In today’s information rich world, we are seeing more and more data-related analysis skills in business analysis jobs. We’ve been asked several times whether business intelligence and business analysis roles are really different roles, and how to build a career path into business analysis without getting wrapped into business intelligence and data analysis. In this video, we’re going to pick apart the difference between data modeling concepts and data analysis, and give you a clear view as to when each skill set is required as you plan out your business analysis career path. For more information on data modeling, check out this list of essential data modeling techniques: http://www.bridging-the-gap.com/data-modeling-techniques/
Views: 18940 Bridging the Gap
When you are trying to figure out what problem to actually be solving before you dive deep into the software requirements, you want to by analyzing the business process and to do that you create both a visual and a textual business process model. A Business Process Model is a commonly used business analysis technique that captures how a business process works and how individuals from different groups work together to achieve a business goal. Let’s look at what a business process model is, how you’d go about creating one, and why it’s important to model your process both visually and textually. And go here to download the Business Process Template (it's free): https://www.bridging-the-gap.com/bptemplate/
Views: 10762 Bridging the Gap
The process of doing statistical analysis follows a clearly defined sequence of steps whether the analysis is being done in a formal setting like a medical lab or informally like you would find in a corporate environment. This lecture gives a brief overview of the process.
Views: 61856 White Crane Education
Business process modeling is used by BAs and non-BAs alike to create lasting change in organizations. It’s how we actually make our ripple effect as business analysts. Today we get specific as I’m sharing 3 examples of some of our business analysts and to-be business analysts, and exactly how they applied business process modeling to change not only their organizations, but also the forward trajectory of their careers. Now, these people were all participants in our Business Process Analysis course, which we’re expanding into an updated Business Process Analysis & Improvement program. To receive our Free Business Process Template + the Free Training: https://www.bridging-the-gap.com/get-business-process-template/ Read more on the blog at: http://www.bridging-the-gap.com/business-process-modeling-case-studies/
Views: 6816 Bridging the Gap
Qualitative research is a strategy for systematic collection, organization, and interpretation of phenomena that are difficult to measure quantitatively. Dr. Leslie Curry leads us through six modules covering essential topics in qualitative research, including what it is qualitative research and how to use the most common methods, in-depth interviews and focus groups. These videos are intended to enhance participants' capacity to conceptualize, design, and conduct qualitative research in the health sciences. Welcome to Module 5. Bradley EH, Curry LA, Devers K. Qualitative data analysis for health services research: Developing taxonomy, themes, and theory. Health Services Research, 2007; 42(4):1758-1772. Learn more about Dr. Leslie Curry http://publichealth.yale.edu/people/leslie_curry.profile Learn more about the Yale Global Health Leadership Institute http://ghli.yale.edu
Views: 172637 YaleUniversity
This KnowledgeKnugget™ (KK) is part of an eCourse "Business Analysis Defined". VIEW COURSE OUTLINE at http://businessanalysisexperts.com/product/video-course-business-analysis-defined/. Also available as Paperback or Kindle eBook at http://www.amazon.com/dp/B00K7MM50O/. DESCRIPTION: Although the field of IT Business Analysis offers great career opportunities for those seeking employment, some business analysis skills are essential for any adult in the business world today. For example, the task of defining the requirements for an IT solution is handed to Business Analysts as well as Subject Matter Experts, Developers, System Analysts, Product Owners, Project Managers, Line Managers, or any other business expert. Applying business analysis techniques to define their business needs results in much higher chances for a successful IT project. In this KnowledgeKnugget™ you will learn what business analysis techniques and tools are most commonly used around the world based on surveys of actual business analysts. This KnowledgeKnugget™ answers questions like: 1. What are the primary activities in business analysis? 2. What tools or techniques do they use? To view more IT requirements training, visit the Business Analysis Learning Store at http://businessanalysisexperts.com/business-analysis-training-store/. PARTIAL TRANSCRIPT: Business analysis is the process of studying a business or any other organization to identify business opportunities / problem areas and suggest potential solutions. A wide range of people with various titles, roles and responsibilities actually apply business analysis techniques within an organization. There are three fundamentally different flavors or levels of business analysis: 1. Strategic Business Analysis (aka Enterprise Analysis) (http://businessanalysisexperts.com/strategic-business-analysis/ ) 2. Tactical Business Analysis (http://businessanalysisexperts.com/tactical-business-analysis/) 3. Operational Business Analysis (http://businessanalysisexperts.com/operational-business-analysis/Operational Business Analysis) Strategic Business Analysis is the study of business visions, goals, objectives, and strategies of an organization or an organizational unit to identify the desired future. It encompasses the analysis of existing organizational structure, policies, politics, problems, opportunities, and application architecture to build a business case for change. This analysis employs business analysis techniques such as Variance Analysis, Feasibility Analysis, Force Field Analysis, Decision Analysis, and Key Performance Indicators to support senior management in the decision-making process. The primary outcome of this work is a set of defined, prioritized projects and initiatives that the organization will undertake to create the desired future. If the initiative includes the development of software using an Agile Software Development Methodology (SDM) (http://businessanalysisexperts.com/product/business-analysis-agile-methodologies/), strategic business analysis techniques identify themes and/or epics, and initiate a product backlog. Tactical Business Analysis is at the project or initiative level to flush out the details of the proposed solution and to ensure that it meets the needs of the business community. Commonly used business analysis techniques at this level include Stakeholder Identification (http://businessanalysisexperts.com/product/how-to-identify-stakeholders-it-projects/), Interviewing (http://businessanalysisexperts.com/product/requirements-elicitation-gathering-business-stakeholder-it-requirements/), Facilitation (http://businessanalysisexperts.com/product/how-to-facilitate-requirements-gathering-workshops/), Baselining, Coverage Matrices, MoSCoW Analysis (http://businessanalysisexperts.com/product/requirements-prioritization-two-simple-techniques/), Benchmarking, Business Rules Analysis, Change Management, Process and Data Modeling (http://businessanalysisexperts.com/product/business-data-modeling-informational-requirements/), and Functional Decomposition (http://businessanalysisexperts.com/product/video-course-exposing-functional-and-non-functional-requirements/). In an Agile environment, Tactical Business Analysis adds to the Product Backlog and/or Release Plans expressed in Themes, Business Epics, Architecture Epics, User Stories (http://businessanalysisexperts.com/product/video-course-writing-user-stories/), and User Story Epics. In a traditional setting, the primary outcome of Tactical Business Analysis is a set of textual and/or modeled Business and Stakeholder Requirements (http://businessanalysisexperts.com/product/video-course-writing-requirements/). ..........
Views: 307309 BA-EXPERTS
PROCESS model 1 demonstrated on SPSS A moderation analysis: meaning, procedure, plot and interpretation
Views: 67905 Goldi Tewari
A tutorial by Dr. Ceni Babaoglu. website: www.cenibabaoglu.com Table of Contents: 00:28 - Outline 00:54 - Initial Analysis 09:37 - Exploratory Analysis 13:15 - Modeling 14:21 - Evaluation 14:54 - Improving the model
Views: 2887 Ceni Babaoglu
This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/2qyTs1d]. This video introduces the Titanic disaster data set and discusses some exploratory analysis on the data. The aim of this video is to recap what you learned so far on a real data set, as well as show-case some data visualization examples. • Download the data set and understand the data structure • Extract some summary statistics from the data set • Visualize the data and find correlations between variables For the latest Application development video tutorials, please visit http://bit.ly/1VACBzh Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 30986 Packt Video
About Barbara A. Carkenord, MBA, CBAP, PMP, PMI-ACP, PMI-PBA, Director of Business Analysis at RMC -Over 25 years of experience in business analysis--one of the original founders of the Business Analysis training industry -Author of the worldwide best-seller Seven Steps to Mastering Business Analysis -Actively involved in the IIBA, she was a core team member of the IIBA BABOK® creation committee -Named 2010 Small Business Woman of the Year by the Georgia Women in Technology Association Business Analysis Core Concept Model and BABOK are trademarks owned by the International Institute of Business Analysis.
Views: 17259 RMC Learning Solutions
In this Statistics Using Python Tutorial, Learn cleaning Data in Python Using Pandas. learn basic data cleaning steps in excel before importing data in python. We use Pandas Functions to clean data perform exploratory data analysis on our Data set. 🔷🔷🔷🔷🔷🔷🔷🔷 Jupyter Notebooks and Practice Files: https://github.com/theengineeringworld/statistics-using-python 🔷🔷🔷🔷🔷🔷🔷🔷 Data Wrangling With Python Using Pandas, Data Science For Beginners, Statistics Using Python 🐍🐼 https://youtu.be/tqv3sL67sC8 Cleaning Data In Python Using Pandas In Data Mining Example, Statistics With Python For Data Science https://youtu.be/xcKXmXilaSw Cleaning Data In Python For Statistical Analysis Using Pandas, Big Data & Data Science For Beginners https://youtu.be/4own4ojgbnQ Exploratory Data Analysis In Python, Interactive Data Visualization [Course] With Python and Pandas https://youtu.be/VdWfB30QTYI 🔷🔷🔷🔷🔷🔷🔷🔷 *** Complete Python Programming Playlists *** * Python Data Science https://www.youtube.com/watch?v=Uct_EbThV1E&list=PLZ7s-Z1aAtmIbaEj_PtUqkqdmI1k7libK * NumPy Data Science Essential Training with Python 3 https://www.youtube.com/playlist?list=PLZ7s-Z1aAtmIRpnGQGMTvV3AGdDK37d2b * Python 3.6.4 Tutorial can be fund here: https://www.youtube.com/watch?v=D0FrzbmWoys&list=PLZ7s-Z1aAtmKVb0fpKyINNeSbFSNkLTjQ * Python Smart Programming in Jupyter Notebook: https://www.youtube.com/watch?v=FkJI8np1gV8&list=PLZ7s-Z1aAtmIVV0dp08_X-yDGrIlTExd2 * Python Coding Interview: https://www.youtube.com/watch?v=wwtzs7vTG50&list=PLZ7s-Z1aAtmJqtN1A3ydeMk0JoD3Lvt9g
Views: 7758 TheEngineeringWorld
In statistical modeling, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). Data Science Certification Training - R Programming: https://www.simplilearn.com/big-data-and-analytics/data-scientist-certification-sas-r-excel-training?utm_campaign=Regression-Data-Science-DtOYBxi4AIE&utm_medium=SC&utm_source=youtube #datascience #datasciencetutorial #datascienceforbeginners #datasciencewithr #datasciencetutorialforbeginners #datasciencecourse What are the course objectives? This course will enable you to: 1. Gain a foundational understanding of business analytics 2. Install R, R-studio, and workspace setup. You will also learn about the various R packages 3. Master the R programming and understand how various statements are executed in R 4. Gain an in-depth understanding of data structure used in R and learn to import/export data in R 5. Define, understand and use the various apply functions and DPLYP functions 6. Understand and use the various graphics in R for data visualization 7. Gain a basic understanding of the various statistical concepts 8. Understand and use hypothesis testing method to drive business decisions 9. Understand and use linear, non-linear regression models, and classification techniques for data analysis 10. Learn and use the various association rules and Apriori algorithm 11. Learn and use clustering methods including K-means, DBSCAN, and hierarchical clustering Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: IT professionals looking for a career switch into data science and analytics Software developers looking for a career switch into data science and analytics Professionals working in data and business analytics Graduates looking to build a career in analytics and data science Anyone with a genuine interest in the data science field Experienced professionals who would like to harness data science in their fields Who should take this course? There is an increasing demand for skilled data scientists across all industries which makes this course suited for participants at all levels of experience. We recommend this Data Science training especially for the following professionals: 1. IT professionals looking for a career switch into data science and analytics 2. Software developers looking for a career switch into data science and analytics 3. Professionals working in data and business analytics 4. Graduates looking to build a career in analytics and data science 5. Anyone with a genuine interest in the data science field 6. Experienced professionals who would like to harness data science in their fields For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 6577 Simplilearn
Modeling, analysis and design skills are essential to BPM success. In this course you will acquire a solid understanding of practical techniques for modeling, analysis and design. The section of this lesson on modeling provides insight on how to depict business processes via maps and models in order to prepare for the analysis and improvement of business process performance. You will learn the significance of creating the right context for process modeling and the definition of clear boundaries. The section on analysis examines various perspectives for analysis, including a focus on time, quality, and cost. You will learn the importance of concisely capturing process issues, methods of prioritization, and the value of impact analysis. The section on design examines the properties of a good process, and outlines essential design principles. You will learn the key components of a solid process design, the pitfalls to avoid and the key elements in making the transition to implementation.
Views: 3179 BPMInstitute
Lecture by Dr. Art Langer, author. Analysis & Design of Information Systems (3nd Ed), Langer, Springer-Verlag 2007 (ISBN978-1-844628-654-4)
Views: 54790 Greg Vimont
In this video you will learn the theory of Time Series Forecasting. You will what is univariate time series analysis, AR, MA, ARMA & ARIMA modelling and how to use these models to do forecast. This will also help you learn ARCH, Garch, ECM Model & Panel data models. For training, consulting or help Contact : [email protected] For Study Packs : http://analyticuniversity.com/ Analytics University on Twitter : https://twitter.com/AnalyticsUniver Analytics University on Facebook : https://www.facebook.com/AnalyticsUniversity Logistic Regression in R: https://goo.gl/S7DkRy Logistic Regression in SAS: https://goo.gl/S7DkRy Logistic Regression Theory: https://goo.gl/PbGv1h Time Series Theory : https://goo.gl/54vaDk Time ARIMA Model in R : https://goo.gl/UcPNWx Survival Model : https://goo.gl/nz5kgu Data Science Career : https://goo.gl/Ca9z6r Machine Learning : https://goo.gl/giqqmx Data Science Case Study : https://goo.gl/KzY5Iu Big Data & Hadoop & Spark: https://goo.gl/ZTmHOA
Views: 405802 Analytics University
Dynamic models are essential for understanding the system dynamics in open-loop (manual mode) or for closed-loop (automatic) control. These models are either derived from data (empirical) or from more fundamental relationships (first principles, physics-based) that rely on knowledge of the process. A combination of the two approaches is often used in practice where the form of the equations are developed from fundamental balance equations and unknown or uncertain parameters are adjusted to fit process data. In engineering, there are 4 common balance equations from conservation principles including mass, momentum, energy, and species. An alternative to physics-based models is to use input-output data to develop empirical dynamic models such as first-order or second-order systems. See http://apmonitor.com/pdc/index.php/Main/DynamicModeling
Views: 4989 APMonitor.com
Lecturer: Dr. Erin M. Buchanan Missouri State University Spring 2015 Mediation analysis video covering model 4 in the process plug in (Hayes, 2013). Lecture materials and assignment available at statstools.com. http://statstools.com/learn/advanced-statistics/
Views: 112188 Statistics of DOOM
This clip explains how to produce some basic descrptive statistics in R(Studio). Details on http://eclr.humanities.manchester.ac.uk/index.php/R_Analysis. You may also be interested in how to use tidyverse functionality for basic data analysis: https://youtu.be/xngavnPBDO4
Views: 141065 Ralf Becker
www.pinnacleadvisory.com --- Pinnacle Advisory Group's Quantitative Analyst Sauro Locatelli explains what he does and how it aids the investment process. This is the fifth presentation from our February 25 Inside the Investment Committee event. www.pinnacleadvisory.com
Views: 115980 Pinnacle Advisory Group
How to do Autoregression with Data Analysis toolpak, how to lag data, how to pick the best regression model
Views: 6640 Leslie Major
Data analysis is a complex process with frequent shifts among data formats, tools and models, as well as between symbolic and visual thinking. How might the design of improved tools accelerate people's exploration and understanding of data? Covering both interactive demos and principles from academic research, my talk will examine how to craft a careful balance of interactive and automated methods, combining concepts from data visualization, machine learning, and computer systems to design novel interactive analysis tools. www.pydata.org PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R. PyData conferences aim to be accessible and community-driven, with novice to advanced level presentations. PyData tutorials and talks bring attendees the latest project features along with cutting-edge use cases.
Views: 8173 PyData
Lecturer: Dr. Erin M. Buchanan Missouri State University Summer 2018 You will learn how to use the new version of the PROCESS version 3 plug in for SPSS by A Hayes with model 1. In this video, you will learn how to run a two-way moderation analysis with a categorical moderator. First, we cover data screening, then power, the moderation analysis, and graphing the interaction of the simple slopes. Interpretation and APA style is also included. Tomorrow the R version of this same analysis will be posted - so check back in for that! Get the materials and other information at OSF: https://osf.io/ns6jz/
Views: 3996 Statistics of DOOM
The course deals with how to simulate and analyze stochastic processes, in particular the dynamics of small particles diffusing in a fluid. Take this course free on edX: https://www.edx.org/course/stochastic-processes-data-analysis-kyotoux-009x#! ABOUT THIS COURSE The motion of falling leaves or small particles diffusing in a fluid is highly stochastic in nature. Therefore, such motions must be modeled as stochastic processes, for which exact predictions are no longer possible. This is in stark contrast to the deterministic motion of planets and stars, which can be perfectly predicted using celestial mechanics. This course is an introduction to stochastic processes through numerical simulations, with a focus on the proper data analysis needed to interpret the results. We will use the Jupyter (iPython) notebook as our programming environment. It is freely available for Windows, Mac, and Linux through the Anaconda Python Distribution. The students will first learn the basic theories of stochastic processes. Then, they will use these theories to develop their own python codes to perform numerical simulations of small particles diffusing in a fluid. Finally, they will analyze the simulation data according to the theories presented at the beginning of course. At the end of the course, we will analyze the dynamical data of more complicated systems, such as financial markets or meteorological data, using the basic theory of stochastic processes. WHAT YOU'LL LEARN Basic Python programming Basic theories of stochastic processes Simulation methods for a Brownian particle
Views: 4670 edX
Part 1 in a in-depth hands-on tutorial introducing the viewer to Data Science with R programming. The video provides end-to-end data science training, including data exploration, data wrangling, data analysis, data visualization, feature engineering, and machine learning. All source code from videos are available from GitHub. NOTE - The data for the competition has changed since this video series was started. You can find the applicable .CSVs in the GitHub repo. Blog: http://daveondata.com GitHub: https://github.com/EasyD/IntroToDataScience I do Data Science training as a Bootcamp: https://goo.gl/OhIHSc
Views: 1022877 David Langer
For NeuroSolutions Infinity software, visit: http://www.neurosolutions.com/infinity/ Download the FREE Trial: http://www.neurosolutions.com/downloads/ What is Predictive Data Analytics? Learn in under 5 minutes. This video is an introduction to Predictive Data Analytics development methodology. By the end of this video, you'll understand the core concepts of predictive data analytics. You'll be able to get started implementing it into your own custom software solutions.
Views: 52732 NeuroDimension
This introduction to Data Science provides a demonstration of analyzing customer data to predict churn using the R programming language. MetaScale walks through the stops necessary to train and test multiple algorithms in order to provide the most accurate model for predicting when a customer will leave the company.
Views: 28654 MetaScale
This video demonstrates using Microsoft Power BI for the analysis of process management data from TimeScape EDM+. This process management data covers the data lifecycle of capture, normalisation, consolidation, cleansing and validation provided by the TimeScape EDM+ enterprise data management system. Summary: • Process timings of the data flow (multiple dates) • Process actions summary (single date) • Process action drill-down (lineage) • Validation summary (broken down by instrument, curve and surface) SUBSCRIBE TO OUR CHANNEL https://www.youtube.com/user/XenomorphSoftware?sub_confirmation=1 LEARN MORE ABOUT TIMESCAPE EDM+ ENTERPRISE DATA MANAGEMENT http://www.xenomorph.com/timescape-edm/ ABOUT XENOMORPH Xenomorph believes that great decisions are made when everyone can easily access and analyze the same high quality data. Based on this belief, we develop software solutions for financial markets that enable both technologists and business users alike to manage, cleanse and analyze more data, more quickly. Our award-winning TimeScape EDM+ data and analytics management platform is used by trading, research, risk, product control, IT and back-office staff at investment banks, brokerages, insurers, hedge funds and asset management companies worldwide. CONNECT WITH XENOMORPH Xenomorph - http://www.xenomorph.com Twitter - https://twitter.com/XenomorphNews LinkedIn - https://www.linkedin.com/company/xenomorph Google+ - https://plus.google.com/+XenomorphSoftware Blog - http://www.xenomorph.com/resources/blog/ RSS - http://www.xenomorph.com/feed/
Views: 371 Xenomorph Software
*This animation shows the as-happened design process, discovered based on the real-world data (BIM-based) through the design process of a 3-storey hotel building. *For instance, Stru SD represents the activities which are performed on BIM elements in that stage (Structural modelling in Schematic Design phase) such as addition, removal, rotation, relocation and attribute changes of elements. Also, the numbers on arrows entering the rectangles animation show the number of activities which are performed in that space. the numbers on the box show the number of elements in that stage at any point in the design life cycle. *The real time of the project is shown at bottom right corner. So, it shows the project timeline from October 1st to October 10th. *Animation Legend: Each dot represent one BIM element (e.g., a wall) Stru: Structural Engineering Modeling in Revit Arch: Architectural Modeling in Revit MEP: Mechanical Modeling SD: Schematic Design Phase DD: Design Development Phase CD: Construction Documentation Phase *Acknowledgment: The music (Lost Like Dust) is created by Mr. Aaron Shahi.
Views: 20 Sobhan Kouhestani
KPMG Process Mining visualizes actual business processes with Microsoft Power BI. The processes are created from transactional data and do not need any user modeling. This ensures process analysis without bias and provides full end-to-end process transparency. It aims to help businesses find and present better alternatives to real-life processes, to make them more efficient and controls more effective. The demonstration shows a procurement process from the creation of a purchase request to a payment release that has been analyzed in three specific use cases. Learn more: https://powerbi.microsoft.com/en-us/
Views: 9758 KPMG
Lecture Starts at: 8:25 Business data presents a challenge for the data analyst. Business data is often aggregated, recorded over time, and tends to exhibit autocorrelation. Additionally, and most problematically, the amount of business data is usually quite limited. These characteristics lead to a situation where many of the tools in the analyst's tool belt (e.g., regression) aren't ideal for the task. Despite these challenges, proper analysis of business data represents a fundamental skill required of Business/Data Analysts, Product/Program Managers, and Data Scientists. At this meetup presenter Dave Langer will show how to get started analyzing business data in a robust way using Excel – no programming or statistics required! Dave will cover the following during the presentation: • The types of business data and why business data is a unique analytical challenge. • Requirements for robust business data analysis. • Using histograms, running records, and process behavior charts to analyze business data. • The rules of trend analysis. • How to properly compare business data across time, organizations, geographies, etc.Where you can learn more about the tools and techniques. *Excel spreadsheets can be found here: https://code.datasciencedojo.com/datasciencedojo/tutorials/tree/master/Business%20Data%20Analysis%20with%20Excel **Find out more about David here: https://www.meetup.com/data-science-dojo/events/236198327/ -- Learn more about Data Science Dojo here: https://hubs.ly/H0hz7sf0 Watch the latest video tutorials here: https://hubs.ly/H0hz8rL0 See what our past attendees are saying here: https://hubs.ly/H0hz7ts0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://plus.google.com/+Datasciencedojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 51606 Data Science Dojo
Tutorial introducing the idea of linear regression analysis and the least square method. Typically used in a statistics class. Playlist on Linear Regression http://www.youtube.com/course?list=ECF596A4043DBEAE9C Like us on: http://www.facebook.com/PartyMoreStudyLess Created by David Longstreet, Professor of the Universe, MyBookSucks http://www.linkedin.com/in/davidlongstreet
Views: 797516 statisticsfun
This webinar highlights how MATLAB can work with Excel. Get a Free MATLAB Trial: https://goo.gl/C2Y9A5 Ready to Buy: https://goo.gl/vsIeA5 Learn MATLAB for Free: https://goo.gl/xIiHyG Many technical professionals find that they run into limitations using Excel for their data analysis applications. This webinar highlights how MATLAB can supplement the capabilities of Excel by providing access to thousands of pre-built engineering and advanced analysis functions and versatile visualization tools. Learn more about using MATLAB with Excel: http://goo.gl/3vkFMW Learn more about MATLAB: http://goo.gl/YKadxi Through product demonstrations you will see how to: • Access data from spreadsheets • Plot data and customize figures • Perform statistical analysis and fitting • Automatically generate reports to document your analysis • Freely distribute your MATLAB functions as Excel add-ins This webinar will show new features from the latest versions of MATLAB including new data types to store and manage data commonly found in spreadsheets. Previous knowledge of MATLAB is not required. About the Presenter: Adam Filion holds a BS and MS in Aerospace Engineering from Virginia Tech. His research involved nonlinear controls of spacecraft and periodic orbits in the three-body problem. After graduating he joined the MathWorks Engineering Development Group in 2010 and moved to Applications Engineering in 2012.
Views: 242614 MATLAB
Thanks to all of you who support me on Patreon. You da real mvps! $1 per month helps!! :) https://www.patreon.com/patrickjmt !! Part 2: http://www.youtube.com/watch?v=jtHBfLtMq4U In this video, I discuss Markov Chains, although I never quite give a definition as the video cuts off! However, I finish off the discussion in another video! This video gives a 'real life' problem as some motivation and intuition, as well as introduces a bit of terminology.
Views: 580592 patrickJMT
In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating. The challenge for this video is here: https://github.com/llSourcell/prepare_dataset_challenge Carl's winning code: https://github.com/av80r/coaster_racer_coding_challenge Rohan's runner-up code: https://github.com/rhnvrm/universe-coaster-racer-challenge Come join other Wizards in our Slack channel: http://wizards.herokuapp.com/ Dataset sources I talked about: https://github.com/caesar0301/awesome-public-datasets https://www.kaggle.com/datasets http://reddit.com/r/datasets More learning resources: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://www.youtube.com/watch?v=kSslGdST2Ms http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/ http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf Please subscribe! And like. And comment. That's what keeps me going. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 192666 Siraj Raval
- By: Jorge P. Rodríguez - Affiliation: IFISC - Date: 2018-09-21T08:30:00+00:00 Ph.D. defense
Subject: Statistics Paper: Advanced Data Analysis
Views: 180 Vidya-mitra
There are audio issues with this video that cannot be fixed. We recommend listening to the tutorial without headphones to minimize the buzzing sound. Tutorial information may be found at https://scipy2017.scipy.org/ehome/220975/493423/ Data Science and Machine learning have been synonymous with languages like Python. Libraries like numpy and Pandas have become the de facto standard when working with data. The DataFrame object provided by Pandas gives us the ability to work with heterogeneous unstructured data that is commonly used in "real world" data. New learners are often drawn to Python and Pandas because of all the different and exciting types of models and insights the language can do and provide, but are awestruck when faced with the initial learning curve. This tutorial aims to guide the learner from using spreadsheets to using the Pandas DataFrame. Not only does moving to a programming language allow the user to have a more reproducible workflow, but as datasets get larger, some cannot even be opened in a spreadsheet program. The goal is to have an absolute beginner proficient enough with Pandas that they can start working with data in Python. We will cover how to load and view our data. Then, some basic methods to do quick visualizations of our data for exploratory data analysis. We will then work on combining and working multiple datasets (concatenating and merging), and introduce what Dr. Hadley Wickham has coined "tidy data". Tidy data is an important concept because the process of tidying data will fix a host of data problems that are needed to perform analysis. We then cover functions and applying methods to our data with a focus on data cleaning, and how we can use the concept of split-apply-combine (groupby) to summarize or reduce our data. Finally, we cover the basics of string manipulation and how to use it to clean data before briefly covering the role of Pandas in analysis packages such as scikit learn. The tutorial will with a fitted model. The goal is to get people familiar with Python and Pandas so they can learn and explore many other parts of the Python ecosystem.
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Dr. Gardner overviews the data gathering process for conducting a performance analysis. Learn more about Franklin’s MS degree in instructional design: https://www.franklin.edu/degrees/masters/instructional-design-learning-technology Learn more about Franklin University’s doctoral degree in instructional design leadership: https://www.franklin.edu/degrees/doctoral/instructional-design-leadership Reference: Van Tiem, D., Moseley, J.L., & Dessinger, J.C. (2012). Fundamentals of performance improvement: Optimizing results through people, processes, and organizations (3rd ed.). San Francisco, CA: Wiley/International Society for Performance Improvement.
This Data Science tutorial video will give you an idea on the life of a Data Scientist, steps involved in Data science project, roles & salary offered to a Data Scientist. Data is everywhere. In fact, the amount of digital data that exists is growing at a rapid rate, doubling every two years, and changing the way we live. Data Science is basically dealing with unstructured and structured data. Data Science is a field that comprises of everything that is related to data cleansing, preparation, and analysis. In simple terms, it is the umbrella of techniques used when trying to extract insights and information from data. Now, let us get started and understand what is Data Science all about. Below topics are explained in this Data Science tutorial: 1. Life of a Data Scientist 2. Steps in Data Science project - Understanding the business problem - Data acquisition - Data preparation - Exploratory data analysis - Data modeling - Visualization and communication - Deploy & maintenance 3. Roles offered to a Data Scientist 4. Salary of a Data Scientist To learn more about Data Science, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Read the full article here: https://www.simplilearn.com/career-in-data-science-ultimate-guide-article?utm_campaign=What-is-Data-Science-bTTxei-S1WI&utm_medium=Tutorials&utm_source=youtube Watch more videos on Data Science: https://www.youtube.com/watch?v=0gf5iLTbiQM&list=PLEiEAq2VkUUIEQ7ENKU5Gv0HpRDtOphC6 #DataScienceWithPython #DataScienceWithR #DataScienceCourse #DataScience #DataScientist #BusinessAnalytics #MachineLearning This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course. Why learn Data Science? Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data. You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to: 1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics. Install the required Python environment and other auxiliary tools and libraries 2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions 3. Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave 4. Perform data analysis and manipulation using data structures and tools provided in the Pandas package 5. Gain expertise in machine learning using the Scikit-Learn package The Data Science with python is recommended for: 1. Analytics professionals who want to work with Python 2. Software professionals looking to get into the field of analytics 3. IT professionals interested in pursuing a career in analytics 4. Graduates looking to build a career in analytics and data science 5. Experienced professionals who would like to harness data science in their fields Learn more at: https://www.simplilearn.com/big-data-and-analytics/python-for-data-science-training?utm_campaign=What-is-Data-Science-X3paOmcrTjQ&utm_medium=Tutorials&utm_source=youtube For more information about Simplilearn’s courses, visit: - Facebook: https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simp... - Website: https://www.simplilearn.com Get the Android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
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Get to know more about The Data Analysis process and Applications for the same. The Post Graduate Program in Data Analytics is a 500+ hour program covering foundational concepts and hands-on learning of leading analytical tools, such as SAS, R, Python, Hive, Spark and Tableau as well as functional analytics across many domains. Over the course of 3 semesters, candidates will not only gain theoretical knowledge of data science tools, but also gain exposure to business perspectives and industry best practices through guest lectures and project submissions. Click here to know more about the Program http://imarticus.org/post-graduate-program-in-data-analytics Imarticus Learning is a professional education institute focused on bridging the gap between industry & academia by offering certified industry-endorsed courses in Financial Services, Business Analysis, Business Analytics & Wealth Management. Visit: http://www.imarticus.org
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