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Standard Edition of the Deep Dive into Core Java Programming. An approach to learning Java that is both practical and effective. Become an expert in Java.

Description Business Intelligence Diploma In today's fast-paced business world, understanding and utilising data is pivotal. The Business Intelligence Diploma offers individuals an online platform to gain a comprehensive understanding of the domain of business intelligence (BI). By embarking on this digital learning journey, participants will gain vital knowledge in areas such as data collection, warehousing solutions, and visualization techniques. Beginning with an introduction to Business Intelligence, learners will quickly gain a solid foundation. The importance of efficiently gathering and managing data is undisputed in modern businesses. This course provides a thorough understanding of data collection and management, ensuring that students are well-equipped to handle vast amounts of information. An equally important component of BI is how data is stored. The Business Intelligence Diploma covers data warehousing solutions, ensuring participants know how to store, retrieve, and manage data in an efficient manner. Once data is securely stored, the next step is to make sense of it. Through this course's focus on data visualization techniques, learners will discover how to present data in clear, compelling formats. Of course, data alone is just a tool. The true value comes from using this data to make informed decisions. The course delves into analytical models and algorithms, providing students with the means to interpret complex data sets and extract meaningful insights. These insights then play a pivotal role in business decision-making, another key area covered by the Business Intelligence Diploma. KPIs (Key Performance Indicators) and performance metrics are crucial elements in evaluating the success and impact of business initiatives. In the Business Intelligence Diploma, participants will learn to identify, measure, and analyse these metrics, ensuring that business strategies align with objectives and goals. Implementing business intelligence within an organisation is not without its challenges. Thus, the diploma also touches on BI implementation and change management. This ensures that students are prepared not only to understand BI but to lead its integration within their organisations. The business landscape is ever-evolving, with new trends constantly emerging. Staying abreast of these changes is crucial. The Business Intelligence Diploma provides insights into emerging trends in the BI world, ensuring that learners remain at the forefront of the industry. Lastly, measuring the impact and ROI (Return on Investment) of Business Intelligence is vital in evaluating its effectiveness. This course provides the tools and knowledge required to assess the tangible benefits of BI initiatives within an organisation. In conclusion, the Business Intelligence Diploma offers an online, comprehensive curriculum for those wishing to master the intricacies of business intelligence. From data collection to measuring BI's impact, this course ensures that its participants are well-equipped to thrive in the ever-evolving world of business. By completing this diploma, individuals will undoubtedly be better prepared to harness the power of data and use it to drive informed business decisions. Join the Business Intelligence Diploma today and embark on a transformative digital learning experience. What you will learn 1:Introduction to Business Intelligence 2:Data Collection and Management 3:Data Warehousing Solutions 4:Data Visualization Techniques 5:Analytical Models and Algorithms 6:Business Intelligence in Decision Making 7:KPIs and Performance Metrics 8:BI Implementation and Change Management 9:Emerging Trends in Business Intelligence 10:Measuring the Impact and ROI of Business Intelligence Course Outcomes After completing the course, you will receive a diploma certificate and an academic transcript from Elearn college. Assessment Each unit concludes with a multiple-choice examination. This exercise will help you recall the major aspects covered in the unit and help you ensure that you have not missed anything important in the unit. The results are readily available, which will help you see your mistakes and look at the topic once again. If the result is satisfactory, it is a green light for you to proceed to the next chapter. Accreditation Elearn College is a registered Ed-tech company under the UK Register of Learning( Ref No:10062668). After completing a course, you will be able to download the certificate and the transcript of the course from the website. For the learners who require a hard copy of the certificate and transcript, we will post it for them for an additional charge.

Dive into the world of numbers and patterns with our 'Statistical Analysis Course,' where data tells stories and predictions shape the future. In the first module, you're introduced to the vast landscape of statistics, a toolset essential for deciphering the tales hidden within data. As you progress, familiarise yourself with the fundamental statistical terminology, paving the way for a deeper grasp of how data clusters around central values. The journey through this course is a blend of theory and application, from mastering the intricacies of data variability to the advanced realms of regression analysis and predictive algorithms. Learning Outcomes Gain a solid understanding of statistics and its significance in various fields. Learn to describe and utilise basic statistical terminology and methods. Comprehend and calculate measures of central tendency and data dispersion. Develop skills in probability, distribution analysis, and statistical inference. Apply statistical methods correctly and appreciate the Bayesian approach for learning from data. Why choose this Statistical Analysis 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 Statistical Analysis Course 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 Statistical Analysis Course for? Aspiring data analysts seeking a foundation in statistics. Business professionals who require analytical skills for data-driven decision-making. Students of the social sciences, economics, or any field involving data interpretation. Researchers needing a robust grasp of statistical analysis methods. Anyone interested in understanding how to utilise data for predictions and analytics. Career path Data Analyst - £25,000 to £40,000 Market Research Analyst - £23,000 to £35,000 Quantitative Analyst - £35,000 to £70,000 Statistical Researcher - £27,000 to £45,000 Business Intelligence Analyst - £30,000 to £55,000 Econometrician - £30,000 to £60,000 Prerequisites This Statistical Analysis Course does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Statistical Analysis Course 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. Course Curriculum Module 01: The Realm of Statistics The Realm Of Statistics 00:26:00 Module 02: Basic Statistical Terms Basic Statistical Terms 00:41:00 Module 03: The Center of the Data The Center of the Data 00:03:00 Module 04: Data Variability Data Variability 00:15:00 Module 05: Binomial and Normal Distributions Binomial and Normal Distributions 00:14:00 Binomial Probabilities Table 00:00:00 Z-Table 00:00:00 Module 06: Introduction to Probability Introduction to Probability 00:35:00 Module 07: Estimates and Intervals Estimates and Intervals 00:34:00 Module 08: Hypothesis Testing Hypothesis Testing 00:31:00 Module 09: Regression Analysis Regression Analysis 00:11:00 Module 10: Algorithms, Analytics and Predictions Algorithms, Analytics and Prediction 00:47:00 Module 11: Learning From Experience: The Bayesian Way Learning From Experience: The Bayesian Way 00:31:00 Module 12: Doing Statistics: The Wrong Way Doing Statistics: The Wrong Way 00:37:00 Module 13: How We Can Do Statistics Better How We Can Do Statistics Better 00:41:00 Assignment Assignment - Statistical Analysis Course 00:00:00

If you are new to Quantum computing, then this course will help you understand the fundamentals and practicalities of this field. This course will provide you with step-by-step guidance in learning the implementation and important methodologies associated with Quantum computing in a beginner-friendly environment.

This course aims to teach the programming language C++ with an emphasis on the modern features introduced in C++17. The course will cover both old and new concepts in C++, including classes, operator overloading, inheritance, polymorphism, templates, and concurrency. By the end of the course, the students will have gained the knowledge needed to become proficient C++ developers.

Are you fascinated with Netflix and YouTube recommendations and how they accurately recommend content that you would like to watch? Are you looking for a practical course that will teach you how to build intelligent recommendation systems? This course will show you how to build accurate recommendation systems in Python using real-world examples.

In this course, you will quickly learn how to build DNNs (Deep Neural Networks) and how to train them. This learning-by-doing course will also help you master the elementary concepts and methodology with Python. You need to have a basic knowledge of python to get the best out of this course.

Level 5 QLS Endorsed Course | Endorsed Certificate Included | Plus 5 Career Guided Courses | CPD Accredited

Sales Overview: Roadway/Highway Design and Engineering Software Are you ready to revolutionize your roadway and highway projects? Introducing our comprehensive Roadway/Highway Design and Engineering software, meticulously crafted to streamline every aspect of your design process. From initial conceptualization to final construction, our software offers a suite of modules tailored to meet the diverse needs of modern transportation infrastructure projects. 1. Introduction To Roadway/Highway Design and Engineering: Kickstart your projects with a solid foundation in roadway and highway design principles. Our software provides an intuitive introduction module, offering users a clear understanding of the fundamentals essential for successful project execution. With interactive tutorials and detailed resources, users can quickly familiarize themselves with industry best practices and regulatory requirements. 2. Geometric Design of Roadways/Highways: Efficient and safe roadway geometry is paramount to any transportation project. Our software's Geometric Design module empowers engineers to create optimal road alignments, intersections, and transitions with precision and ease. Through advanced algorithms and customizable parameters, users can simulate various design scenarios to achieve optimal traffic flow and safety standards. 3. Pavement Design for Roadways/Highways: Ensure the longevity and performance of your road surfaces with our Pavement Design module. Tailored to accommodate diverse traffic loads and environmental conditions, our software offers state-of-the-art pavement analysis tools. From flexible to rigid pavements, our algorithms optimize material selection and thickness design, empowering engineers to deliver sustainable infrastructure solutions. 4. Drainage Design for Roadways/Highways: Combat water accumulation and mitigate flood risks with our Drainage Design module. Our software integrates hydraulic modeling and stormwater management techniques to design efficient drainage systems for roadways and highways. With intuitive interfaces and predictive analysis capabilities, engineers can confidently implement drainage solutions that meet regulatory standards and minimize environmental impact. 5. Materials and Construction: Seamlessly transition from design to construction with our Materials and Construction module. Access a comprehensive database of construction materials and techniques, complete with cost estimations and procurement guidelines. Whether it's asphalt mixes or bridge components, our software empowers project stakeholders to make informed decisions and optimize construction processes for efficiency and quality. 6. Environmental Considerations in Roadway/Highway Design and Engineering: Embrace sustainability and environmental stewardship in every phase of your project with our Environmental Considerations module. From ecological impact assessments to carbon footprint analyses, our software equips engineers with the tools to minimize environmental disturbances and enhance project sustainability. With built-in compliance checks and mitigation strategies, users can navigate regulatory requirements with confidence while preserving natural resources. Experience the future of roadway and highway design with our cutting-edge software solution. Empower your team to deliver innovative infrastructure projects that prioritize safety, efficiency, and sustainability. Contact us today to learn more about how our Roadway/Highway Design and Engineering software can elevate your projects to new heights. Course Curriculum Module 1: Introduction To Roadway/Highway Design and Engineering Introduction To Roadway/Highway Design and Engineering 00:00 Module 2: Geometric Design of Roadways/Highways Geometric Design of Roadways/Highways 00:00 Module 3: Pavement Design for Roadways/Highways Pavement Design for Roadways/Highways 00:00 Module 4: Drainage Design for Roadways/Highways Drainage Design for Roadways/Highways 00:00 Module 5: Materials and Construction Materials and Construction 00:00 Module 6: Environmental Considerations in Roadway/Highway Design and Engineering Environmental Considerations in Roadway/Highway Design and Engineering 00:00

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