146 Data Mining courses delivered Online

***24 Hour Limited Time Flash Sale*** Master GIS: ArcGIS for Hydrology, Spatial Analysis, Remote Sensing & Cartography Admission Gifts FREE PDF & Hard Copy Certificate| PDF Transcripts| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter Have you ever dreamed of turning your love for maps and data into a fulfilling career? The geospatial science field is booming in the UK, but a recent study by the Chartered Institute of Environmental Management revealed a shocking fact: 72% of environmental consultancies are struggling to find qualified professionals with the right GIS and data analysis skills. That's where our Master GIS program comes in! This Master GIS program provides in-depth training on ArcGIS, the industry-standard GIS software. You'll master essential skills like spatial analysis, remote sensing for data collection, and creating professional maps using both ArcGIS and open-source QGIS. Additionally, you'll gain expertise in Python programming, data visualisation, and web mapping, making you a highly sought-after geospatial professional. With this Master GIS: ArcGIS for Hydrology, Spatial Analysis, Remote Sensing & Cartography bundle, you will get 20 CPD Accredited PDF Certificates, Hard Copy Certificates, and our exclusive student ID card, all absolutely free. Courses Are Included In this Master GIS: ArcGIS for Hydrology, Spatial Analysis, Remote Sensing & Cartography Career Bundle: Course 01: Diploma in ArcGIS for Hydrology at QLS Level 4 Course 02: Award in Spatial Analysis in ArcGIS at QLS Level 2 Course 03: Remote Sensing in ArcGIS Course 04: QGIS Cartography Course 05: Business Intelligence and Data Mining Diploma Course 06: Python Data Science Course 07: Spatial Data Visualization and Machine Learning in Python Level 4 Course 08: Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 Course 09: Maps in R Shiny and Leaflet Course 10: Create Smart Maps in Python and Leaflet Level 3 Course 11: Advanced Diploma in Geology Course 12: Structural Geology, Tectonics & Rock Mechanics Course 13: Geospatial Technology, Remote Sensing and Web Mapping Course 14: Environmental Management Course 15: Environmental Law Course 16: Carbon Literacy Project Course 17: Forensic Anthropology Course 18: Autocad Design Course 19: Meteorology Course 20: Architectural Studies Invest in Your Future: Whether you're looking to launch a new career in GIS or enhance your existing skillset, this course bundle is your perfect starting point. Take your first step towards a rewarding career - enrol today! Learning Outcomes of Master GIS: ArcGIS for Hydrology Apply advanced ArcGIS techniques for hydrological analysis. Conduct spatial analysis to uncover hidden patterns in geospatial data. Utilise remote sensing imagery to extract valuable information. Create professional and informative maps using ArcGIS and QGIS. Analyse and visualise spatial data using Python libraries. Develop interactive web maps using Python and Leaflet. Why Choose Us? Get a Free CPD Accredited Certificate upon completion of Master GIS: ArcGIS for Hydrology Get a free student ID card with Master GIS: ArcGIS for Hydrology Training program (£10 postal charge will be applicable for international delivery) The Master GIS: ArcGIS for Hydrology is affordable and simple to understand This course is entirely online, interactive lesson with voiceover audio Get Lifetime access to the Master GIS: ArcGIS for Hydrology course materials The Master GIS: ArcGIS for Hydrology comes with 24/7 tutor support Take a step toward a brighter future! *** Course Curriculum *** Course 01: Diploma in ArcGIS for Hydrology at QLS Level 4 Section 01: Introduction To ArcGIS Software Introduction to ArcGIS software Selection by Location, attributes, clip features and tables to Excel Performing actions on the data: change the projections, dissolve, clip From .TXT and .DXF to shapefile Calculations with attribute table and KML files in ArcMap Export from ArcMap to PDF ArcScene 3D GIS Example Test AutoCAD fixing polygon coordinates Section 02: ArcGIS For Hydrology Download hydrological data Calculate flow direction and flow accumulation Watershed delineation Clip layers to watershed boundary Stream segments and catchments areas Stream order convert to vector Spatial join to catchments Join stream id to stream order Map data and initial layout Add and format map elements Course 02: Award in Spatial Analysis in ArcGIS at QLS Level 2 Module 01: Point Density Analysis Module 02: Raster Calculator and Vector Isolation Module 03: Raster to Topography Module 04: Raster Reclassification Module 05: Raster Overlay Module 06: Slope Analysis and Hydrology tools Module 07: Introduction to TIFF Files Module 08: Introduction to 3D Surfaces Module 09: Satellite Images and TIN Surfaces Module 10: Exercise Course 03: Remote Sensing in ArcGIS Module 01: Remote sensing, satellite images, spectral bands introduction Module 02: Layers stacking satellite images Module 03: Georeferencing satellite images Module 04: Introduction to geoprocessing raster tools Module 05: Raster Analysis Functions Module 06: Georeferencing toposheet Module 07: Site suitability using weighted overlay analysis - part 1 Module 08 Site suitability using weighted overlay analysis - part 2 Module 09: Watershed Delineation from DEM Module 10: Unsupervised classification =========>>>>> And 17 More Courses <<<<<========= How will I get my Certificate? After successfully completing the course, you will be able to order your Certificates as proof of your achievement. PDF Certificate: Free (Previously it was £12.99*20 = £259) CPD Hard Copy Certificate: Free ( For The First Course: Previously it was £29.99) QLS Endorsed Hard Copy Certificate: £79) CPD 200 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 bundle. This bundle is ideal for: GIS enthusiasts Environmental scientists Urban planners Data analysts Geographers Requirements You will not need any prior background or expertise to enrol in this course. Career path After completing this course, you are to start your career or begin the next phase of your career. GIS Analyst Cartographer Environmental Consultant Data Scientist Web Developer Planner Certificates CPD Accredited Digital Certificate Digital certificate - Included Upon passing the Course, you need to order a Digital Certificate for each of the courses inside this bundle as proof of your new skills that are accredited by CPD QS for Free. CPD Accredited Hard Copy Certificate Hard copy certificate - Included Please note that International students have to pay an additional £10 as a shipment fee. Award in Spatial Analysis in ArcGIS at QLS Level 2 Hard copy certificate - £79 Please note that International students have to pay an additional £10 as a shipment fee.

Unlock the potential of business intelligence with our specialized Business Intelligence Analyst Course. Learn to analyze data, extract insights, and drive strategic decisions to optimize business performance. Gain practical skills in data visualization, reporting, and predictive analytics using industry-leading tools and techniques. Whether you're a business professional or aspiring analyst, this course equips you with the expertise to excel in leveraging data for business intelligence.

Get ready for an exceptional online learning experience with the Data Analyst (Data Analytics)bundle! This carefully curated collection of 30 premium courses is designed to cater to a variety of interests and disciplines. Dive into a sea of knowledge and skills, tailoring your learning journey to suit your unique aspirations. The Data Analyst (Analytics) is a dynamic package, blending the expertise of industry professionals with the flexibility of digital learning. It offers the perfect balance of foundational understanding and advanced insights. Whether you're looking to break into a new field or deepen your existing knowledge, the Data Analyst (Analytics) package has something for everyone. As part of the Data Analyst (Analytics) package, you will receive complimentary PDF certificates for all courses in this bundle at no extra cost. Equip yourself with the Data Analyst (Analytics) bundle to confidently navigate your career path or personal development journey. Enrol today and start your career growth! This Bundle Comprises the Following Data Analyst (Data Analytics)CPD Accredited Courses: Course 01: Basic Data Analysis Course 02: Business Data Analysis Course 03: Introduction to Data Analytics with Tableau Course 04: Google Data Studio: Data Analytics Course 05: SQL Database Basics for Everyone Course 06: R Programming for Data Science Course 07: 2021 Data Science & Machine Learning with R from A-Z Course 08: Learn Python, JavaScript, and Microsoft SQL for Data science Course 09: Spatial Data Visualisation and Machine Learning in Python Course 10: Building Big Data Pipelines with PySpark MongoDB and Bokeh Course 11: Complete Python Machine Learning & Data Science Fundamentals Course 12: Clinical Data Management with SAS Programming Course 13: Certificate in Data Entry and Management Course 14: Quick Data Science Approach from Scratch Course 15: Web Mapping and Data Visualizations Course 16: Programming AutoCAD with SQL Server Database Using C# Course 17: Big Data Analytics with PySpark Power BI and MongoDB Course 18: Develop Big Data Pipelines with R & Sparklyr & Tableau Course 19: Develop Big Data Pipelines with R, Sparklyr & Power BI Course 20: Data Center Training Essentials: Power & Electrical Course 21: Business Intelligence and Data Mining Course 22: Set Menu Prices for your restaurant using data Course 23: Data Analysis In Excel Course 24: Data Protection Course 25: Reporting and Data Course 26: Career Development Plan Fundamentals Course 27: CV Writing and Job Searching Course 28: Networking Skills for Personal Success Course 29: Excel: Top 50 Microsoft Excel Formulas in 50 Minutes! Course 30: Decision Making and Critical Thinking What will make you stand out? Upon completion of this online Data Analyst (Data Analytics) bundle, you will gain the following: CPD QS Accredited Proficiency with this Data Analyst (Analytics) bundle After successfully completing the Data Analyst (Analytics) bundle, you will receive a FREE CPD PDF Certificates as evidence of your newly acquired abilities. Lifetime access to the whole collection of learning materials of this Data Analyst (Analytics) bundle The online test with immediate results You can study and complete the Data Analyst (Analytics) bundle at your own pace. Study for the Data Analyst (Analytics) bundle using any internet-connected device, such as a computer, tablet, or mobile device. Each course in this Data Analyst (Analytics) bundle holds a prestigious CPD accreditation, symbolising exceptional quality. The materials, brimming with knowledge, are regularly updated, ensuring their relevance. This bundle promises not just education but an evolving learning experience. Engage with this extraordinary collection, and prepare to enrich your personal and professional development. Embrace the future of learning with the Data Analyst (Analytics), a rich anthology of 30 diverse courses. Each course in the Data Analyst (Analytics) bundle is handpicked by our experts to ensure a wide spectrum of learning opportunities. ThisData Analyst (Analytics) bundle will take you on a unique and enriching educational journey. The bundle encapsulates our mission to provide quality, accessible education for all. Whether you are just starting your career, looking to switch industries, or hoping to enhance your professional skill set, the Data Analyst (Analytics) bundle offers you the flexibility and convenience to learn at your own pace. Make the Data Analyst (Data Analytics) package your trusted companion in your lifelong learning journey. CPD 300 CPD hours / points Accredited by CPD Quality Standards Who is this course for? The Data Analyst (Data Analytics) bundle is perfect for: Lifelong learners looking to expand their knowledge and skills. Professionals seeking to enhance their career with CPD certification. Individuals wanting to explore new fields and disciplines. Anyone who values flexible, self-paced learning from the comfort of home. Requirements You are cordially invited to enroll in this bundle; please note that there are no formal prerequisites or qualifications required. We've designed this curriculum to be accessible to all, irrespective of prior experience or educational background. Career path Unleash your potential with the Data Analyst (Data Analytics) bundle. Acquire versatile skills across multiple fields, foster problem-solving abilities, and stay ahead of industry trends. Ideal for those seeking career advancement, a new professional path, or personal growth. Embrace the journey with the Data Analyst (Analytics)bundle package. Certificates CPD Quality Standard Certificate Digital certificate - Included 30 CPD Quality Standard Certificates - Free

FREE PDF & Hard Copy Certificate|50 PDF and Hardcopy Certificates| FREE Student ID| Assessment| Lifetime Access| Enrolment Letter

Getting Started The main aim of the OTHM Level 7 Diploma in Data Science is to enhance the knowledge and skills required to extract business-oriented insights from data. This involves comprehending how value and information circulate in a business and utilising this knowledge to recognise potential business prospects.The main aim of the OTHM Level 7 Diploma in Data Science is to enhance the knowledge and skills required to extract business-oriented insights from data. This involves comprehending how value and information circulate in a business and utilising this knowledge to recognise potential business prospects. Key Benefits This qualification will bring you many vital benefits, such as; Learners can gain the essential subject knowledge needed to progress successfully into further study or the world of work. Refreshed content that is closely aligned with employer and higher education needs Develop a comprehensive knowledge of classical data analytics, including statistical inference, predictive modelling, time series analysis and data reduction. Become familiar with and use the tools and techniques used in data visualisation. Assessments that consider cognitive skills along with affective and applied skills Key Highlights Do you wish to be a Data Scientist? Then, The OTHM Level 7 Diploma in Data Science program offered by the School of Business and Technology London is the right solution for you. Remember! The assessment for the qualification is done based on assignments only, and you do not need to worry about writing any exam. With the School of Business and Technology London, you can complete the qualification at your own pace, choosing online or blended learning from the comfort of your home. Learning and pathway materials and study guides developed by our OTHM-approved tutors will be available around the clock in our cutting-edge learning management system. Most importantly, at the School of Business and Technology London, we will provide comprehensive tutor support through our dedicated support desk. If you choose your course with blended learning, you will also enjoy live sessions with an assigned tutor, which you can book at your convenience. Career Pathways The OTHM Level 7 Diploma in Data Science can open many career pathways including, but not limited to: Data scientist- Est. Salary £59,680 Data Analyst- Est. Salary £42,984 Business Analyst-Est. Salary £54,413   About Awarding Body OTHM is an established and recognised Awarding Organisation (Certification Body) launched in 2003. OTHM has already made a mark in the UK and global online education scenario by creating and maintaining a user-friendly and skill based learning environment. OTHM has both local and international recognition which aids OTHM graduates to enhance their employability skills as well as allowing them to join degree and/or Master top-up programmes. OTHM qualifications has assembled a reputation for maintaining significant skills in a wide range of job roles and industries which comprises Business Studies, Leadership, Tourism and Hospitality Management, Health and Social Care, Information Technology, Accounting and Finance, Logistics and Supply Chain Management. Learners must request before enrolment to interchange unit(s) other than the preselected units shown in the SBTL website because we need to make sure the availability of learning materials for the requested unit(s). SBTL will reject an application if the learning materials for the requested interchange unit(s) are unavailable. Learners are not allowed to make any request to interchange unit(s) once enrolment is complete. UNIT1- Data Science Foundations Reference No : Unit 1 - F/650/5562 Credit : 20 || TQT : 200 Hours This unit introduces various data science concepts, including data administration, governance, and big data sources. UNIT2- Probability and Statistics for Data Analysis Reference No : Unit 2 - H/650/5563 Credit : 20 || TQT : 200 Hours The objective of this unit is to offer a comprehensive introduction to the fundamental principles of probability and statistics, starting from the basics. It will cover a wide spectrum of data analysis procedures and methodologies. UNIT3- Advanced Predictive Modeling Reference No : Unit 3 - J/650/5564 Credit : 20 || TQT : 200 Hours You will become acquainted with key predictive modelling methods and their underlying foundational principles in this unit. UNIT4- Data Analysis and Visualisation Reference No : Unit 4 - K/650/5565 Credit : 20 || TQT : 200 Hours This unit serves as a crucial foundation for grasping the core concepts of the data analysis process, encompassing data collection, data cleansing, data analysis, and the effective communication of insights through visualisations and dashboard tools. UNIT5- Data Mining Machine Learning and Artificial Intelligence Reference No : Unit 5 - J/650/5573 Credit : 20 || TQT : 200 Hours The primary aim of this unit is to provide an introduction to the scientific principles underpinning machine intelligence and to explore the philosophical discourse surrounding the endeavour to simulate human intelligence for addressing real-world challenges. UNIT6- Advanced Computing Research Methods Reference No : Unit 6 - L/650/5566 Credit : 20 || TQT : 200 Hours This unit aims to enhance learners' skills in preparing for diverse forms of academic computing research by guiding them through creating and designing a research proposal. Delivery Methods School of Business & Technology London provides various flexible delivery methods to its learners, including online learning and blended learning. Thus, learners can choose the mode of study as per their choice and convenience. The program is self-paced and accomplished through our cutting-edge Learning Management System. Learners can interact with tutors by messaging through the SBTL Support Desk Portal System to discuss the course materials, get guidance and assistance and request assessment feedbacks on assignments. We at SBTL offer outstanding support and infrastructure for both online and blended learning. We indeed pursue an innovative learning approach where traditional regular classroom-based learning is replaced by web-based learning and incredibly high support level. Learners enrolled at SBTL are allocated a dedicated tutor, whether online or blended learning, who provide learners with comprehensive guidance and support from start to finish. The significant difference between blended learning and online learning methods at SBTL is the Block Delivery of Online Live Sessions. Learners enrolled at SBTL on blended learning are offered a block delivery of online live sessions, which can be booked in advance on their convenience at additional cost. These live sessions are relevant to the learners' program of study and aim to enhance the student's comprehension of research, methodology and other essential study skills. We try to make these live sessions as communicating as possible by providing interactive activities and presentations. Resources and Support School of Business & Technology London is dedicated to offering excellent support on every step of your learning journey. School of Business & Technology London occupies a centralised tutor support desk portal. Our support team liaises with both tutors and learners to provide guidance, assessment feedback, and any other study support adequately and promptly. Once a learner raises a support request through the support desk portal (Be it for guidance, assessment feedback or any additional assistance), one of the support team members assign the relevant to request to an allocated tutor. As soon as the support receives a response from the allocated tutor, it will be made available to the learner in the portal. The support desk system is in place to assist the learners adequately and streamline all the support processes efficiently. Quality learning materials made by industry experts is a significant competitive edge of the School of Business & Technology London. Quality learning materials comprised of structured lecture notes, study guides, practical applications which includes real-world examples, and case studies that will enable you to apply your knowledge. Learning materials are provided in one of the three formats, such as PDF, PowerPoint, or Interactive Text Content on the learning portal. How does the Online Learning work at SBTL? We at SBTL follow a unique approach which differentiates us from other institutions. Indeed, we have taken distance education to a new phase where the support level is incredibly high.Now a days, convenience, flexibility and user-friendliness outweigh demands. Today, the transition from traditional classroom-based learning to online platforms is a significant result of these specifications. In this context, a crucial role played by online learning by leveraging the opportunities for convenience and easier access. It benefits the people who want to enhance their career, life and education in parallel streams. SBTL's simplified online learning facilitates an individual to progress towards the accomplishment of higher career growth without stress and dilemmas. How will you study online? With the School of Business & Technology London, you can study wherever you are. You finish your program with the utmost flexibility. You will be provided with comprehensive tutor support online through SBTL Support Desk portal. How will I get tutor support online? School of Business & Technology London occupies a centralised tutor support desk portal, through which our support team liaise with both tutors and learners to provide guidance, assessment feedback, and any other study support adequately and promptly. Once a learner raises a support request through the support desk portal (Be it for guidance, assessment feedback or any additional assistance), one of the support team members assign the relevant to request to an allocated tutor. As soon as the support receive a response from the allocated tutor, it will be made available to the learner in the portal. The support desk system is in place to assist the learners adequately and to streamline all the support process efficiently. Learners should expect to receive a response on queries like guidance and assistance within 1 - 2 working days. However, if the support request is for assessment feedback, learners will receive the reply with feedback as per the time frame outlined in the Assessment Feedback Policy.

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

Limited Time Offer | 11-in-1 Bundle | CPD QS Certified | 110 CPD Points | Gifts: Hard Copy + PDF Certificates + Student ID Card | Lifetime Access | Cutomer Support | Enrol Now

Duration 2.5 Days 15 CPD hours This course is intended for This course is intended for those with a basic understanding of Tableau who want to pursue mastery of the advanced features. Overview The goal of this course is to present essential Tableau concepts and its advanced functionalities to help better prepare and analyze data. This course will use Tableau Hyper, Tableau Prep and more. Getting Up to Speed ? a Review of the Basics Connecting Tableau to your data Connecting to Tableau Server Connecting to saved data sources Measure Names and Measure Values Three essential Tableau concepts Exporting data to other devices Summary All About Data ? Getting Your Data Ready Data mining and knowledge discovery process models CRISP?DM All About Data ? Joins, Blends, and Data Structures All About Data - Joins, Blends, and Data Structures Introduction to joins Introduction to complex joins Exercise: observing join culling Introduction to join calculations Introduction to spatial joins Introduction to unions Understanding data blending Order of operations No dimensions from a secondary source Introduction to scaffolding Introduction to data structures Exercise: adjusting the data structure for different questions Summary Table Calculations Table Calculations A definition and two questions Introduction to functions Directional and non-directional table calculations Application of functions Summary Level of Detail Calculations Level of Detail Calculations Building playgrounds Playground I: FIXED and EXCLUDE Playground II: INCLUDE Practical application Exercise: practical FIXED Exercise: practical INCLUDE Exercise: practical EXCLUDE Summary Beyond the Basic Chart Types Beyond the Basic Chart Types Improving popular visualizations Custom background images Tableau extensions Summary Mapping Mapping Extending Tableau's mapping capabilities without leaving Tableau Extending Tableau mapping with other technology Exercise: connecting to a WMS server Exploring the TMS file Exploring Mapbox Accessing different maps with a dashboard Creating custom polygons Converting shape files for Tableau Exercise: polygons for Texas Heatmaps Summary Tableau for Presentations Tableau for Presentations Getting the best images out of Tableau From Tableau to PowerPoint Embedding Tableau in PowerPoint Animating Tableau Story points and dashboards for Presentations Summary Visualization Best Practices and Dashboard Design Visualization Best Practices and Dashboard Design Visualization design theory Formatting rules Color rules Visualization type rules Compromises Keeping visualizations simple Dashboard design Dashboard layout Sheet selection Summary Advanced Analytics Advanced Analytics Self-service Analytics Use case ? Self-service Analytics Use case ? Geo-spatial Analytics Summary Improving Performance Improving Performance Understanding the performance-recording dashboard Exercise: exploring performance recording in Tableau desktop Performance-recording dashboard events Behind the scenes of the performance- recording dashboard Hardware and on-the-fly techniques Hardware considerations On-the-fly-techniques Single Data Source > Joining > Blending Three ways Tableau connects to data Using referential integrity when joining Advantages of blending Efficiently working with data sources Tuning data sources Working efficiently with large data sources Intelligent extracts Understanding the Tableau data extract Constructing an extract for optimal performance Exercise: summary aggregates for improved performance Optimizing extracts Exercise: materialized calculations Using filters wisely Extract filter performance Data source filter performance Context filters Dimension and measure filters Table-calculation filters Efficient calculations Boolean/Numbers > Date > String Additional performance considerations Avoid overcrowding a dashboard Fixing dashboard sizing Setting expectations Summary Additional course details: Nexus Humans Advanced Tableau 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 Advanced Tableau 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.

This course covers the Django web framework from the beginning and also covers advanced Django features. Besides Django, the course also covers HTML, CSS, and Bootstrap, which will introduce full-stack development with Django so that you can build complete web apps from scratch. Learn to develop your own web applications with the help of this course.

Learn Python programming by developing robust GUIs and games