658 Computing courses

SSRS Report Builder Course Summary. Practical SSRS Report Builder course, to leavy you full conversant with queries, DML and DDL statements. Hands-on, Practical SSRS course. PCWorkshops SSRS Course Certificate. Max 4 people per course, we keep it personalised. Where is the SSRS Report Builder Course? Online: Instructor-led. Classroom: Request per email. SSRS Course Materials SSRS Coding Examples Manuals and Notes: In-depth reference materials Exercises: Practical exercises Payment Options → £600, Follow the booking link Arrange for installments Included with SSRS Report Builder Course: SSRS Course Certificate on completion. SSRS Course Manual. Practical SSRS Course exercises. SSRS Code Samples. After the course, 1 free session for questions. Max group size, online and in classroom, on this SSRS Course is 4. Course Topics SSRS Report Builder Course Topics SSRS Course topics: This course focus on producing reports using MS SQL SSRS Report Builder, to use the product features and tools related to data retrieval, visualisations and deployment. The Target Audience: end-users who will produce reports for their department or group using datasets. SSRS Course Topics Report Models: Data source views Creating a model Entities, attributes and roles Tables: Creating tables Interactive sorting Floating headers Grouping: Inserting groups Group headers/footers Displaying group totals Expanding and collapsing Expressions: The expression builder The Report Items collection Built-in fields Conditional formatting expressions Page headers and footers Page names and numbers Exporting reports: The various report formats Using RenderFormat. Parameters: Creating parameters Filtering using parameters Drop list parameters Coping with empty tables Matrices: Row and column groups Sorting and filtering matrices Creating subtotals Charts: Creating charts The parts of a chart Formatting charts Basic reports: Text boxes and images Selecting report items Formatting parts of a report Miniature charts Creating sparklines Creating data bars Graphical indicators Creating and customising indicators Linear and radial gauges Lists: Page-per-record lists Dashboard lists Other list ideas Subreports: Creating a child report Creating the main report Linking the two reports Report parts: Publishing report parts The report part gallery Updating report parts Book the SSRS Course About us Our experienced trainers are award winners. More about us FAQ's Client Comments

SSRS Report Builder Course Summary. Practical SSRS Report Builder course, to leavy you full conversant with queries, DML and DDL statements. Hands-on, Practical SSRS course. PCWorkshops SSRS Course Certificate. Max 4 people per course, we keep it personalised. Where is the SSRS Report Builder Course? Online: Instructor-led. Classroom: Request per email. SSRS Course Materials SSRS Coding Examples Manuals and Notes: In-depth reference materials Exercises: Practical exercises Payment Options → £600, Follow the booking link Arrange for installments Included with SSRS Report Builder Course: SSRS Course Certificate on completion. SSRS Course Manual. Practical SSRS Course exercises. SSRS Code Samples. After the course, 1 free session for questions. Max group size, online and in classroom, on this SSRS Course is 4. Course Topics SSRS Report Builder Course Topics SSRS Course topics: This course focus on producing reports using MS SQL SSRS Report Builder, to use the product features and tools related to data retrieval, visualisations and deployment. The Target Audience: end-users who will produce reports for their department or group using datasets. SSRS Course Topics Report Models: Data source views Creating a model Entities, attributes and roles Tables: Creating tables Interactive sorting Floating headers Grouping: Inserting groups Group headers/footers Displaying group totals Expanding and collapsing Expressions: The expression builder The Report Items collection Built-in fields Conditional formatting expressions Page headers and footers Page names and numbers Exporting reports: The various report formats Using RenderFormat. Parameters: Creating parameters Filtering using parameters Drop list parameters Coping with empty tables Matrices: Row and column groups Sorting and filtering matrices Creating subtotals Charts: Creating charts The parts of a chart Formatting charts Basic reports: Text boxes and images Selecting report items Formatting parts of a report Miniature charts Creating sparklines Creating data bars Graphical indicators Creating and customising indicators Linear and radial gauges Lists: Page-per-record lists Dashboard lists Other list ideas Subreports: Creating a child report Creating the main report Linking the two reports Report parts: Publishing report parts The report part gallery Updating report parts Book the SSRS Course About us Our experienced trainers are award winners. More about us FAQ's Client Comments

Java Webinar Course summary In this Java Webinar Course you will learn Java Basics and OOP Principles. Java Webinar Course description This Java Course is Online, instructor-led, practical attended via Gotomeeting.com. When we receive your booking, we email a connection invitation. This Java Course Webinar will allow you to learn flexibly and commute-free from home. There is no need for attending face-to-face, all advantages of the classroom in built-in and planned for during the delivery of this webinar. We teach online with excellent instruction and targetted well-planned practicals that will clearly illustrate techniques and accurately develop your skills. Question time is built in. Session 1: Data Types, Variables, and Arrays: Primitive types; Characters; Boolean; Working with variables and its scope; Type conversion and casting; Introduction to String class; Working with arrays Session 2: Operators and Expressions Introduction of operators; Arithmetic operators; Relational operators; Assignment operator; Logical operators; Increment and decrement operators; More in operators Session 3: Decision Making If statement; If - else statement; If- else if - else statement; Nested if - else; Switch Statements Session 4: Using Loops The while, do-while and the for loop; Enhanced for loop; Jump statements : break, continue; The return statement; Nesting loops; Using the for-each loop with Arrays Session 5: Methods Introduction of methods; Creating a Method in class; Calling a Method; Returning value from a Method; Adding a Method that takes parameters Session 6: Classes Creating an Object; Using an Object. Adding Instance variables; Controlling accessibility; Class Constructors; Parameterized Constructors; Inheritance. Abstraction. Interfaces and implementing interfaces. Override. Polymorphism. The dot operator, this, static, super, final keywords More in Methods and Classes: Overloading methods; Overloading Constructors; Using objects as parameters; Returning objects; Recursion Java Access control: private, public and protected; Java Nested and inner classes; A stack class Session 7: Java Data structures Files, Streams, Collections. Session 8: Java Exception Handling Fundamentals of exception handling; Exception types; Using try and catch; Multiple catch clauses; Nested try statements; Throw and throws; Finally Blocks Included in the Java Webinar Courses: Java Webinar Course Certificate on completion Java Webinar Course Manual Practical Java Webinar Course exercises, Java Webinar Code Samples/ Java Webinar Course Revision work After the course, 1 free session for questions online Java Webinar Course via Gotomeeting.com. Max group size, online and in classroom, on this Java Webinar Course is 4.

Database Design and Data Normalization Course Summary Relational Databases & Data Normalisation Overview Training Courses London. Learn how to decide which tables and columns are required for a database, plan relationships, how to decide on data types and how to relate tables. Database Design and Data Normalization Course Description Session 1: Introduction to Relational Databases The Database Approach Database Management Systems The function of a Database Management System The Processing Architecture The Schema Architecture The Storage Architecture An overview of Database System Development An overview of general database principles Session 2: Conceptual Data Models Overview of Entity Models Entities and how to identify them Attributes and how to identify them Relationships and their qualities Building an Entity Relationship Diagram Resolving many-to-many relationships Sub-type and Super-type Relationships Arc Relationships Recursive Relationships Constraints and Assumptions Normalization to the First Normal Form (1NF) Normalization to the Second Normal Form (2NF) Normalization to the Third Normal Form (3NF) Exercises: Analyzing a specification Creating an Entity Relationship Diagram from it Session 3: The Relational Model Relational Structure Relational Terminology Domains A Relational schema Candidate keys and Primary Keys Foreign Keys Key Constraints Attribute Constraints Implementing Sub-type/Super-type Relationships Views Denormalization using Pre-joined Tables Denormalization using Derived Data An Introduction to Structured Query Language (SQL) Simple Queries Simple Updates of the Data Included in the Database Design and Data Normalization Courses: Database Design and Data Normalization Course Certificate on completion Database Design and Data Normalization Course Manual Practical Database Design and Data Normalization Course exercises, Database Course Homework / Database Course Revision work After the course, 1 free session for questions online Database Course via Gotomeeting.com. Max group size, online and in classroom, on this Database Course is 4.

Java CrossOver Course for Coders 1-Day, 11am - 6pm Online, Instructor-led, Interactive, Practical Java Basics to coders who are converting to Java from a different coding language. You do not need much time to learn the syntax of Java, because you have done similar work in other programming languages Hands-on, Practical Course, Instructor-led Course. Online with Gotomeeting.com 1 day, from 11am - 6pm. In classroom at on request only. Locations Java CrossOver Course description Overview of Java: This course covers Java Object-Oriented Programming (OOP) principles. Java Data Types and using Variables in Java: Here we learn to code using data types and variables of different data types: Primitives; Characters; Boolean; Variables and variable scope; String class; Type conversion and casting. Java Operators and Expressions: What are operators and how to use them; Arithmetic operators; Relational operators; Assignment operator; Logical operators; Increment and decrement operators. Decision Making: The Java Syntax for the If-statement; If,else; If,else if,else; Nested if; Switch Statements; Using Loops: For loops, while loops, do while, Enhanced for loop; Nesting loops; break, continue; The return statement. Arrays: Working with arrays, 2-d arrays, iterating arrays. Using the for-each loop with Arrays Lists: Working with lists. Lists vs Arrays. Files,streams with Java: Open, Read and Write to CSV Files, txt files and bin files. Exception Handling: Fundamentals of exception handling; Exception types; Using try and catch; Multiple catch clauses; Nested try statements; Throw and throws; The finally blocks Introduction to Classes and Objects: Creating a Class; Creating an Object; Using an Object; Adding Instance variables; Controlling accessibility; Naming conventions for class members. Class Constructors; Parameterized Constructors; Inheritance. Abstraction. Interfaces and implementing interfaces. Override. Polymorphism. The dot operator, this keyword, the static keywords, the super keyword. Using Methods: Introduction of methods; Creating a Method in class; Calling a Method; Returning value from a Method; Adding a Method that takes parameters; More in Methods and Classes: Overloading methods; Overloading Constructors; Using objects as parameters; Returning objects; Recursion; Access control : private, public and protected; The final keyword. What is included PCWorkshops Course Certificate on completion Java Course Notes Java Code Examples Practical Java Course exercises, Java Course Revision work After the course: 1-Hour personalised online revision session Book the Java CrossOver Course About us Our experienced trainers are award winners. More about us FAQ's Client Comments

Software comes in a variety of guises - application software, firmware, middleware, system software. Increasingly, however, it doesn't necessarily present that way, especially as the boundaries between software, data and source code are becoming more and more blurred. And as software becomes more complex and more difficult to disentangle, so it becomes harder to manage and to value. But as it becomes more integral to every aspect of a business, so it is ever more important to keep on top of the technical, legal and commercial issues that arise, issues such as: To address these issues, organisations need a process for evaluating their current situation from all perspectives and for identifying the key actions they need to take to ensure holistic management of their software. This very practical programme will help set your organisation on the right path. Note: this is an indicative agenda, to be used as a starting point for a conversation between client and consultant, depending on the organisation's specific situation and requirements. This programme is designed to give you a deeper understanding of: The technical, legal and commercial risks associated with software development, procurement, use and commercial exploitation The most appropriate processes and responsibilities for managing those risks Note: this is an indicative agenda, to be used as a starting point for a conversation between client and consultant, depending on the organisation's specific situation and requirements. 1 Software business model What is the software business model? What options exist? Has the software business model been thoroughly reviewed to ensure its viability? This means fully understanding the market opportunity, the business environment and customer and end-user expectations. 2 Technology What are the technologies? How has the technology selection been validated considering the competitiveness, structure, and potential for future innovation? 3 UI and UX What is the UI and UX? How to best articulate this? Has the user interface and user experience been studied from both a subjective and objective view to give insight into customer behaviour? 4 Legal framework / commercial aspects Has the necessary legal framework or commercial aspects that may impact upon use or operation of the software been understood and risks identified and mitigated? 5 Software development What is the software development process? Are both the business management and development team's processes resilient in order to improve the company's capability and the maturity of the software? 6 Software quality What is quality? What are the metrics around software quality? What is the maturity level, based around a qualitative and quantitative assessment? 7 Intellectual property associated What IP should be considered when it comes to software? Does the company understand both the intellectual property risks and potential opportunities associated with this software? 8 Security What does software security mean in this context? How is it being addressed? 9 An holistic approach Review of roles and responsibilities to ensure appropriate management and protection

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Machine literacy in data wisdom is a fleetly expanding discipline and now is the crucial element. This groundbreaking field equips computers and systems with the capacity to learn from data and ameliorate their performance over time without unequivocal programming. Statistical ways are employed to train algorithms to produce groups or prognostications and to find significant findings in data mining systems. immaculately, the conclusions made from these perceptivity impact crucial growth pointers in operations and companies. What's Machine Learning? . Machine learning classes in pune The machine literacy term was chased by Arthur Samuel in 1959. It's the discipline solely concentrated on studying and erecting tools and ways that can let machines learn. These styles use data to enhance the computer performance of a particular set of tasks. Machine literacy algorithms induce prognostications or possibilities and produce a model grounded on data samples, also called training data. There's a need for machine literacy as these algorithms are applied in a broad range of operations, for illustration, computer vision, dispatch filtering, speech recognition, husbandry, and drugs, where it's a challenge to produce traditional algorithms that can negotiate the needed tasks. orders in Machine Learning Being such a vast and complicated field, machine literacy is divided into three different orders machine literacy orders Supervised literacy – In this system, the algorithm is trained using data that has been labeled and in which the target variable or asked result is known. Once trained, the algorithm may make prognostications grounded on unidentified information by learning how to associate input variables with the willed affair. Unsupervised literacy – In this case, the algorithm is trained on unlabeled data, and its thing is to discover structures or patterns within the data without having a specific target variable in mind. Common unsupervised literacy tasks include dimensionality reduction and clustering. underpinning literacy – An algorithm is trained via relations with the terrain in this type of literacy. The algorithm learns how to operate in order to maximize a price signal or negotiate a particular ideal. Through prices or penalties, it receives feedback that helps it upgrade its decision-making process. Artificial Intelligence and Machine Learning Artificial intelligence( AI) is divided into several subfields, and machine literacy( ML) is one of them. In order to produce intelligent machines that can pretend mortal intelligence, a variety of methodologies, approaches, and technologies are used. This notion is known as artificial intelligence( AI). The development of ways and models that allow computers to acquire knowledge from data and make recommendations or judgments without unequivocal programming is the focus of machine literacy( ML). Some academics were interested in the idea of having machines learn from data in the early stages of AI as an academic field. They tried to approach the issue using colorful emblematic ways and neural networks. They were primarily perceptrons, along with other models that were ultimately discovered to be reimaginings of the generalized direct models of statistics. For this case, you aim to make a system secerning cows and tykes. With the AI approach, you'll use ways to make a system that can understand the images with the help of specific features and rules you define. Machine literacy models will bear training using a particular dataset of pre-defined images. You need to give numerous farmlands of cows and tykes with corresponding markers. Why is Machine Learning Important? Machine literacy is an abecedarian subfield of artificial intelligence that focuses on assaying and interpreting patterns and structures in data. It enables logic, literacy, and decision-making outside of mortal commerce. The significance of machine literacy is expanding due to the extensively more expansive and more varied data sets, the availability and affordability of computational power, and the availability of high-speed internet. It facilitates the creation of new products and provides companies with a picture of trends in consumer geste and commercial functional patterns. Machine literacy is a high element of the business operations of numerous top enterprises, like Facebook, Google, and Uber. Prophetic Analytics Machine learning course in pune Machine literacy makes prophetic analytics possible by using data to read unborn results. It's salutary in the fields of finance, healthcare, marketing, and logistics. Associations may prognosticate customer growth, spot possible troubles, streamline operations, and take visionary action to ameliorate results using prophetic models. Personalization and recommendation systems Machine literacy makes recommendation systems and substantiated gests possible, impacting every aspect of our diurnal lives. Platforms like Netflix, Amazon, and Spotify use machine literacy algorithms to comprehend stoner preferences and offer substantiated recommendations. Personalization boosts stoner pleasure and engagement while promoting business expansion. Image and speech recognition Algorithms for machine literacy are particularly good at jobs like speech and picture recognition. Deep literacy, a branch of ML, has converted computer vision and natural language processing. It makes it possible for machines to comprehend, dissect, and produce visual and audio input. This technology is helpful for driverless vehicles, surveillance, medical imaging, and availability tools, among other effects. Machine learning training in pune

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