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The Data Analysis Process
 
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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: 36568 White Crane Education
Data Analysis & Discussion
 
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This video is meant to be used as an introductory lesson to Mini Research Writing focusing on Data Analysis and Discussion. As this is a mini class project, some of the requirements have been made simple due to time constraints. Plus, the focus of this mini research paper is to get students familiarized to the ways of writing an academic paper and the items that needs to be included. suitable for beginners!
Views: 13985 NurLiyana Isa
Interview with a Data Analyst
 
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This video is part of the Udacity course "Intro to Programming". Watch the full course at https://www.udacity.com/course/ud000
Views: 268017 Udacity
Excel Data Analysis: Sort, Filter, PivotTable, Formulas (25 Examples): HCC Professional Day 2012
 
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Download workbook: http://people.highline.edu/mgirvin/ExcelIsFun.htm Learn the basics of Data Analysis at Highline Community College Professional Development Day 2012: Topics in Video: 1. What is Data Analysis? ( 00:53 min mark) 2. How Data Must Be Setup ( 02:53 min mark) Sort: 3. Sort with 1 criteria ( 04:35 min mark) 4. Sort with 2 criteria or more ( 06:27 min mark) 5. Sort by color ( 10:01 min mark) Filter: 6. Filter with 1 criteria ( 11:26 min mark) 7. Filter with 2 criteria or more ( 15:14 min mark) 8. Filter by color ( 16:28 min mark) 9. Filter Text, Numbers, Dates ( 16:50 min mark) 10. Filter by Partial Text ( 20:16 min mark) Pivot Tables: 11. What is a PivotTable? ( 21:05 min mark) 12. Easy 3 step method, Cross Tabulation ( 23:07 min mark) 13. Change the calculation ( 26:52 min mark) 14. More than one calculation ( 28:45 min mark) 15. Value Field Settings (32:36 min mark) 16. Grouping Numbers ( 33:24 min mark) 17. Filter in a Pivot Table ( 35:45 min mark) 18. Slicers ( 37:09 min mark) Charts: 19. Column Charts from Pivot Tables ( 38:37 min mark) Formulas: 20. SUMIFS ( 42:17 min mark) 21. Data Analysis Formula or PivotTables? ( 45:11 min mark) 22. COUNTIF ( 46:12 min mark) 23. Formula to Compare Two Lists: ISNA and MATCH functions ( 47:00 min mark) Getting Data Into Excel 24. Import from CSV file ( 51:21 min mark) 25. Import from Access ( 54:00 min mark) Highline Community College Professional Development Day 2012 Buy excelisfun products: https://teespring.com/stores/excelisfun-store
Views: 1449485 ExcelIsFun
What does a data analyst actually do?
 
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Dave Elkington, CEO of InsideSales.com, explains the role of a data analyst, and why cleaning up data sets takes up the vast majority of their time. Algorithms and computer science play a minor role. For more, see https://www.siliconrepublic.com Follow us on Twitter: https://twitter.com/siliconrepublic Like us on Facebook: https://www.facebook.com/siliconrepublic
Views: 101639 siliconrepublic
How to analyze your data and write an analysis chapter.
 
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In this video Dr. Ziene Mottiar, DIT, discusses issues around analyzing data and writing the analysing chapter. The difference between Findings and Analysis chapters is also discussed. This video is useful for anyone who is writing a dissertation or thesis.
Views: 62961 ZieneMottiar
Qualitative analysis of interview data: A step-by-step guide
 
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The content applies to qualitative data analysis in general. Do not forget to share this Youtube link with your friends. The steps are also described in writing below (Click Show more): STEP 1, reading the transcripts 1.1. Browse through all transcripts, as a whole. 1.2. Make notes about your impressions. 1.3. Read the transcripts again, one by one. 1.4. Read very carefully, line by line. STEP 2, labeling relevant pieces 2.1. Label relevant words, phrases, sentences, or sections. 2.2. Labels can be about actions, activities, concepts, differences, opinions, processes, or whatever you think is relevant. 2.3. You might decide that something is relevant to code because: *it is repeated in several places; *it surprises you; *the interviewee explicitly states that it is important; *you have read about something similar in reports, e.g. scientific articles; *it reminds you of a theory or a concept; *or for some other reason that you think is relevant. You can use preconceived theories and concepts, be open-minded, aim for a description of things that are superficial, or aim for a conceptualization of underlying patterns. It is all up to you. It is your study and your choice of methodology. You are the interpreter and these phenomena are highlighted because you consider them important. Just make sure that you tell your reader about your methodology, under the heading Method. Be unbiased, stay close to the data, i.e. the transcripts, and do not hesitate to code plenty of phenomena. You can have lots of codes, even hundreds. STEP 3, decide which codes are the most important, and create categories by bringing several codes together 3.1. Go through all the codes created in the previous step. Read them, with a pen in your hand. 3.2. You can create new codes by combining two or more codes. 3.3. You do not have to use all the codes that you created in the previous step. 3.4. In fact, many of these initial codes can now be dropped. 3.5. Keep the codes that you think are important and group them together in the way you want. 3.6. Create categories. (You can call them themes if you want.) 3.7. The categories do not have to be of the same type. They can be about objects, processes, differences, or whatever. 3.8. Be unbiased, creative and open-minded. 3.9. Your work now, compared to the previous steps, is on a more general, abstract level. 3.10. You are conceptualizing your data. STEP 4, label categories and decide which are the most relevant and how they are connected to each other 4.1. Label the categories. Here are some examples: Adaptation (Category) Updating rulebook (sub-category) Changing schedule (sub-category) New routines (sub-category) Seeking information (Category) Talking to colleagues (sub-category) Reading journals (sub-category) Attending meetings (sub-category) Problem solving (Category) Locate and fix problems fast (sub-category) Quick alarm systems (sub-category) 4.2. Describe the connections between them. 4.3. The categories and the connections are the main result of your study. It is new knowledge about the world, from the perspective of the participants in your study. STEP 5, some options 5.1. Decide if there is a hierarchy among the categories. 5.2. Decide if one category is more important than the other. 5.3. Draw a figure to summarize your results. STEP 6, write up your results 6.1. Under the heading Results, describe the categories and how they are connected. Use a neutral voice, and do not interpret your results. 6.2. Under the heading Discussion, write out your interpretations and discuss your results. Interpret the results in light of, for example: *results from similar, previous studies published in relevant scientific journals; *theories or concepts from your field; *other relevant aspects. STEP 7 Ending remark This tutorial showed how to focus on segments in the transcripts and how to put codes together and create categories. However, it is important to remember that it is also OK not to divide the data into segments. Narrative analysis of interview transcripts, for example, does not rely on the fragmentation of the interview data. (Narrative analysis is not discussed in this tutorial.) Further, I have assumed that your task is to make sense of a lot of unstructured data, i.e. that you have qualitative data in the form of interview transcripts. However, remember that most of the things I have said in this tutorial are basic, and also apply to qualitative analysis in general. You can use the steps described in this tutorial to analyze: *notes from participatory observations; *documents; *web pages; *or other types of qualitative data. STEP 8 Suggested reading Alan Bryman's book: 'Social Research Methods' published by Oxford University Press. Steinar Kvale's and Svend Brinkmann's book 'InterViews: Learning the Craft of Qualitative Research Interviewing' published by SAGE. Good luck with your study. Text and video (including audio) © Kent Löfgren, Sweden
Views: 640580 Kent Löfgren
Analytical Reports: Writing Analytical Reports
 
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This video introduces students to Analytical Reports, which are a common form of communication in the technical workplace. These reports present research addressing a specific problem or research question. The typical arrangement of an Analytical Report contains the following sections: Introduction, Methods, Results and Discussion (the IMRaD pattern). In this video, these sections are discussed by highlighting examples from a student report.
Views: 23206 umnWritingStudies
The Complete Introduction to Business Data Analysis
 
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The Complete Introduction to Business Data Analysis teaches you how to apply different methods of data analysis to turn your data into new insight and intelligence. The ability to ask questions of your data is a powerful competitive advantage, resulting in new income streams, better decision making and improved productivity. A recent McKinsey Consulting report has identified that data analysis is one of the most important skills required in the American economy at the current time. This course focuses on the following different methods of analysis. During the course you will understand why the form of analysis is important and also provide examples of using the analysis using Excel 2013. The methods of analysis covered include Comparison Analysis, Trend Analysis, Ranking Analysis, Interactive Dashboards, Contribution Analysis, Variance Analysis, Pareto Analysis, Frequency Analysis and Correlations The Complete Introduction to Business Data Analysis is designed for all business professionals who want to take their ability to turn data into information to the next level. If you are an Excel user then you will want to learn the easy to use techniques that are taught in this course. This course is presented using Excel 2013. Excel 2010 can be used for the majority of the training exercises. Small parts of the course do use Excel Power Pivot and Power View. Please note that this course does not include any complicated formulas, VBA or macros. The course utilizes drag and drop techniques to create the majority of the different data analysis techniques.
Views: 2841 Data Insight Training
A Day in the Life of a Data Analyst
 
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Take a look behind the scenes at the Intermountain Healthcare employees that keep us running smoothly! Our Data Analyst's work hard each day using data, research, numbers, and demographics to help people live the healthiest lives possible.
Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help
 
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The kind of graph and analysis we can do with specific data is related to the type of data it is. In this video we explain the different levels of data, with examples. Subtitles in English and Spanish.
Views: 746396 Dr Nic's Maths and Stats
Fundamentals of Qualitative Research Methods: Data Analysis (Module 5)
 
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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: 136321 YaleUniversity
Likert Scales and Data Analysis
 
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Advice on gathering and analyzing data in organizations, tips on using Likert scales, and a case study on leveraging data to help the bottom line. McMillan Interview http://videos.asq.org/influencing-public-policy-with-data-analysis Full Case Study by S. Pandravada and T. Gurun https://secure.asq.org/perl/msg.pl?prvurl=http://asq.org/2017/02/statistical-process-control/fresh-foods-ordering-process.pdf
Views: 6157 ASQ
Statistics & Data Analysis: Does It Have A Future?
 
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FREE DOWNLOAD - 7 Habits of Highly Successful Software Developers ➨ https://simpleprogrammer.com/yt/7-habits FREE DOWNLOAD - 7 Habits of Highly Successful Software Developers ➨ https://simpleprogrammer.com/yt/7-habits SUBSCRIBE TO THIS CHANNEL: vid.io/xokz Inevitable Book: https://simpleprogrammer.com/theinevitable Statistics & Data Analysis: Does It Have A Future? The process of evaluating data using analytical and logical reasoning to examine each component of the data provided is called data analysis or statistics. This form of analysis is just one of the many steps that must be completed when conducting a research experiment. Data from various sources is gathered, reviewed, and then analyzed to form some sort of finding or conclusion. There are a variety of specific data analysis method, some of which include data mining, text analytics, business intelligence, and data visualizations. (Source: http://www.businessdictionary.com/definition/data-analysis.html) As you know, we are gathering more and more data each new year. As our society develops, more data is stored and more it needs interpretation. Doest it has a future? Or is it a lost case? Watch this video and find out! If you have a question, email me at [email protected] If you liked this video, share, like and, of course, subscribe! Subscribe To My YouTube Channel: http://bit.ly/1zPTNLT Visit Simple Programmer Website: http://simpleprogrammer.com/ Connect with me on social media: Facebook: https://www.facebook.com/SimpleProgrammer Twitter: https://twitter.com/jsonmez Other Links: Sign up for the Simple Programmer Newsletter: http://simpleprogrammer.com/email Simple Programmer blog: http://simpleprogrammer.com/blog Learn how to learn anything quickly: http://10stepstolearn.com Boost your career now: http://devcareerboost.com
Views: 13395 Bulldog Mindset
Data Analysis in SPSS Made Easy
 
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Use simple data analysis techniques in SPSS to analyze survey questions.
Views: 771597 Claus Ebster
Practice 4 - Analyzing and Interpreting Data
 
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Science and Engineering Practice 3: Analyzing and Interpreting Data Paul Andersen explains how scientists analyze and interpret data. Data can be organized in a table and displayed using a graph. Students should learn how to present and evaluate data. Intro Music Atribution Title: I4dsong_loop_main.wav Artist: CosmicD Link to sound: http://www.freesound.org/people/CosmicD/sounds/72556/ Creative Commons Atribution License
Views: 55905 Bozeman Science
Data Collection & Analysis
 
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Impact evaluations need to go beyond assessing the size of the effects (i.e., the average impact) to identify for whom and in what ways a programme or policy has been successful. This video provides an overview of the issues involved in choosing and using data collection and analysis methods for impact evaluations
Views: 51027 UNICEF Innocenti
What Is An Analysis In A Report?
 
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Commerce server provides dozens of reports so that you can analyze product sales, web site usage, diagnostics, and more. The report is the section where you demonstrate your ability to understand material and present & explain data case at hand others in a meaningful concise fashion showing critical analysis. An industry analysis report is a document that evaluates given and the companies involved in it. However, the technical information is difficult to understand because it complicated and not readily known structure of a data analysis report. In its most basic form, a sales analysis report shows whether are increasing or declining. Financial analysis report readyratios. In other words analysis report definition, meaning, english dictionary, synonym, see also 'analysis situs',analysis situs',blot analysis',combinatorial analysis', reverso vocabulary 20 dec 2011 while both draw upon the same collected online data, reporting and are very different in terms of their purpose, tasks, outputs, delivery, value. How to write data analysis reports. This section and the next, on reporting discussing your findings, deal with body of thesis. Develop some dummy tables or lists to hold your analyzed data share those with othersidentify the most important findings from data, summarize them and then use specific results (e. Often included as part of a business plan, an industry analysis report seeks to establish what is financial report? Comprehensive reports accentuate the strengths and weaknesses company. Lesson 1 know your content structure of a data analysis report cmu statisticsanalysis what's the difference? A student's sample analytical to determine best how write business analyzing, interpreting and reporting basic research resultswhat is sales report? Hdr thesis monash universityenglish definition dictionary difference between web analytics an industry (with pictures) wikihow. Report contents this report is divided into four analytical reports try to understand and fix problems. A financial analysis report is. Pdf url? Q webcache. Analysis and report writing tips. Communicating the company's strengths and weaknesses in an accurate honest manner is helpful convincing investors to invest your business. In a doctoral thesis, this will consist of number chapters. While both of these areas web analytics draw upon the same collected data, reporting and analysis are very different in terms their purpose, tasks, outputs, a student's sample analytical report. You use sql server reporting se a sales analysis report shows the trends that occur in company's volume over time. A table or list of data) to clarify your findingspresent analysis in an orderly, meaningful, simple way the report. This means writing a report that uses 26 sep 2017 business analysis reports are often the most important company documents on record, and there can be many reasons for them. Analysis to determine the best stove for long distance ultralight backpackingdevin woodeastern maine comm
Power BI Dashboard & Reports - Sales Analysis
 
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Power BI Reports - Our Sales Analysis Solution Demonstration contains various generic reporting examples which have been popular client choices. View the metrics as Vs Prior or Vs Target, select your time periods and use the various drill downs to answer specific business questions. Know which products, stores or customers or salespersons are doing most of your business, and which are not very profitable. Spot trends in time, locations or products and be empowered to make data driven decisions. (http://databear.com) Through our custom apps, connecting your data to your solution has never been easier. To interact with many more of our solutions, visit http://databear.com/solutions/
What is SAS?
 
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http://zerotoprotraining.com This video explains What is Statistical Analysis System known as SAS. Category: Business Intelligence Tags: SAS Overview, Statistical Analysis System Overview
Views: 131421 HandsonERP
CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters
 
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CAREERS IN DATA ANALYTICS - Salary , Job Positions , Top Recruiters What IS DATA ANALYTICS? Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses. As a term, data analytics predominantly refers to an assortment of applications, from basic business intelligence (BI), reporting and online analytical processing (OLAP) to various forms of advanced analytics. In that sense, it's similar in nature to business analytics, another umbrella term for approaches to analyzing data -- with the difference that the latter is oriented to business uses, while data analytics has a broader focus. The expansive view of the term isn't universal, though: In some cases, people use data analytics specifically to mean advanced analytics, treating BI as a separate category. Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends and gain a competitive edge over rivals -- all with the ultimate goal of boosting business performance. Depending on the particular application, the data that's analyzed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources. Types of data analytics applications : At a high level, data analytics methodologies include exploratory data analysis (EDA), which aims to find patterns and relationships in data, and confirmatory data analysis (CDA), which applies statistical techniques to determine whether hypotheses about a data set are true or false. EDA is often compared to detective work, while CDA is akin to the work of a judge or jury during a court trial -- a distinction first drawn by statistician John W. Tukey in his 1977 book Exploratory Data Analysis. Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves analysis of numerical data with quantifiable variables that can be compared or measured statistically. The qualitative approach is more interpretive -- it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view. At the application level, BI and reporting provides business executives and other corporate workers with actionable information about key performance indicators, business operations, customers and more. In the past, data queries and reports typically were created for end users by BI developers working in IT or for a centralized BI team; now, organizations increasingly use self-service BI tools that let execs, business analysts and operational workers run their own ad hoc queries and build reports themselves. Keywords: being a data analyst, big data analyst, business analyst data warehouse, data analyst, data analyst accenture, data analyst accenture philippines, data analyst and data scientist, data analyst aptitude questions, data analyst at cognizant, data analyst at google, data analyst at&t, data analyst australia, data analyst basics, data analyst behavioral interview questions, data analyst business, data analyst career, data analyst career path, data analyst career progression, data analyst case study interview, data analyst certification, data analyst course, data analyst in hindi, data analyst in india, data analyst interview, data analyst interview questions, data analyst job, data analyst resume, data analyst roles and responsibilities, data analyst salary, data analyst skills, data analyst training, data analyst tutorial, data analyst vs business analyst, data mapping business analyst, global data analyst bloomberg, market data analyst bloomberg
Views: 19542 THE MIND HEALING
Writing-up Qualitative Research
 
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Looks at a range of issues that need thinking about when writing up qualitative research. These include: getting started, free-writing, organization – chronological, thematic etc. – focus, drop files, getting feedback, details, tightening up, style, conclusions and editing. This was a lecture given to postgraduate (graduate) students at the University of Huddersfield as part of a course on Qualitative Data Analysis. To learn more about social research methods you might be interested in this new, inexpensive, postgraduate, distance learning course: MSc Social Research and Evaluation. The course is delivered entirely via the Internet. http://sre.hud.ac.uk/ Becker, H. S. (1986). Writing for Social Scientists: How to Start and Finish your Thesis, Book or Article. Chicago and London: University of Chicago Press. Elbow, P. (1981) Writing with Power: Techniques for Mastering the Writing Process. New York: Oxford University Press Wolcott, H. F. (2009) Writing up qualitative research (3rd ed.). Newbury Park, Calif. ; London: Sage.
Views: 37835 Graham R Gibbs
How to Create a Summary Report from an Excel Table
 
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One of my viewers asked for my help in creating an Executive Summary Report - because her manager will not allow her to use a Pivot Table. Here are the tips and techniques that I demonstrate in this lesson: 1) Use Excel's Advanced Filter to Extract a list of unique customer names from a filed with over 4,000 records. 2) Convert a normal range of data cells into an Excel 2007 / 2010 Table (as a List in Excel 2003) - so that range references will update automatically when you append records. 3) Create Named Ranges of Cells that you can use in Formulas & Functions. 4) Use the SUMIF, AVERAGEIF and COUNTIF Functions in the Summary Report. I invite you to visit my online shopping website - http://shop.thecompanyrocks.com - to view all of my video tutorials. Danny Rocks The Company Rocks
Views: 1041136 Danny Rocks
What Is The Definition Of Analytical Report?
 
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Difference between analytical & operational reportingupdated september 26, 2017. As a specific genre of reports, analytical reports are hybrid between an informational reporting is the type business that used to make decisions. The correct type and level of information is critical for start studying informational vs analytical reports. Html "imx0m" url? Q webcache. Googleusercontent searchthere is another type of business reporting that used to make decisions. Analytical reports definition and uses video informational analytical study. Learn vocabulary analytical report presenting challenges. Get students to define the audience, purpose, and 22 jun 2014 this presentation outlines various types of reports best practices industry standards for writing persuasive analytical. Difference between analytical & operational reporting informational vs reports flashcards analysis what's the difference? The difference and analytics report writing tipsdefinition of study by medical dictionary. • Specify how looking for online definition of analytical study in the medical dictionary? Developed and launched german language version of market reports store analytical services provides accurate, unbiased analysis on research this relatively young field, this study establishes a baseline, both in the definition and the the indicators presented in our analytical reports are based on publication and elsevier employs various ways to define research areas, ranging from using definition of data analysis the process of evaluating data using analytical and logical reasoning to examine each component of the data providedAnalytical reports definition and uses video informational and analytical reports definition and uses video what is an analytical report? What is an analytical report? Quora. An example of such a report might list the top products sold analysis is process compiling and reviewing information with objective forming conclusion, explaining why something happened or 22 jun 2016 what difference between operational analytical reporting? Data analytics an increasingly important asset for growing (analytical reports don't always focus on problems they can also opportunities improvement. Other analytical reports are feasibility studies that examine proposed solutions and determine their practicality. Offering recommendations is the biggest difference between informational and analytical reporting meaning of report refers to that contains information along with some definitions renowned authors are given below reports function as proposals identify or define problems argue for specific ways resolving them. What is the definition of an 'analytical report'? The purpose analytical business reports difference between operational vs reportinganalytical. Meaning of analytical report qs study. Quality of define the problem. What needs to 19 oct 2010 you may have seen various people use the terms 'reporting' and roles of reporting analysis, let's start with some high
Views: 14 SMART Hairstyles
R Markdown for a Data Analysis Report
 
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Guide for my students on producing data analysis reports using R Markdown in the R Studio IDE.
Views: 13050 Homer White
Report Like a Boss Using Google Data Studio
 
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Join Data Studio Product Manager Nikhil Roy and Google Analytics Advocate Louis Gray live from Google HQ on August 17th to learn about how to design and deliver exceptional reports in Google Data Studio.
Views: 127054 Google Analytics
Writing Tip #3: Writing Qualitative Findings Paragraphs
 
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This video presents a "formula" for writing qualitative findings paragraphs in research reports. It presents the Setup-Quote-Comment model (SQC).
How to Report Statistics in APA Style
 
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Tutorial for reporting statistics in an APA style manuscript, including using special scripts/symbols and the Equation Editor function in Word 2010 for Windows. Learn basic format for reporting results of chi-squared test of independence, correlation, t test, and ANOVA. Subtitles available: click on the CC button toward the bottom right of the video. Menu available for jumping to chapters in the flash video posted on the KSU Psych Lab website (link below). Terrence Jorgensen Kennesaw State University Psychology Lab http://psychology.hss.kennesaw.edu/resources/psychlab/
Views: 87516 Terry Jorgensen
Why Use R? - R Tidyverse Reporting and Analytics for Excel Users
 
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https://www.datastrategywithjonathan.com Free YouTube Playlist https://www.youtube.com/playlist?list=PL8ncIDIP_e6vQ0uQofezvKv3yPnL5Unxe From Excel To Big Data and Interactive Dashboard Visualizations in 5 Hours If you use Excel for any type of reporting or analytics then this course is for you. There are a lot of great courses teaching R for statistical analysis and data science that can sometimes make R seem a bit too advanced for every day use. Also since there are many different ways of using R that can often add to the confusion. The reality is that R can be used to make your every day reporting analytics that you do in Excel much faster and easier without requiring any complex statistical techniques while at the same time giving you a solid foundation to expand into those areas if you so wish. This course uses the Tidyverse standards for using R which provides a single, comprehensive and easy to understand method for using R without complicating things via multiple methods. It's designed to build upon the the skills you are already familiar with in Excel to shortcut your learning journey. If you're looking to learn Advanced Excel, Excel VBA or Databases then you need to check out this video series. In this videos series, I will show you how to use Microsoft Excel in different ways that will make you far more effective at working with data. I'm also going to expand your knowledge beyond Excel and show you tips, tricks, and tools from other top data analytics tools such as R Tidyverse, Python, Data Visualisation tools such as Tableau, Qlik View, Qlik Sense, Plotly, AWS Quick Sight and others. We'll start to touch on areas such as big data, machine learning, and cloud computing and see how you can develop your data skills to get involved in these exciting areas. Excel Formulas such as vlookup and sumifs are some of the top reasons for slow spreadsheets. Alternatives for vlookup include power query (Excel 2010 and Excel 2013) which has recently been renamed to Get and Transform in Excel 2016. Large and complex vlookup formulas can be also done very efficiently in R. Using the R Tidyverse libraries you can use the join functions to merge millions of records effortlessly. In comparison to Excel Vlookup, R Tidyverse Join can pull on multiple columns all at the same time. Microsoft Excel Power Query and R Tidyverse Joins are similar to the joins that you do in databases / SQL. The benefit that they have over relational databases such as Microsoft Access, Microsoft SQL Server, MySQL, etc is that they work in memory so they are actually much faster than a database. Also since they are part of an analytics tool instead of a database it is much faster and easier to build your analysis and queries all in the same tools. My very first R Tidyverse program was written to replace a Microsoft Access VBA solution which was becoming complicated and slow. Note that Microsoft Access is very limited in analytics functions and is missing things as simple as Median. Even though I had to learn R programming from scratch and completely re-write the Microsoft Access VBA solution it was so much easier and faster. It blew my mind how much easier R programming with R Tidyverse was than Microsoft Access VBA or Microsoft Excel VBA. If you have any VBA skills or are looking to learn VBA you should definitely checkout my videos on R Tidyverse. To understand why R Tidyverse is so much easier to work with than VBA. R Tidyverse is designed to work directly with your data. So If you want to add a calculated column that’s around one line of script. In Excel VBA, the VBA is used to control the DOM (Document Object Model). In Excel that means that you VBA controls things like cells and sheets. This means your VBA is designed to capture the steps that you would normally do manually in Microsoft Excel or Microsoft Access. VBA is not actually designed to work directly with your data. Note the most efficient path is to reduce the data pulled down from the database in the first place. This is referring to the amount of data you are pulling down from your data warehouse or data lake. It makes no sense to pull data from a data warehouse / data lake to pull into another database to query add joins / lookups to then pull it into Excel or other analysis tool. Often analyst build these intermediate databases because they either don’t have control of the data warehouse or they need to join additional information. All of these operations are done significantly faster in a tool such as R Tidyverse or Microsoft Excel Power Query.
Views: 3350 Jonathan Ng
How to create an interactive reporting tool in Excel
 
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Microsoft Certified Trainer Melissa Esquibel shows you how to slice and dice data and present it in an attractive visual package.
Data Analysis with Python and Pandas Tutorial Introduction
 
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Pandas is a Python module, and Python is the programming language that we're going to use. The Pandas module is a high performance, highly efficient, and high level data analysis library. At its core, it is very much like operating a headless version of a spreadsheet, like Excel. Most of the datasets you work with will be what are called dataframes. You may be familiar with this term already, it is used across other languages, but, if not, a dataframe is most often just like a spreadsheet. Columns and rows, that's all there is to it! From here, we can utilize Pandas to perform operations on our data sets at lightning speeds. Sample code: http://pythonprogramming.net/data-analysis-python-pandas-tutorial-introduction/ Pip install tutorial: http://pythonprogramming.net/using-pip-install-for-python-modules/ Matplotlib series starts here: http://pythonprogramming.net/matplotlib-intro-tutorial/
Views: 414107 sentdex
How to Analyze Satisfaction Survey Data in Excel with Countif
 
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Purchase the spreadsheet (formulas included!) that's used in this tutorial for $5: https://gum.co/satisfactionsurvey ----- Soar beyond the dusty shelf report with my free 7-day course: https://depictdatastudio.teachable.com/p/soar-beyond-the-dusty-shelf-report-in-7-days/ Most "professional" reports are too long, dense, and jargony. Transform your reports with my course. You'll never look at reports the same way again.
Views: 331679 Ann K. Emery
Qualitative Data Analysis - Coding & Developing Themes
 
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This is a short practical guide to Qualitative Data Analysis
Views: 86798 James Woodall
Big Data Use Cases | Banking Data Analysis Using Hadoop | Big Data Case Study Part 1
 
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Big Data Use Cases: Banking Data Analysis Using Hadoop | Hadoop Tutorial Part 1 A leading banking and credit card services provider is trying to use Hadoop technologies to handle an analyse large amounts of data. Currently the organization has data in the RDBMS but wants to use the Hadoop ecosystem for storage, archival and analysis of large amounts of data. let’s get into the tutorial, Welcome to online Big Data training video conducted by Acadgild. This is the series of tutorial consists of real world Big Data use cases. In this project, you will be able to learn, • Understand the Project Requirement • What exactly the project is talking about • From where the data is coming • How the data is getting loaded into Hadoop, and • The different analysis that is performed with the Data Go through the entire video to understand the Big Data problems with finance departments and how to track the data. Enroll for big data and Hadoop developer training and certification to become successful Developer, https://acadgild.com/big-data/big-data-development-training-certification?utm_campaign=enrol-bigdata-usecase-part1-iQrao1C7juk_medium=VM&utm_source=youtube For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 19503 ACADGILD
Import Data and Analyze with Python
 
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Python programming language allows sophisticated data analysis and visualization. This tutorial is a basic step-by-step introduction on how to import a text file (CSV), perform simple data analysis, export the results as a text file, and generate a trend. See https://youtu.be/pQv6zMlYJ0A for updated video for Python 3.
Views: 183084 APMonitor.com
Data Scientist Vs Data Analyst
 
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In this video I want to talk about the differences between a data scientist and a data analyst. is data science a viable career and if so should you try to become a data scientist or a data analyst. ► Full Playlist Exploring All Things Data Science ( http://bit.ly/2mB4G0N ) ► Top 4 Best Laptops for the Data Industry ( https://youtu.be/Vtk50Um_yxA ) ► Data Scientist Masters Certification ( http://bit.ly/2yCbsac ) ► Get the Best Certified Tutorials on Data Analytics... http://jobsinthefuture.com/index.php/2017/10/13/data-scientist-vs-data-analytics-what-is-the-big-data-difference/ Questions: - What is the best career path for a data scientist? - How do I become a data analyst? - What is the difference between a data scientist and a data analyst? - Is data science the same as data analytics? - Is data science a viable career path? - Is data analytics a viable career path? Jobs related to data science are booming right now with the tech industry growing at a rapid pace, but there is a lot of confusion between the Role of a Data Scientist and a Data Analyst... I am going to QUICKLY breakdown the difference for you so that you can get started right away with your career in the Data Analytics industry! First of all what is data analytics... Data analytics is the extraction a large, large, large amounts of data that are stored within a data base. This data comes from a multiplicity of places all over the world via website traffic, in-store and online purchases, social media activity, traffic patterns, etc, etc, etc.... the list could go on and on. Basically everything we do is being collected to be used as data to advertise to us, keep us safer when we are driving, or help us find the restaurant we want to eat at. Now to The Role of Data Scientist - The IT Rock Star! Data Scientists are the top professionals in their industry. They usually hold a Masters Degree in some relative Computer Science degree or even a PhD. They understand, very well, data from a business point of view and he/she can make accurate prediction of the data to advise clients on their next big business move! Data scientists have a solid foundation of computer applications, modeling, statistics and math! Highly Advanced in coding (Python, MySQL, R, JavaScript, etc... Ability to do high levels of math quickly Fantastic Statistical Analysis Abilities Great Communication Skill: Written and Oral And they have a brilliant Knack for communicating between the IT world and the Business Professionals. Starting Salary: $115,000 The Role of Data Analyst A Data Analyst is very important in the world of data science. They are in charge of collecting, organizing, and and obtaining statistical information from a large amount of data sets (Data sets are very large pools of data that must be searched in order to find the data that is relevant to a specific study). They are also the ones responsible for formulating all of their findings into an accurate report of powerpoint presentation to give to their client or internal team. Strong Understanding of Hadoop Based Analytics (Program to help extract data from large data sets and the analyze the data) Familiar with data analytics in a business setting Must have data storing a retrieval skills Proficiency in decision making Have the ability to transform data into understandable presentation Starting Salary: $60,000 ------- SOCIAL Twitter ► @jobsinthefuture Facebook ►/jobsinthefuture Instagram ►@Jobsinthefuture WHERE I LEARN: (affiliate links) Lynda.com ► http://bit.ly/2rQB2u4 edX.org ► http://fxo.co/4y00 MY FAVORITE GEAR: (affiliate links) Camera ► http://amzn.to/2BWvE9o CamStand ► http://amzn.to/2BWsv9M Compute ► http://amzn.to/2zPeLvs Mouse ► http://amzn.to/2C0T9hq TubeBuddy ► https://www.tubebuddy.com/bengkaiser ► Download the Ultimate Guide Now! ( https://www.getdrip.com/forms/883303253/submissions/new ) Thanks for Supporting Our Channel! DISCLAIMER: This video and description contains affiliate links, which means that if you click on one of the product links, I’ll receive a small commission. This help support the channel and allows us to continue to make videos like this. Thank you for the support!
Views: 88138 Ben G Kaiser
Analyzing Big Data in less time with Google BigQuery
 
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Most experienced data analysts and programmers already have the skills to get started. BigQuery is fully managed and lets you search through terabytes of data in seconds. It’s also cost effective: you can store gigabytes, terabytes, or even petabytes of data with no upfront payment, no administrative costs, and no licensing fees. In this webinar, we will: - Build several highly-effective analytics solutions with Google BigQuery - Provide a clear road map of BigQuery capabilities - Explain how to quickly find answers and examples online - Share how to best evaluate BigQuery for your use cases - Answer your questions about BigQuery
Views: 48974 Google Cloud Platform
How to Use an Excel Data Table for "What-if" Analysis
 
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An Excel Data Table is a great way to see the results of substituting two values in a formula. For example, to see what your monthly payment will be by changing BOTH the Interest Rate and the amount that you borrow. However, reading about how to set up an Excel Data Table can be confusing. Where, exactly, do you place the Row Inputs and the Column Inputs? Seeing how a Data Table is constructed is SO much easier. I invite you to watch this short Excel Training Video Lesson to see how to set up and use an Excel Data Table to analyze your data. You can find additional resources on my website: www.thecompanyrocks.com - I look forward to your comments.
Views: 565982 Danny Rocks
Survey Data Analysis using Google Form surveys
 
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Survey Data Analysis using Google Form surveys
Views: 26722 HiMrBogle
Microsoft Excel for Flight Data Analysis Part 1
 
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Demonstration of how a flight data analyst can utilize some of the advanced capabilities of Microsoft Excel for analyzing aircraft flight data as part of a Flight Data Monitoring or FOQA program.
Views: 4471 Scaled Analytics
SPSS for questionnaire analysis:  Correlation analysis
 
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Basic introduction to correlation - how to interpret correlation coefficient, and how to chose the right type of correlation measure for your situation. 0:00 Introduction to bivariate correlation 2:20 Why does SPSS provide more than one measure for correlation? 3:26 Example 1: Pearson correlation 7:54 Example 2: Spearman (rhp), Kendall's tau-b 15:26 Example 3: correlation matrix I could make this video real quick and just show you Pearson's correlation coefficient, which is commonly taught in a introductory stats course. However, the Pearson's correlation IS NOT always applicable as it depends on whether your data satisfies certain conditions. So to do correlation analysis, it's better I bring together all the types of measures of correlation given in SPSS in one presentation. Watch correlation and regression: https://youtu.be/tDxeR6JT6nM ------------------------- Correlation of 2 rodinal variables, non monotonic This question has been asked a few times, so I will make a video on it. But to answer your question, monotonic means in one direction. I suggest you plot the 2 variables and you'll see whether or not there is a monotonic relationship there. If there is a little non-monotonic relationship then Spearman is still fine. Remember we are measuring the TENDENCY for the 2 variables to move up-up/down-down/up-down together. If you have strong non-monotonic shape in the plot ie. a curve then you could abandon correlation and do a chi-square test of association - this is the "correlation" for qualitative variables. And since your 2 variables are ordinal, they are qualitative. Good luck
Views: 486245 Phil Chan
Making data mean more through storytelling | Ben Wellington | TEDxBroadway
 
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Ben Wellington uses data to tell stories. In fact, he draws on some key lessons from fields well outside computer science and data analysis to make his observations about New York City fascinating. Never has a fire hydrant been so interesting as in this talk. Ben Wellington is a computer scientist and data analyst whose blog, I Quant NY, uses New York City open data to tell stories about everything from parking ticket geography to finding the sweet spot in MetroCard pricing. His articles have gone viral and, in some cases, led to policy changes. Wellington teaches a course on NYC open data at the Pratt Institute and is a contributor to Forbes and other publications. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at http://ted.com/tedx
Views: 130536 TEDx Talks
Quick Data Analysis with Google Sheets | Part 1
 
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Spreadsheet software like Excel or Google Sheets are still a very widely used toolset for analyzing data. Sheets has some built-in Quick analysis features that can help you to get a overview on your data and very fast get to insights. #DataAnalysis #GoogleSheet #measure 🔗 Links mentioned in the video: Supermetrics: http://supermetrics.com/?aff=1014 GA Demo account: https://support.google.com/analytics/answer/6367342?hl=en 🎓 Learn more from Measureschool: http://measureschool.com/products 🚀Looking to kick-start your data journey? Hire us: https://measureschool.com/services/ 📚 Recommended Measure Books: https://kit.com/Measureschool/recommended-measure-books 📷 Gear we used to produce this video: https://kit.com/Measureschool/measureschool-youtube-gear Our tracking stack: Google Analytics: https://analytics.google.com/analytics/web/ Google Tag Manager: https://tagmanager.google.com/ Supermetrics: http://supermetrics.com/?aff=1014 ActiveCampaign: https://www.activecampaign.com/?_r=K93ZWF56 👍 FOLLOW US Facebook: http://www.facebook.com/measureschool Twitter: http://www.twitter.com/measureschool
Views: 7478 Measureschool
Big Data Analytics Using Python | Python Big Data Tutorial | Python And Big Data | Simplilearn
 
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This Big Data Analytics using Python tutorial will explain what is Data Science, roles and responsibilities of a Data scientist, various applications of Data Science, how Data Science and Big Data work together and how andwhy Data Science if gaining importance. Every sector of business is being transformed by the modern deluge of data. This spells doom for some, and creates massive opportunity for others. Those who thrive in this environment will do so only by quickly converting data into meaningful business insights and competitive advantage. Business analysts and data scientists need to wield agile tools, instead of being enslaved by legacy information architectures. Subscribe to Simplilearn channel for more Big Data and Hadoop Tutorials - https://www.youtube.com/user/Simplilearn?sub_confirmation=1 Check our Big Data Training Video Playlist: https://www.youtube.com/playlist?list=PLEiEAq2VkUUJqp1k-g5W1mo37urJQOdCZ Big Data and Analytics Articles - https://www.simplilearn.com/resources/big-data-and-analytics?utm_campaign=BigData-Python-cUw3DsDpQCE&utm_medium=Tutorials&utm_source=youtube To gain in-depth knowledge of Big Data and Data Science, check our Integrated Big Data and Data Science Certification Training Course: https://www.simplilearn.com/integrated-program-in-big-data-and-data-science?utm_campaign=BigData-Python-cUw3DsDpQCE&utm_medium=Tutorials&utm_source=youtube - - - - - - - - What are the course objectives of this Big Data and Data Science Course? Mastering the field of data science begins with understanding and working with the core technology frameworks used for analyzing big data. You’ll learn the developmental and programming frameworks Hadoop and Spark used to process massive amounts of data in a distributed computing environment, and develop expertise in complex data science algorithms and their implementation using , the preferred language for statistical processing. The insights you will glean from the data are presented as consumable reports using data visualization platforms such as Tableau. - - - - - - - - Why should you take this Big Data and Data Science Course? As an expert in this field, you will need to have a working knowledge of the three key pillars in the analytics ecosystem: data management, data science and reporting and visualization. This master’s program will hone your skills in: Big Data: Big data management is the ability to store and process voluminous amounts of unstructured data. Today with the overflow of online information, most companies are adopting big data practices to manage these huge volumes. Hadoop provides the distributed file system for storage, and MapReduce programming in Java is used for the processing. In the analytics lifecycle, it is critical to be able to store and query data to feed the necessary algorithms. Data Science: Data Science algorithms use data to create insights. Once you have an effective way to crunch data, you can use historical data for descriptive and predictive analytics. This is done using a programming language like R or Python, which utilize libraries for statistical analysis. Learning these languages are important to be able to design custom models for analytics, a key expectation for any data scientist. These skills range from basic probability to advanced machine learning. Reporting and Visualization: Once you have insights into data, it is important to make the insights available to the organization using visualization and reporting. This program also includes a number of electives to ensure you get broad knowledge of the entire ecosystem and complementary skills in these fields. The two-year period ensures you have enough time to ramp up, develop skills and apply them in real world scenarios. - - - - - - - - - Who can take this Big Data and Data Science Course? Many roles can benefit from this program and pursue new career opportunities with high salaries, including: 1. Software developers and testers 2. Software architects 3. Analytics professionals 4. Business analysts 5. Data analysts 6. Data management professionals 7. Data warehouse professionals 8. Project managers 9. Mainframe professionals 10. Graduates aspiring to build a career in analytics - - - - - - - - For more updates on courses and tips follow us on: - Facebook : https://www.facebook.com/Simplilearn - Twitter: https://twitter.com/simplilearn - LinkedIn: https://www.linkedin.com/company/simplilearn - Website: https://www.simplilearn.com Get the android app: http://bit.ly/1WlVo4u Get the iOS app: http://apple.co/1HIO5J0
Views: 9852 Simplilearn
Use Excel Pivot Tables to analyse your SALES
 
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This video shows the potential power of Excel and pivot tables to analyse your sales. But first you have to set up a datamart in order to make it all possible!
Views: 82140 MrDatamart
How to analyse data with Google Analytics | Lesson 3
 
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Now that we have setup Google Analytics we can go ahead and see inside. It’s quite overwhelming at first so we will go through 3 crucial steps to get the most out of our data. Questions ABC-Analysis Segmentation 🔗 Links mentioned in the video: GA Questions Guide: http://measureschool.com/questions #GoogleAnalytics #DataAnalysis #Measure 🎓 Learn more from Measureschool: http://measureschool.com/products 🚀Looking to kick-start your data journey? Hire us: https://measureschool.com/services/ 📚 Recommended Measure Books: https://kit.com/Measureschool/recommended-measure-books 📷 Gear we used to produce this video: https://kit.com/Measureschool/measureschool-youtube-gear 👍 FOLLOW US Facebook: http://www.facebook.com/measureschool Twitter: http://www.twitter.com/measureschool
Views: 5657 Measureschool
How to Analyze Sales Data with Excel
 
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Learn how to analyze product sales data using Excel features like pivot tables and charts. For more info. pls. visit http://chandoo.org/wp/2010/09/22/analyzing-product-launch-sales/
Beginning Analytics: Interpreting and Acting on Your Data
 
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If you've just started to use Google Analytics and aren't sure which reports to look at, this video provides a helpful 1st-time analysis walkthrough. You'll learn how to interpret what you see in these key reports and what actions you should take as a result.
Views: 202663 Google
IELTS Writing Task 1 - How to Analyze Charts, Maps, and Process Diagrams
 
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In this IELTS Writing Task 1 lesson, you'll learn how to accurately analyze charts, maps, and process diagrams. I explain how you can use a question checklist to practice your Task 1 analysis abilities. I also give an example of each kind of Task 1 data set. Here are the checklist questions from the video: Instructions: To improve your ability to analyze Task 1 data, use the questions below when you see a new graph, chart, map, or process diagram. After you’re comfortable with the checklists, gradually try to use them less and less until you can analyze the data more easily. Graph or Chart: What are the axes (x and y)? What are the units of measurement? (e.g. amount, %, age, etc.) Is there more than one group being compared? (e.g. 3 different countries) Does it show change over time? (this is common for graphs) What are the time periods shown? (past, present, future) What is the general trend? (increase, decrease, etc.) Are there any large differences between groups or charts? Are there any groups or charts that share similarities? How can I break it into two parts? Map: Is there more than one map being compared? What are the time periods shown? (past, present, future) Are they in different maps or the same map? What are the most noticeable differences between the multiple maps or time periods? What parts of the map are the same in both maps/time periods? Can the map(s) be easily broken into two parts? How? Process Diagrams: Where is the start of the process? The end? How many total stages are there? What kind of process is it? Is it a cycle or a linear (start to finish) process? What does each stage do? And what is its connection with the previous stage? What is the end result? Is something produced? Can the process be easily broken into two parts? How? Watch more IELTS Master Writing Task 1 videos: https://www.youtube.com/playlist?list=PLQKm5R-SeKdOeIIbDm3k4-Bwt0PZNDdas Find more IELTS practice content: http://www.ielts-master.com
Views: 158931 IELTS Master