among arithmeticians and data miners for mounting statistical software and data examination. You can become a Statistics Expert once you have mastered R – one of the most popular tools for data analysis and statistics. Learners are required to complete 10 weeks of faculty led Online mode. The following are the modules covered

• Statistics with R
• Introduction to Data
• Introduction to Probability
• Distributions
• Introduction to linear regression
• Foundations for inference and estimation
• Foundations for inference and hypothesis testing
• Linear Regression and Multiple Regression

This program is brought to you by Praxis B school

In this specialization, you will learn to analyze and visualize data in R and create data reproducible data analysis report, demonstrate a conceptual understanding of the unified nature of statistical inference, make data-based decisions, visualize date with R packages and communicate effectively without relying on statistical jargon. 

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The Statistics 101 course will help you build knowledge about the current trends and future aspects of Statistics. It will help you understand how Statistics is used in businesses, corporates, governments to assemble data and draw conclusions using specific mathematical formula. Learners are required to complete 6 weeks of faculty led Online mode. The following are the modules covered Statistics 101 Introduction to Statistics Introduction to Statistics – II Measures of Central Tendency, Spread and Shape – I CMeasures of Central Tendency, Spread and Shape – II Measures of Central Tendency, Spread and Shape – III Measuring Association

This program is brought to you by Praxis B school

The entry-level course is a gateway to the world of analytics that covers the fundamentals of statistics that enables the candidates to gather industry knowledge in Data Visualization, Summarizing Data, Displaying and Comparing Quantitative Data, and more.

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This course will help you learn and master the RDSMS that is widely used in database management systems to manage and examine huge amount of data. You can become a Data Analyst Expert once you have mastered RDSMS – one of the most popular tools in database management systems. Learners are required to complete 6 weeks of faculty led Online mode. The following are the modules covered RDBMS with SQL and DWH Introduction to DBMS / RDBMS Data Modelling Physical Data Model Getting Started with SQL Lite DDL DML Introduction to Data Warehousing Dimensional Modelling Advanced SQL Olap Cubes Olap Cubes Practicals

This program is brought to you by Praxis B school

This program is designed to equip candidates with immense industry knowledge in relational database management systems, to describe the data, to define and manipulate date, to create and drop database and tables, administration, upgrading and improving current database, retaining and protecting the reliability of data.

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Course Details

This course will help you learn and master the R language that is widely used among arithmeticians and data miners for mounting statistical software and data examination. You can become a Data Analyst Expert once you have mastered R – one of the most popular tools for data analysis. Learners are required to complete 6 weeks of faculty led Online mode. The following are the modules covered R Programming R Programming Introduction to R – I Introduction to R – II Common Data Structures in R Conditional Operation and Loops Looping in R using Apply Family Functions Creating User Defined Functions in R Graphics with R Advanced Graphics with R

This program is brought to you by Praxis B school

The program is designed to enable candidates with effectual industry knowledge for data importing and cleaning, statistical computing and designing, creating tools for data analysis, data modeling, data visualization and more, useful for their career.

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This course will help you learn and master the tools used in Text Analytics which are widely used for deriving high-quality information from text through data mining. You can become a Text Analytics Expert once you have mastered the tools like Lexalytics, IBM Watson AlchemyAPI, Sysomos, etc. Learners are required to complete 6 weeks of faculty led Online mode. The following are the modules covered Text Analytics Basics of text analysis processes Web crawling Web Scraping from downloaded html files Text classification Singular Value decomposition concept Latent Semantic Analysis Document clustering Topic Modeling Class Assignments Presentation

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The program covers the essentials of text analytics in text categorization, text clustering, sentimental analysis, concept extraction, document summarization and entity related modeling. 

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This course will help you learn and master the Python Programming language that is widely used among software developers for build control and management, testing, and in many other ways. You can become a Software Developer once you have mastered the Python Programming language – one of the most popular tools for data analysis. Learners are required to complete 4 weeks of faculty led Online mode. The following are the modules covered Python Understanding Basics of Python Control Structures and for loop Playing with while loop | break and continue Strings and files List Dictionary and Tuples

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This course is designed to provide in-depth knowledge in open source and object-oriented programming language to serve purposes like website programming, writing system administration software, software testing, desktop application development and much more.

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This course will help you learn and master the Hadoop Software which is widely used for distributed storage and processing of datasets of big data. You can become a Big Data Engineer once you have mastered the Apache Hadoop Software – one of the most popular tools for used for processing of datasets of big data. Learners are required to complete 10 weeks of faculty led Online mode. The following are the modules covered Hadoop Introduction to Big Data and Hadoop Introduction to DBMS systems using MySQL Big Data and Hadoop EcoSystem HDFS Unix & HDFS Hands-on Map-Reduce basics Map Reduce Advanced Topics and Hands on Pig introduction and Hands on Pig Scripting Hive Introduction, Metastore, Limitations of Hive Comparison with Traditional Database and HIVE scripting Hive Data Types, Partitioning and Bucketing Hive Tables (Managed and External) Hive Continued Scoop Introduction and Hands-on Introduction to NoSql and HBASE HBASE architecture and Hands-on

This program is brought to you by Praxis B school

The candidates are provided with ground breaking online certificate courses in Hadoop to program, execute parallel processing, testing designing and developing systems, and documentation skills.

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This course will help you learn and master the Data Visualization with Tableau which is widely used in corporates for business intelligence. You can become a Tableau expert once you have mastered this software – one of the most popular tools for Business Intelligence and Analytics. Learners are required to complete 6 weeks of faculty led Online mode. The following are the modules covered Data Visualization with Tableau Need for visualizing data Research methodologies Importance of Big data visualization Tableau product offerings Installation of Tableau Public Working with Tableau - Live Case study/Discussion Creating interactive dashboards with Tableau Public Case study discussion Story Boarding with Tableau Public Case study discussion Geomapping in Tableau Qlik view – Basics Google charts – Basics Dynamic charts with Google Docs Supplementary material & Case study discussion Closing session & Queries

This program is brought to you by Praxis B school

This high-end program designed and delivered by top industry experts and professors train the new generation of data-savvy professionals for fast analytics, producing visualizations for an outsized quantity of information and integration.

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