119 Data Mining courses

This is a comprehensive and practical Apache Spark course. In this course, you will learn and master the art of framing data analysis problems as Spark problems through 20+ hands-on examples, and then scale them up to run on cloud computing services. Explore Spark 3, IntelliJ, Structured Streaming, and a stronger focus on the DataSet API.

Duration 3 Days 18 CPD hours This course is intended for This course is intended for beginner to intermediate business and requirements analysts who are looking to improve their elicitation and requirements writing and documentation skills. This course is also a great fit for technical writers, product and software testers, project managers, product owners who work closely with business analysts or who perform some for of business analysis themselves. Overview Understand the role of the business analyst and core competencies for performing successfully Discuss the criticality of business analysis and requirements for successful project outcomes Understand the main professional associations and standards supporting business analysts in the industry Discuss the common problems with requirements and explore approaches to address these issues Obtain a clear understanding of the various requirements types and the significance for eliciting each type Demonstrate your ability to identify stakeholders Explore various methods for understanding and analyzing stakeholders Discuss and apply good planning practices to requirements elicitation efforts Obtain knowledge and understanding of over 15 current and commonly applied elicitation techniques Understand how to progress from elicitation to analysis to documentation Write well-formed and validated requirements Gain understanding of the best practices for writing quality requirements Learn the technical writing techniques that apply directly to writing requirements documents Discuss writing pitfalls, risks that impact requirements, and how to address them Learn best practices for communicating and collaborating with stakeholders, sharing the results of elicitation and the resulting documentation Learn approaches for validating requirements Understand the difference between validating requirements and validating the solution With elicitation serving as a major component of the requirements process, it is imperative that business analysts maintain high competency levels in elicitation practices and technique use to help organizations overcome the requirements related challenges faced on projects. Regardless whether you are a practitioner just starting off your career in business analysis or whether you have been performing the role for some years, this course will provide insight into the latest thoughts on elicitation and writing effective requirements and present a number of current techniques that are being applied on projects across industries today. REVIEW OF FOUNDATIONAL CONCEPTS * Definition of a business analysis * Definition of business analyst * BA role vs. PM role * Business analysis competencies * Benefits of business analysis * Purpose for having a BA standard * IIBA?s BABOK© Guide and PMI?s Practice Guide in Business Analysis * Business analysis core concepts * Discussion: Project challenges UNDERSTANDING REQUIREMENTS * Common problems with requirements * Understand the problem first * Define the business need * Situation statements and moving to requirements * Understanding requirement types * Business requirements * Stakeholder/User Requirements * Solution Requirements * Functional Requirements * Non-Functional Requirements * Assumptions and Constraints * Discussions: Requirement problems, business needs, and identifying non-functional requirements DISCOVERING STAKEHOLDERS * Definition of a stakeholder * Stakeholder types * Identifying stakeholders * Performing stakeholder analysis * Stakeholders and requirements * Tips for identifying stakeholders * Grouping stakeholders * Creating a RACI model * Tips for analyzing stakeholders * Documenting results of stakeholder analysis * Workshop: Discovering stakeholders PREPARING FOR REQUIREMENTS ELICITATION * Planning for elicitation * Benefits of elicitation planning * What do you plan? * The elicitation plan * Setting objectives for elicitation * Determining the scope for elicitation * Establishing pre-work * Determining the outputs for the session * The iterative nature of elicitation * Elicitation roles * Elicitation planning techniques * Discussions: Who to involve in elicitation, planning impacts, and unplanned elicitation * Workshop: Planning for elicitation CONDUCT REQUIREMENTS ELICITATION * Elicitation skillset * Types of elicitation techniques * Using active listening in elicitation * Techniques for performing elicitation * Benchmarking/Market Analysis * Brainstorming * Business Rules Analysis * Collaborative Games * Concept Modeling * Data Mining * Data Modeling * Document Analysis * Focus Groups * Interface Analysis * Interviews * Observation * Process Modeling * Prototyping * Survey or Questionnaire * Workshops WRITE EFFECTIVE REQUIREMENTS * Elicitation and Analysis * Requirements related issues * Implications of bad requirements * Elicitation and documentation * Writing skillset * Documenting requirements * Modeling requirements * Defining the project life cycle * Impact of project life cycle on documentation * Requirements specifications * Characteristics of good requirements * Guidelines for writing textual requirements * Structuring a requirement * Writing pitfalls * Traceability * Requirements attributes * Risks associated to requirements * Discussions: Project Life Cycle and Correcting Poorly Written Requirements * Workshops: Documenting Requirements and Identify Characteristics of Good Requirements CONFIRM AND COMMUNICATE ELICITATION REQUIREMENTS * Business analysis communication * Requirements communication * Communication skills * The 7 Cs * Timing of communication * Planning communication * Importance of Collaboration * Planning collaboration * Documenting communication/collaboration needs * Confirming elicitation results * Verify requirements * Characteristics of good requirements (revisited) * Requirements checklist * Requirements validation * Signing off on requirements * Discussions: Responsibility for Communication, Eliciting Communication Needs, Validation * Signoff * Workshops: Communicating Requirements and Obtaining Signoff EVALUATE THE SOLUTION * Business analyst role in solution evaluation * Why solutions under perform * What we are looking for in solution evaluation * When does solution evaluation occur * Performing solution evaluation * Planning solution evaluation * Metrics that might exist * Evaluating long term performance * Qualitative vs. quantitative measures * Tools & techniques used in solution evaluation * Comparing expected to actuals * When solution evaluation discovers a variance * Tools/techniques for analyzing variances * Proposing a recommendation * Communicating results of solution evaluation * Discussion: Addressing Variance WRAP UP AND NEXT STEPS * Useful books and links on writing effective requirements * BABOK© Guide * Business Analysis for Practitioners: A Practice Guide ADDITIONAL COURSE DETAILS: Nexus Humans BA04 - Eliciting and Writing Effective Requirements 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 BA04 - Eliciting and Writing Effective Requirements 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.

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.

Get involved in a learning adventure, mastering R from foundational basics to advanced techniques. This course is a gateway to the realm of data science. Explore statistical machine learning models and intricacies of deep learning and create interactive Shiny apps. Unleash the power of R and elevate your proficiency in data-driven decision-making.

This course covers the important topics needed to pass the AWS Certified Data Analytics-Specialty exam (AWS DAS-C01). You will learn about Kinesis, EMR, DynamoDB, and Redshift, and get ready for the exam by working through quizzes, exercises, and practice exams, along with exploring essential tips and techniques.

"The best way to predict the future is to create it." In today's data-driven world, the demand for skilled Data Analysts is skyrocketing. From businesses harnessing vast amounts of information to predict market trends to policymakers leveraging insights for a better future, the Data Analyst stands at the epicentre of these transformations. As a Data Analyst, you'll have the tools to turn raw data into actionable insights, driving strategic decisions for businesses worldwide. With the right training, a Data Analyst can decipher the narratives behind numbers, revealing trends, and crafting effective business strategies. Immerse yourself in our carefully curated curriculum and transform from a novice to an in-demand Data Analyst, ready to navigate the ever-evolving data landscape. The future beckons, and it's data-rich. Become the Data Analyst the industry needs today! Learning Outcomes: Here is a glimpse of what you are going to learn with us: * Understand data analysis concepts and techniques. * Learn how to collect, clean and process data. * Learn how to visualise data using different tools and techniques. * Understand the principles of Google Data Studio. * Learn how to use machine learning algorithms to analyse data. * Learn how to use data mining techniques to discover patterns in forecasting. * Take your Excel skills to the next level. Our comprehensive Data Analyst (Data Analytics) Endorsed training is designed to equip you with the essential skills and knowledge needed to excel in this dynamic field. Course 01: Introduction to Data Analysis Every aspiring Data Analyst must begin with a solid foundation. "Introduction to Data Analysis" is the first step for budding Data Analysts, designed to equip you with the foundational concepts integral to the field. Understand the significance and role of a Data Analyst in today's data-driven world and grasp the methodologies that drive actionable insights from vast datasets. Course 02: Google Data Studio: Data Analytics In the toolbox of every proficient Data Analyst is a mastery of data visualisation tools. This course dives deep into Google Data Studio, an essential platform for Data Analysts. Learn how to transform complex data into compelling visual narratives, making your insights accessible and actionable for stakeholders. Course 03: Data Analysis & Forecasting The mark of an expert Data Analyst lies in prediction. "Data Analysis & Forecasting" equips Data Analysts with the skills to anticipate future trends from historical data. Delve into forecasting techniques, ensuring you stand out as a forward-thinking Data Analyst, always one step ahead in strategic decision-making. This unparalleled bundle provides the skills and knowledge you need to become a proficient data analyst. So, don't wait any longer. Enrol now and start learning. Quality Licence Scheme Endorsed Certificate of Achievement: Upon successful completion of the course, you will be eligible to order an original hardcopy certificate of achievement. This prestigious certificate, endorsed by the Quality Licence Scheme, will be titled 'Diploma in Data Analyst (Data Analytics) at QLS Level 5'. Your certificate will be delivered directly to your home. The pricing scheme for the certificate is as follows: * £109 GBP for addresses within the UK. Please note that delivery within the UK is free of charge. Please Note: NextGen Learning is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. CPD 150 CPD hours / points Accredited by CPD Quality Standards COURSE 01: INTRODUCTION TO DATA ANALYSIS 2:09:44 * 1: Module 01: Introduction Preview 02:22 * 2: Module 02: Agenda and Principles of Process Management Preview 06:28 * 3: Module 03: The Voice of the Process 04:35 * 4: Module 04: Working as One Team for Improvement 04:18 * 5: Module 05: Exercise: The Voice of the Customer 03:05 * 6: Module 06: Tools for Data Analysis 07:10 * 7: Module 07: The Pareto Char 02:51 * 8: Module 08: The Histogram 02:45 * 9: Module 09: The Run Chart 03:47 * 10: Module 10: Exercise: Presenting Performance Data 04:42 * 11: Module 11: Understanding Variation 06:05 * 12: Module 12: The Control Chart 07:22 * 13: Module 14: Control Chart Special Cases 06:13 * 14: Module 15: Interpreting the Control Chart 09:42 * 15: Module 16: Control Chart Exercise 07:22 * 16: Module 17: Strategies to Deal with Variation 05:38 * 17: Module 18: Using Data to Drive Improvement 13:48 * 18: Module 19: A Structure for Performance Measurement 05:59 * 19: Module 20: Data Analysis Exercise 05:39 * 20: Module 21: Course Project 03:17 * 21: Module 22: Test your Understanding 16:36 COURSE 02: GOOGLE DATA STUDIO: DATA ANALYTICS 2:39:59 * 22: Module 01: Introduction to GDS 35:33 * 23: Module 02: Data Visualization 1:29:03 * 24: Module 03: Geo-visualization 15:36 * 25: Module 04: A Socio-Economic Case Study 19:47 COURSE 03: DATA ANALYSIS & FORECASTING 1:10:51 * 26: Excel Data Analysis - 1 03:49 * 27: Excel Data Analysis - 2 06:35 * 28: Excel Data Analysis - 3 03:23 * 29: Excel Data Analysis - 4 03:14 * 30: Excel Data Analysis - 5 10:52 * 31: Excel Data Analysis - 6 15:51 * 32: Excel Data Analysis - 7 02:00 * 33: Excel Data Analysis - 8 09:14 * 34: Excel Data Analysis - 9 15:53 ORDER YOUR CPD QUALITY STANDARD CERTIFICATE (OPTIONAL) 01:00 * 35: CPD Quality Standard Certificate (Optional) 01:00 PDF ORDER YOUR QLS ENDORSED CERTIFICATE (OPTIONAL) 01:00 * 36: QLS Endorsed Certificate (Optional) 01:00 PDF WHO IS THIS COURSE FOR? Besides, our Data Analyst (Data Analytics) Endorsed training can empower employees, business owners, students, and anyone interested in data analysis to make data-driven decisions. * Aspiring Data Analysts * Data enthusiasts * Business professionals * Data specialists * Managers and decision-makers * Career-driven individuals REQUIREMENTS Without any formal requirements, you can delightfully enrol in this Data Analyst (Data Analytics) Endorsed training. CAREER PATH Get edges over others after completing our endorsed training. There are thousands of career opportunities available. Such as: * Data Analyst: £30,000 - £55,000 * Business Intelligence Analyst: £35,000 - £65,000 * Data Scientist: £45,000 - £90,000 * Big Data Analyst: £35,000 - £80,000 * Marketing Data Analyst: £25,000 - £55,000 CERTIFICATES CPD QUALITY STANDARD CERTIFICATE Digital certificate - £4.99 HARDCOPY CERTIFICATE (FREE UK DELIVERY) Hard copy certificate - £9.99 Hardcopy Transcript: £9.99

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 [https://www.sevenmentor.com/machine-learning-course-in-pune.php] 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 [https://www.sevenmentor.com/machine-learning-course-in-pune.php] 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 [https://www.sevenmentor.com/machine-learning-course-in-pune.php]

This beginner's course exclusively delivers Python programming from basic to advanced. You will learn Python concepts in real-life programming examples by building real-world applications. Learn the syntax of Python language and understand the logic behind the programming process to create your Python programs successfully and master Python coding.

Duration 2 Days 12 CPD hours This course is intended for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful. Overview By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data. This course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You?ll discover how to tune the algorithms to provide the best predictions on new and unseen data. As you delve into later sections, you?ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. DATA EXPLORATION AND CLEANING * Python and the Anaconda Package Management System * Different Types of Data Science Problems * Loading the Case Study Data with Jupyter and pandas * Data Quality Assurance and Exploration * Exploring the Financial History Features in the Dataset * Activity 1: Exploring Remaining Financial Features in the Dataset INTRODUCTION TO SCIKIT-LEARN AND MODEL EVALUATION * Introduction * Model Performance Metrics for Binary Classification * Activity 2: Performing Logistic Regression with a New Feature and Creating a Precision-Recall Curve DETAILS OF LOGISTIC REGRESSION AND FEATURE EXPLORATION * Introduction * Examining the Relationships between Features and the Response * Univariate Feature Selection: What It Does and Doesn't Do * Building Cloud-Native Applications * Activity 3: Fitting a Logistic Regression Model and Directly Using the Coefficients THE BIAS-VARIANCE TRADE-OFF * Introduction * Estimating the Coefficients and Intercepts of Logistic Regression * Cross Validation: Choosing the Regularization Parameter and Other Hyperparameters * Activity 4: Cross-Validation and Feature Engineering with the Case Study Data DECISION TREES AND RANDOM FORESTS * Introduction * Decision trees * Random Forests: Ensembles of Decision Trees * Activity 5: Cross-Validation Grid Search with Random Forest IMPUTATION OF MISSING DATA, FINANCIAL ANALYSIS, AND DELIVERY TO CLIENT * Introduction * Review of Modeling Results * Dealing with Missing Data: Imputation Strategies * Activity 6: Deriving Financial Insights * Final Thoughts on Delivering the Predictive Model to the Client