800 Business Analytics & Intelligence courses

Duration 3 Days 18 CPD hours This course is intended for This is an introductory-level course designed to teach experienced systems administrators how to install, maintain, monitor, troubleshoot, optimize, and secure Hadoop. Previous Hadoop experience is not required. Overview Working within in an engaging, hands-on learning environment, guided by our expert team, attendees will learn to: Understand the benefits of distributed computing Understand the Hadoop architecture (including HDFS and MapReduce) Define administrator participation in Big Data projects Plan, implement, and maintain Hadoop clusters Deploy and maintain additional Big Data tools (Pig, Hive, Flume, etc.) Plan, deploy and maintain HBase on a Hadoop cluster Monitor and maintain hundreds of servers Pinpoint performance bottlenecks and fix them Apache Hadoop is an open source framework for creating reliable and distributable compute clusters. Hadoop provides an excellent platform (with other related frameworks) to process large unstructured or semi-structured data sets from multiple sources to dissect, classify, learn from and make suggestions for business analytics, decision support, and other advanced forms of machine intelligence. This is an introductory-level, hands-on lab-intensive course geared for the administrator (new to Hadoop) who is charged with maintaining a Hadoop cluster and its related components. You will learn how to install, maintain, monitor, troubleshoot, optimize, and secure Hadoop. Introduction Hadoop history and concepts Ecosystem Distributions High level architecture Hadoop myths Hadoop challenges (hardware / software) Planning and installation Selecting software and Hadoop distributions Sizing the cluster and planning for growth Selecting hardware and network Rack topology Installation Multi-tenancy Directory structure and logs Benchmarking HDFS operations Concepts (horizontal scaling, replication, data locality, rack awareness) Nodes and daemons (NameNode, Secondary NameNode, HA Standby NameNode, DataNode) Health monitoring Command-line and browser-based administration Adding storage and replacing defective drives MapReduce operations Parallel computing before MapReduce: compare HPC versus Hadoop administration MapReduce cluster loads Nodes and Daemons (JobTracker, TaskTracker) MapReduce UI walk through MapReduce configuration Job config Job schedulers Administrator view of MapReduce best practices Optimizing MapReduce Fool proofing MR: what to tell your programmers YARN: architecture and use Advanced topics Hardware monitoring System software monitoring Hadoop cluster monitoring Adding and removing servers and upgrading Hadoop Backup, recovery, and business continuity planning Cluster configuration tweaks Hardware maintenance schedule Oozie scheduling for administrators Securing your cluster with Kerberos The future of Hadoop

Criminal intelligence and crime analysis are important tools used by law enforcement agencies to help identify and solve crimes. Criminal intelligence involves gathering, analysing, and disseminating information about criminal activity and potential threats. In contrast, crime analysis involves using data and other information to identify patterns and trends in criminal activity, which can help law enforcement agencies to better allocate their resources and develop strategies to prevent and solve crimes. Criminal intelligence and crime analysis are important and growing fields within law enforcement. Gaining expertise in these areas can open up opportunities for advancement and leadership roles within an agency. In addition to traditional law enforcement roles, there is also a wide range of non-law enforcement jobs in the field of criminal intelligence and crime analysis, including positions in government agencies, private consulting firms, and other organisations. Many of these positions require a strong background in data analysis, research, and critical thinking, which can be gained through training and education in criminal intelligence and crime analysis. Learning Outcomes: Intelligence cycle: The process of collecting, analysing, and disseminating intelligence, including the use of various sources of information and the importance of accuracy and timeliness. Crime analysis: Techniques for analysing crime data and identifying patterns, trends, and hot spots. Intelligence-led policing: The use of intelligence to inform policing strategies and tactics. Criminal psychology: Understand criminal psychology, counterterrorism and criminal intelligence analysis Enrol in the Criminal Intelligence and Crime Analysis course and learn a new professional skill from anywhere, at any time! Why choose Criminal Intelligence and Crime Analysis? Opportunity to boost your CV/Resume with CPD accredited proficiency Student ID card with amazing discounts - completely for FREE! (Postal charges will be applicable for international delivery) Instant results upon completion of each assessment Certificates available in both PDF and hard copy format Interactive, high-quality course content with 24/7 online access Full tutor support and learning assistance included Criminal intelligence and crime analysis involve using data and other information to identify patterns and trends in criminal activity and to help law enforcement agencies prevent and solve crimes. Criminal intelligence involves gathering and analysing information about potential threats and criminal activity and disseminating this information to relevant parties. ***Course Curriculum*** Here are the topics you will cover on the Criminal Intelligence and Crime Analysis Course. ***Criminal Intelligence and Crime Analysis*** Module 01: Introduction to Intelligence Analysis Module 02: Criminal Intelligence Analysis Module 03: Criminal Intelligence Analyst - Skills and Competencies Module 04: Understanding Criminal Psychology Module 05: Research Methods in Crime Analysis Module 06: Decision Making in Criminal Intelligence Analysis Module 07: Intelligence-Led Policing Module 08: Counterterrorism and Criminal Intelligence Analysis Module 09: Technology in Criminal Intelligence Analysis Module 10: The Criminal Justice System in England and Wales Assessment Process Once you have finished the learning stages in the course, your abilities will be assessed by an automated multiple-choice question session, after which you will receive the results immediately. CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Criminal Intelligence and Crime Analysis has been professionally-designed for motivated learners who are looking to add a new skill to their CV and stand head and shoulders above the competition. Learn the latest industry-specific information with the course and Crime Analysis. Enrol on the Criminal Intelligence and Crime Analysis and learn a new professional skill from anywhere, at any time! Requirements Enrol on the Criminal Intelligence and Crime Analysis with no formal entry requirements! If you have a passion for knowledge and want to learn something new, register on the Criminal Intelligence and Crime Analysis without any delay. We only ask that all students have a passion for learning and literacy, and be over the age of 16. Complete the Criminal Intelligence and Crime Analysis online from your computer, tablet, or smartphone, and learn in a way that suits you. Career path The Criminal Intelligence Analyst course may open up new employment opportunities and expand your career opportunities. Such as: - Investigating Officer Intelligence Officer Investigative Support Assistant Crime Scene Investigator Certificates CPD Accredited Certificate Digital certificate - £10 CPD Accredited Certificate Hard copy certificate - £29 If you are an international student, then you have to pay an additional 10 GBP as an international delivery charge.

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Overview This comprehensive course on Intelligence Analyst Certification Course will deepen your understanding on this topic.After successful completion of this course you can acquire the required skills in this sector. This Intelligence Analyst Certification Course comes with accredited certification, which will enhance your CV and make you worthy in the job market.So enrol in this course today to fast track your career ladder. How will I get my certificate? You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate. Who is This course for? There is no experience or previous qualifications required for enrolment on this Intelligence Analyst Certification Course. It is available to all students, of all academic backgrounds. Requirements Our Intelligence Analyst Certification Course is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 8 sections • 8 lectures • 06:03:00 total length •Defining Intelligence Analysis: 00:46:00 •Development of Intelligence Analysis: 00:49:00 •The Intelligence Cycle: 00:36:00 •Critical Thinking and Structuring: 00:39:00 •Analysis Process and Best Practice: 01:00:00 •Intelligence and National Security: 00:45:00 •Legal Issues and Ethics: 00:42:00 •Your Role, Responsibilities, and Functions as an Analyst: 00:46:00

Dive into the intricate world of data and uncover its mysteries with our 'Introduction to Data Analysis at QLS Level 3' course. As the adage goes, 'In God we trust, all others bring data'. This comprehensive course is tailored to make you fluent in the language of numbers and patterns. From understanding the voice of the process to exploring tools for data interpretation and mastering chart varieties like Pareto and Histogram, this curriculum is a bridge between raw data and actionable insights. Learning Outcomes Gain a foundational understanding of process management and its principles. Develop proficiency in using various analytical tools and charts. Understand and interpret data variations for actionable insights. Master techniques for presenting performance data efficiently. Learn strategies to address data variations and drive improvement. Why choose this Introduction to Data Analysis at QLS Level 3 course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Introduction to Data Analysis at QLS Level 3 Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this Introduction to Data Analysis at QLS Level 3 course for? Aspiring data analysts looking for a foundational course. Managers keen on using data to enhance business processes. Professionals aiming to integrate data-driven decision-making in their roles. Teams looking to streamline operations through data. Students considering a future in data analysis or related fields. Career path Data Analyst: £25,000 - £40,000 Business Intelligence Analyst: £30,000 - £50,000 Process Manager: £35,000 - £55,000 Performance Data Presenter: £28,000 - £45,000 Quality Control Specialist: £27,000 - £43,000 Operational Improvement Manager: £38,000 - £60,000 Prerequisites This Introduction to Data Analysis at QLS Level 3 does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Introduction to Data Analysis at QLS Level 3 was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £85 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. Course Curriculum Introduction to Data Analysis Module 01: Introduction 00:02:00 Module 02: Agenda and Principles of Process Management 00:06:00 Module 03: The Voice of the Process 00:05:00 Module 04: Working as One Team for Improvement 00:04:00 Module 05: Exercise: The Voice of the Customer 00:03:00 Module 06: Tools for Data Analysis 00:07:00 Module 07: The Pareto Chart 00:03:00 Module 08: The Histogram 00:03:00 Module 09: The Run Chart 00:04:00 Module 10: Exercise: Presenting Performance Data 00:05:00 Module 11: Understanding Variation 00:06:00 Module 12: The Control Chart 00:06:00 Module 13: Control Chart Example 00:04:00 Module 14: Control Chart Special Cases 00:06:00 Module 15: Interpreting the Control Chart 00:10:00 Module 16: Control Chart Exercise 00:07:00 Module 17: Strategies to Deal with Variation 00:06:00 Module 18: Using Data to Drive Improvement 00:14:00 Module 19: A Structure for Performance Measurement 00:06:00 Module 20: Data Analysis Exercise 00:06:00 Module 21: Course Project 00:03:00 Module 22: Test your Understanding 00:17:00 Additional Resources Resources - Introduction to Data Analysis 00:00:00 Mock Exam Mock Exam - Introduction to Data Analysis at QLS Level 3 00:20:00 Final Exam Final Exam - Introduction to Data Analysis at QLS Level 3 00:20:00 Assignment Assignment - Introduction to Data Analysis at QLS Level 3 00:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

48-Hour Knowledge Knockdown! Prices Reduced Like Never Before! Are you looking to improve your current abilities or make a career move? Our unique Intelligence Analyst Certification course might help you get there! Expand your expertise with high-quality training - study the Intelligence Analyst Certification course and get an expertly designed, great-value training experience. Learn from industry professionals and quickly equip yourself with the specific knowledge and skills you need to excel in your chosen career through theonline training course. The Intelligence Analyst Certification course is broken down into several in-depth modules to provide you with the most convenient and rich learning experience possible. Upon successful completion of the Intelligence Analyst Certification course, an instant e-certificate will be exhibited in your profile that you can order as proof of your skills and knowledge. Add these amazing new skills to your resume and boost your employability by simply enrolling in this course. This Intelligence Analyst Certification training can help you to accomplish your ambitions and prepare you for a meaningful career. So, join us today and gear up for excellence! Why Prefer Us? Opportunity to earn a certificate accredited by CPDQS. Get a free student ID card!(£10 postal charge will be applicable for international delivery) Innovative and Engaging Content. Free Assessments 24/7 Tutor Support. Take a step toward a brighter future! *** Course Curriculum *** Here is the curriculum breakdown of the Intelligence Analyst Certification course: Intelligence Analyst Certification Course Module 01: Defining Intelligence Analysis Module 02: Development of Intelligence Analysis Module 03: The Intelligence Cycle Module 04: Critical Thinking and Structuring Module 05: Analysis Process and Best Practice Module 06: Intelligence and National Security Module 07: Legal Issues and Ethics Module 08: Your Role, Responsibilities, and Functions as an Analyst Assessment Process Once you have completed all the modules in the Intelligence Analyst Certification course, you can assess your skills and knowledge with an optional assignment. Certificate of Completion The learners have to complete the assessment of this Intelligence Analyst Certification course to achieve the CPDQS accredited certificate. Digital Certificate: £10 Hard Copy Certificate: £29 (Inside UK) Hard Copy Certificate: £39 (for international students) CPD 10 CPD hours / points Accredited by CPD Quality Standards Who is this course for? Anyone interested in learning more about the topic is advised to take this Intelligence Analyst Certification course. This course is open to everybody. Requirements You will not need any prior background or expertise to enrol in this Intelligence Analyst course. Career path After completing this Intelligence Analyst course, you are to start your career or begin the next phase of your career. Certificates CPD Accredited Certificate Digital certificate - £10 CPD Accredited Certificate Hard copy certificate - £29 If you are an international student, then you have to pay an additional 10 GBP as an international delivery charge.

About Course Data Science and Data Analytics with Python: A Comprehensive Course for Beginners Unlock the power of data and gain insights that drive informed decisions with this comprehensive course on data science and data analytics with Python. This course is designed for beginners of all skill levels, with no prior programming experience required. You will learn the essential skills to embark on your data-driven journey, including: Data manipulation with NumPy and Pandas Data visualization with Matplotlib and Seaborn Statistical analysis with Python Machine learning and artificial intelligence You will also gain hands-on experience with real-world data projects, allowing you to apply your newfound knowledge to solve real-world problems. By the end of this course, you will be able to: Understand the fundamentals of data science and data analytics Apply Python to manipulate, visualize, and analyze data Use Python to build machine learning and artificial intelligence models Solve real-world data problems This course is the perfect launchpad for your data science journey. Whether you are looking to pivot your career, enhance your skill set, or simply quench your curiosity, this course will give you the foundation you need to succeed. Enroll today and start exploring the fascinating world of data science together! What Will You Learn? Understand the fundamentals of data science and data analytics Apply Python to manipulate, visualize, and analyze data Use Python to build machine learning and artificial intelligence models Solve real-world data problems Course Content Introduction to Python Data Science Introduction to Python Data Science Environment Setup Data Cleaning Packages Working with the Numpy package Working with Pandas Data science package Data Visualization Packages Working with Matplotlib Data Science package (Part - 1) Working with Matplotlib Data Science (Part - 2) A course by Uditha Bandara Microsoft Most Valuable Professional (MVP) RequirementsBeginners level knowledge for working with Data .Programming knowledge not required. Audience Beginners with no prior programming experience Anyone interested in learning data science and data analytics Audience Beginners with no prior programming experience Anyone interested in learning data science and data analytics

AI in Project Management: The Next Generation of Project Decision Making Project managers need to make critical project decisions on a daily basis. They are confronted with increasing complexities, high ambiguity and the need to process an exponentially growing amount of data and information in order to make informed and good decisions. This leads to an increasing risk of project failure - meanwhile, the project management industry is already challenged with ongoing low project success rates, caused by often massive failures of projects. Project Data Analytics and Artificial Intelligence (AI) are expected to fill the gap by providing analytical and unbiased capabilities that go beyond human possibilities, towards a data-driven and fact-based decision-making approach. While there is little doubt that AI as a trending technology will disrupt the project management practice and augment today's project management capabilities, AI cannot be seen as just another new tool to make project management more effective. Rather, AI will act as a complement to human intelligence, requiring a collaborative approach and, accordingly, a significant change in project culture and peoples' mindset. Today's project decisions are usually driven by human intuition, experience, leadership, and often do not follow any rational logic. Project decision-makers will be required to abandon such an approach and shift to a data-driven, decision-making approach. This session will provide an overview of the expected changes from AI-driven project management, the resulting impact on project decision making and changes in project culture, and what actions can be taken by project professionals to match their beliefs and behaviours with the new project culture. Learning goals: Gain insights into how AI for project management will significantly change decision-making in projects Gain an understanding of how to transition to a new AI-powered project culture

Duration 1 Days 6 CPD hours This course is intended for Report authors wanting to develop interactive report content, or content disconnected from IBM Cognos servers. In this course, participants increase their IBM Cognos Analytics experience by building interactive reports using Active Report controls, which can be distributed to and consumed by users in a disconnected environment, including mobile devices. Introduction to IBM Cognos Active Reports Examine IBM Cognos Active Reports Convert an existing report into an Active Report Add interactions in Active Reports using Active Report connections Create a basic Active Report Examine interactive behavior of Active Report controls Save a report in the IBM Cognos Analytics portal Save an Active Report to an MHT file Save an Active Report as a report template Use an Active Report as a prompt page Understand Active Report security Use Active Report Connections Examine Active Report connections Filter and select in controls using Active Report connections Examine variables Use a single variable to control multiple controls Use multiple variables to show different data in different controls Use Active Report controls to support mobile device usage Active Report Charts & Decks Add charts to active reports Understand and optimize chart behavior Examine decks and data decks Optimize use of decks Review Master Detail relationships Examine RAVE visualizations

Duration 1 Days 6 CPD hours This course is intended for This basic course is for users and developers familiar with earlier versions of IBM InfoSphere Information Server or IBM InfoSphere MDM who want to learn about new features in V11.3 Overview The objectives of this course are as follows:- Learn about the new features of DataStage V11.3- Learn about the new features of Information Analyzer V11.3- Learn about the new features of Data Click V11.3- Learn about the new features of the Information Governance Catalog V11.3 This course is designed to introduce you to new features in data integration and governance in IBM InfoSphere Information Server V11.3 and IBM InfoSphere MDM V11.3. Outline Unit DS: New Features in IBM InfoSphere DataStage V11.3 Unit DC: New Features in IBM InfoSphere Data Click V11.3 Unit IA: New Features in IBM InfoSphere Information Analyzer V11.3 **All units are accompanied by hands-on lab exercises. Additional course details: Nexus Humans KM650 IBM What is New in IBM InfoSphere Data Integration and Governance? V11.3 training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the KM650 IBM What is New in IBM InfoSphere Data Integration and Governance? V11.3 course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.