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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: 61015 White Crane Education
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: 1582011 ExcelIsFun
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: 28135 umnWritingStudies
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: 28410 NurLiyana Isa
Data Analyst Job Description | What 4 Skills Will You Need To Be A Data Analyst?
 
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In this video we are going to define the job description of a data analyst, what a data analyst does, and the best online course to become a data analyst. ► Full Playlist Explaining Data Jargon ( http://bit.ly/2mB4G0N ) ► Top 4 Best Laptops for Data Analysts ( https://youtu.be/Vtk50Um_yxA ) ► Break Into the Data Industry with the best data analytics online learning resources from Edureka! ( http://bit.ly/2yCbsac ) --- affiliate link to help support this channel!^ Currently the average pay for a data analyst is $76,419 on the button, according to glassdoor I receive a lot of questions about what it takes to become a data analyst and what is a data analyst. Clearing up what a data analyst does everyday and what that description means to someone looking to enter the data science industry What will you actually be asked to do on the day to day as a data analyst. ► Top 4 Responsibilities in the Daily Life of a Data Analyst: 1 ) Mathematics Although mathematics only makes up about 20% of the day to day life of a data analyst. It is still important to have a strong understanding of the foundations of mathematics. - Addition - Subtraction - Multiplication - Division - Most Importantly --- Statistics Data analytics is all about statistics. Most of the statistics will be handled by the tools you are working with, but in order to be a great data analyst it is best to know why the tools are producing specific results. A strong understanding of statistics will be useful to you. 2 ) Computer Programming You must be able to work proficiently in one or more computer programming languages. This make up for roughly 60%-70% of your daily work. in order to analyze data it must be queried (drawn) from a large data warehouse. You will use computer programming languages such as SQL, Python, and R to query data. Before we move on let me define the term Query, if it does not resonate with you. You need strong computer programming skills in order to accomplish this task. As a data analyst you will do a lot of drawing and analyzing data. ► For more info on databases, SQL, and other jargon check out our Video Series on Data Jargon ( https://www.youtube.com/playlist?list=PL_9qmWdi19yDhnzqVCAhA4ALqDoqjeUOr ) 3 ) Know the Tools of the Trade Once you query data from the database onto your workspace you will begin to utilize data analytics tools to process, scrub, and analyze data (data Jargon explained on our Video series ^^^). You will be able to perform these tasks by using tools like Hadoop, Open Refine, Tableau, Apache Spark, etc... As you process the data you will begin to see connections between the data sets. You will see some of the following errors and you will want to remove these in order to ensure that your data analysis is accurate: - Duplicated data - Improperly formatted data - Incomplete data - Inaccurate data - This data will corrupt your findings and could possibly lose you client or employer millions of dollars. Make sure you know how to use those data analytics tools WELL! 4 ) Communicate and Present Insights Data Analyst will also be called upon to clearly and consciously present your research to clients, managers, or executives. Ok, now I know you are curious if you are capable of learning all of these crucial skills. Yes, you can, but there is a clause. You have to learn from the best. The guys over at Edureka.co are the leading professionals in the big data training industry. Based out of India, home to over 101,000 individuals in the data science industry (at the time of this writing). They are eager to make a way for themselves in the new digital economy. They are on the cutting edge of data analytics and eager to teach it to anyone worldwide. Testimonies of increased salaries, new employment, and 597,089 (updated) satisfied learners make edureka the best choice to learn the skills you need in the data industry. Question is will you actually do it. Imagine deregulating yourself for the data industry. Right now, it is a black hole, you don't know what's inside, but it is screaming opportunity from the darkness. TURN ON THE LIGHT and break into the data industry. A future proof opportunity for the next decade and beyond. ► Edureka Big Data Masters Program ( http://bit.ly/2yCbsac ) affiliate link^ ------- 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 Computer ► 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!
Views: 121606 Ben G Kaiser
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: 68877 ZieneMottiar
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: 130943 Silicon Republic
Intro to Data Analysis / Visualization with Python, Matplotlib and Pandas | Matplotlib Tutorial
 
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Python data analysis / data science tutorial. Let’s go! For more videos like this, I’d recommend my course here: https://www.csdojo.io/moredata Sample data and sample code: https://www.csdojo.io/data My explanation about Jupyter Notebook and Anaconda: https://bit.ly/2JAtjF8 Also, keep in touch on Twitter: https://twitter.com/ykdojo And Facebook: https://www.facebook.com/entercsdojo Outline - check the comment section for a clickable version: 0:37: Why data visualization? 1:05: Why Python? 1:39: Why Matplotlib? 2:23: Installing Jupyter through Anaconda 3:20: Launching Jupyter 3:41: DEMO begins: create a folder and download data 4:27: Create a new Jupyter Notebook file 5:09: Importing libraries 6:04: Simple examples of how to use Matplotlib / Pyplot 7:21: Plotting multiple lines 8:46: Importing data from a CSV file 10:46: Plotting data you’ve imported 13:19: Using a third argument in the plot() function 13:42: A real analysis with a real data set - loading data 14:49: Isolating the data for the U.S. and China 16:29: Plotting US and China’s population growth 18:22: Comparing relative growths instead of the absolute amount 21:21: About how to get more videos like this - it’s at https://www.csdojo.io/moredata
Views: 288961 CS Dojo
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: 65272 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: 61945 UNICEF Innocenti
Interview with a Data Scientist
 
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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: 301038 Udacity
Data Analyst Interview Questions and Answers - For Freshers and Experienced Candidates
 
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Learn most important Data Analyst Interview Questions and Answers, asked at every interview. These Interview questions will be useful to all entry level candidates, beginners, interns and experienced candidates interviewing for the role of Data Analyst across various domains like banking, financial, marketing, statistical etc. The examples and sample answers with each question will make it easier for candidates to understand these conceptual, situational and behavioral interview questions.
Views: 37483 CareerRide
Data Analysis in SPSS Made Easy
 
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Use simple data analysis techniques in SPSS to analyze survey questions.
Views: 856594 Claus Ebster
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.
How to write the Results part 1
 
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For science students who speak English as a second or foreign language. Explains the content of the results section, and also some information about figures and tables. Here is a link to the gorilla paper discussed in the video: http://www.sciencedirect.com/science/article/pii/S0168159105004193 Here is a link to the tea paper discussed: http://europepmc.org/abstract/MED/9630386
Views: 101866 Steve Kirk
Excel 2013 Statistical Analysis #01: Using Excel Efficiently For Statistical Analysis (100 Examples)
 
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Download File: https://people.highline.edu/mgirvin/AllClasses/210Excel2013/Ch00/Excel2013StatisticsChapter00.xlsx All Excel Files for All Video files: http://people.highline.edu/mgirvin/excelisfun.htm. Intro To Excel: Store Raw Data, Data Types, Data Analysis, Formulas, PivotTables, Charts, Keyboards, Number Formatting, Data Analysis & More: (00:08) Introduction to class (00:49) Cells, Worksheets, Workbooks, File Names (02:54) Navigating Worksheets & Workbook (03:58) Navigation Keys (04:15) Keyboard move Active Sheet (05:40) Ribbon Tabs (06:25) Add buttons to Quick Access Tool Bar (07:40) What Excel does: Store Raw Data, Make Calculations, Data Analysis & Charting (08:55) Introduction to Data Analysis (10:37) Data Types in Excel: Text, Numbers, Boolean, Errors, Empty Cells (11:16) Keyboard Enter puts content in cell and move selected cell down (13:00) Data Type DEFAULT Alignments (13:11) First Formula. Entering Cell References in formulas (13:35) Keyboard Ctrl + Enter puts content in cell & keep cell selected (14:45) Why we don’t override DEFAULT Alignments (15:05) Keyboard Ctrl + Z is Undo (17:05) Proper Data Sets & Raw Data (24:21) How To Enter Data & Data Labels (24:21) Stylistic Formatting (26:35) AVERAGE Function (27:31) Format Formulas Differently than Raw Data (28:30) Keyboard Ctrl + C is Copy. Keyboard Ctrl + V is Paste (29:59) Use Eraser remove Formatting Only (29:19) Keyboard Ctrl + B adds Bold (29:57) Excel’s Golden Rule (31:43) Keyboard F2 puts cell in Edit Mode (32:01) Violating Excel’s Golden Rule (34:12) Arrow Keys to put cell references in formulas (35:40) Full Discussion about Formulas & Formulas Elements (37:22) SUM function Keyboard is Alt + = (38:22) Aggregate functions (38:50) Why we use ranges in functions (40:56) COUNT & COUNTA functions (42:47) Edit Formula & change cell references (44:18) Absolute & Relative Cell References (45:52) Use Delete Key, Not Right-click Delete (46:40) Fill Handle & Angry Rabbit to copy formula (47:41) Keyboard F4 Locks Cell Reference (make Absolute) (49:45) Keyboard Tab puts content in Cell and move selected Cell to right (50:55) Order of Operation error (52:17) Range Finder to find formula errors (52:34) Lock Cell Reference after you put cell in Edit Mode (53:58) Quickly copy an edited formula down a column (53:07) F2 key in last cell to find formula errors (54:15) Fix incorrect range in function (54:55) SQRT function & Fractional Exponents (57:20) STDEV.P function (58:10) Navigate Large Data Sets (58:48) Keyboard Ctrl + Arrow jumps to bottom of data set (59:42) Keyboard Ctrl + Shift + Arrow selects to bottom of data set (Current Range) (01:01:41) Keyboard Shift + Enter puts content in Cell and move selected Cell up (01:02:55) Counting with conditions or criteria: COUNTIFS function (01:03:43) Keyboard Ctrl + Backspace jumps back to Active Cell (01:05:31) Counting between an upper & lower limit with COUNTIFS (01:07:36) COUNTIFS copied down column (01:10:08) Joining Comparative Operator with Cell Reference in formula (01:12:50) Data Analysis features in Excel (01:13:44) Sorting (01:16:59) Filtering (01:20:39) Introduction to PivotTables (01:23:39) Create PivotTable dialog box (01:24:33) Dragging & dropping Fields to create PivotTable (01:25:31) Dragging Field to Row area creates a Unique List (01:26:17) Outline/Tabular Layout (01:27:00) Value Field Settings dialog to change: Number Formatting, Function, Name (01:28:12) 2nd & 3rd PivotTable examples (01:31:23) What is a Cross Tabulated Report? (01:33:04) Create Cross Tabulated Report w PivotTable (01:35:05) Show PivotTable Field List (01:36:48) How to Pivot the Report (01:37:50) Summarize Survey Data with PivotTable. (01:38:34) Keyboard Alt, N, V opens PivotTable dialog box (01:41:38) PivotTable with 3 calculations: COUNT, MAX & MIN (01:43:25) Count & Count Number calculations in a PivotTable (01:45:30) Excel 2013 Charts to Visually Articulate Quantitative Data (01:47:00) #1 Rule for Charts: No Chart Junk! (01:47:30) Explain chart types: Column, Bar, Pie, Line and X-Y Scatter Chart (01:51:34) Create Column Chart using Recommended Chart feature (01:53:00) Remove Field Buttons from Pivot Chart (01:54:10) Chart Formatting Task Pane (01:54:45) Vary Fill Color by point (01:55:15) Format Axis with Numbers by Formatting Source Data in PivotTable (01:56:02) Add Data Labels to Chart (01:57:28) Copy Chart & Create Bar Chart (01:57:48) Change Chart Type (01:58:15) Change Gap Width. (01:59:17) Create Pie Chart (01:59:23) Do NOT use 3-D Pie (01:59:42) Add % Data Labels to Pie Chart (02:00:25) Create Line Chart From PivotTable (02:01:20) Link Chart Tile to Cell (02:02:20) Move a Chart (02:02:33) Create an X-Y Scatter Chart (02:03:35) Add Axis Labels (02:05:27) Number Formatting to help save time (02:07:24) Number Formatting is a Façade (02:10:27) General Number Format (02:10:52) Percentage Number Formatting (02:14:03) Don’t Multiply Relative Frequency by 100 (02:17:27) Formula for % Change & End Amount
Views: 431306 ExcelIsFun
Top Data Analytics Skills You Should Know (Career Insights)
 
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Thanks to the digital revolution, analytics is sweeping across industries in a huge way. Mastering certain data analytics skills can enable you to chart a successful career in this lucrative and rapidly changing domain. Data analytics is changing the way we live - from that app you use to navigate to work everyday or the cabs you hail through your phone, or the platforms you order food from, to the online shopping you find yourself doing on weekends. All of this activity generates massive amounts of data. This is where companies who have created these products come in. Analytics is changing the way we also do business. Deriving insights from large volumes of data to enable better decision-making and an even better customer experience has become the norm for competitive firms these days. Which is why being a data analyst in this world pays off well. Through this UpGrad Careers-In-Shorts Series, Rohit Sharma, Program Director at UpGrad, takes you through all you need to know about data analytics - the most promising career of tomorrow! The first one here is about the 4 core skills that will help you transition to the field of data analytics - a career of the future. Want to Be a Data Analyst? Here are Top Skills & Tools to Master: https://blog.upgrad.com/want-to-be-a-data-analyst-here-are-top-skills-tools-to-master/?utm_source=YouTube&utm_medium=Organic_Social&utm_campaign=YouTube_Video&utm_term=YouTube_Video_Data&utm_content=YouTube_Video_Data_Analytics_Skills_Blog_Link Transition to one of the coolest jobs in industry. Enroll now to be an expert Data Analyst: https://upgrad.com/data-science/?utm_source=YouTube&utm_medium=Organic_Social&utm_campaign=YouTube_Video&utm_term=YouTube_Video_Data&utm_content=YouTube_Video_Data_Analytics_Skills UpGrad takes pride in constantly churning out content that is contemporary, written by subject matter experts and delves into the world of Data Science, Big Data, Digital Marketing, Entrepreneurship, Product Management, Machine Learning and Artificial Intelligence, Software Development on regular basis. Stay on top of your industry by interacting with us on our social channels: Follow us on Instagram: https://instagram.com/upgrad_edu Like us on Facebook: https://www.facebook.com/UpGradGlobal Follow us on Twitter: https://www.twitter.com/upgrad_edu Follow us on LinkedIn: https://in.linkedin.com/company/ueducation
Views: 50844 upGrad
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: 170081 TEDx Talks
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/
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: 15786 Jonathan Ng
Microsoft Power Tools for Data Analysis: Dashboards & Reports. Class Introduction Video. MSPTDA #01.
 
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Download Excel File: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/Intro/001-MSPTDA-IntroToClass.xlsx Download pdf Notes: https://people.highline.edu/mgirvin/AllClasses/348/MSPTDA/Content/Intro/001-MSPTDA-IntroToClass.pdf This video introduces the topics that will be covered in this Highline College BI 348 Class: Name of Class: BI 348 – Microsoft Power Tools for Data Analysis: • Power Query • Power Pivot • DAX • Power BI Desktop • Excel For Creating: • Data Models, Reports, Dashboards and Analytics Taught by Mike excelisfun Girvin, Excel MVP 2013-2018 • A class about connecting to multiple source of data, transforming the data into a refreshable & dynamic data model, and building reports and dashboards to provide insightful and actionable information. Prerequisites for this class: • Busn 216: Excel Basics, https://www.youtube.com/playlist?list=PLrRPvpgDmw0n34OMHeS94epMaX_Y8Tu1k • Busn 218: Advanced Excel, https://www.youtube.com/playlist?list=PLrRPvpgDmw0lcTfXZV1AYEkeslJJcWNKw • Busn 210: Business Statistics, https://www.youtube.com/playlist?list=PLrRPvpgDmw0ngx_uPhvasTbOWLOztsaBj What Version of Excel: • Office 365 (updated each month) What Version of Power BI Desktop: • Free Tool we will download (update each month) Over View of Topics for the class: 1. Data Analysis / Business Intelligence terms and concepts that we will learn in this class: • Proper Data Set • Fact Table • Dimension Tables • Relationships • Star Schema • ETL • Measures • Dashboards • SQL • Data Warehousing   2. Learn how to use Excel Power Query: • Import Data from multiple sources • Clean and Transform Data • Create Data Components for Star Schema Data Models • Load Data To Excel, the Data Model and Connection Only • Replace Complicated Excel Solutions with Power Query Solution • Use the Power query User Interface to create Power Query Solutions • Learn about the Case Sensitive, Function-based M Code Language that is behind the scenes in Power Query 3. Learn how to use Excel Power Pivot: • Excel Power Pivot provides: i. Data Model where we can have multiple tables, formulas and relationships (Star Schema) ii. Columnar Database to hold "Big Data" and process quickly over that "Big Data" iii. New Formula Language called DAX: 1. Many More Calculations than in Standard PivotTable 2. Build One Formula that can work in many reports 3. Add Number Formatting to Formulas • Excel Power Pivot to: i. Replace VLOOKUP Formulas and Single Flat PivotTable Data Source with Multiple Tables, Relationships in the Data Model to create more efficient Reports & Dashboards ii. Use Power Pivot Columnar Database to hold millions of rows of data iii. DAX formulas have more Power than Standard PivotTable Calculations 4. Learn about Building Star Schema Data Models: a. Why they are important in Power Pivot and Power BI Desktop b. How to build them using: i. Power Query ii. Power Pivot iii. DAX iv. Power BI Desktop 5. Learn how to author DAX Formulas for Excel’s Power Pivot & Power BI Desktop: a. Calculated Column Formulas for Data Model b. Measure Formulas for PivotTables c. DAX Functions like SUMX, CALCULATE, RELATED, and Much More… d. Lean why we must create Explicit rather than Implicit formulas e. Learn how Row Context works in formulas f. Learn how Filter Context works in formulas g. Learn about Scalar & Table Functions h. Use DAX Studio to visualize and analyze DAX Formulas 6. Learn how to use Power BI Desktop: a. Power Query to import, clean, transform and create Star Schema Data Models b. Create Relationships c. Create DAX Formulas d. Build Interactive Visualizations e. Build Dashboards   7. Learn how to use Excel: • Spreadsheet Formulas & Functions • Standard PivotTables • Power Query • Power Pivot • Build Data Model PivotTables and the resultant Reports, Dashboards and Analytics 8. Building Refreshable, Insightful Dashboards a. Build Excel Dashboards b. Build Power BI Dashboards 9. Case Studies to practice using Power Pivot & Power BI Desktop for Reporting, Building Dashboards and Building Business Analytics Solutions The Power Query logo used in this video is copyright of and used with the express permission of https://powerquery.training Thanks to Ken Puls and Miguel Escobar for letting me use their logo!!!!
Views: 33121 ExcelIsFun
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; *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. 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 Nb: 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. Text and video (including audio) © Kent Löfgren, Sweden
Views: 766301 Kent Löfgren
Data Science vs Big Data vs Data Analytics | Simplilearn
 
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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. According to IBM, 2.5 billion gigabytes (GB) of data was generated every day in 2012. An article by Forbes states that Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet. Which makes it extremely important to at least know the basics of the field. After all, here is where our future lies. In this video, we will differentiate between the Data Science, Big Data, and Data Analytics, based on what it is, where it is used, the skills you need to become a professional in the field, and the salary prospects in each field. 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: 188701 Simplilearn
Data Analysis with Python : Exercise – Titanic Survivor Analysis | packtpub.com
 
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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: 30508 Packt Video
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 GTM Copy Paste https://chrome.google.com/webstore/detail/gtm-copy-paste/mhhidgiahbopjapanmbflpkcecpciffa 🚀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: 11232 Measureschool
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: 1129067 Danny Rocks
Data Analysis with Python for Excel Users
 
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A common task for scientists and engineers is to analyze data from an external source. By importing the data into Python, data analysis such as statistics, trending, or calculations can be made to synthesize the information into relevant and actionable information. See http://apmonitor.com/che263/index.php/Main/PythonDataAnalysis
Views: 184070 APMonitor.com
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: 171933 YaleUniversity
Qualitative Data Analysis - Coding & Developing Themes
 
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This is a short practical guide to Qualitative Data Analysis
Views: 137942 James Woodall
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).
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: 202705 IELTS Master
Data Analytics for Beginners | Introduction to Data Analytics | Data Analytics Tutorial
 
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Data Analytics for Beginners -Introduction to Data Analytics https://acadgild.com/big-data/data-analytics-training-certification?utm_campaign=enrol-data-analytics-beginners-THODdNXOjRw&utm_medium=VM&utm_source=youtube Hello and Welcome to data analytics tutorial conducted by ACADGILD. It’s an interactive online tutorial. Here are the topics covered in this training video: • Data Analysis and Interpretation • Why do I need an Analysis Plan? • Key components of a Data Analysis Plan • Analyzing and Interpreting Quantitative Data • Analyzing Survey Data • What is Business Analytics? • Application and Industry facts • Importance of Business analytics • Types of Analytics & examples • Data for Business Analytics • Understanding Data Types • Categorical Variables • Data Coding • Coding Systems • Coding, coding tip • Data Cleaning • Univariate Data Analysis • Statistics Describing a continuous variable distribution • Standard deviation • Distribution and percentiles • Analysis of categorical data • Observed Vs Expected Distribution • Identifying and solving business use cases • Recognizing, defining, structuring and analyzing the problem • Interpreting results and making the decision • Case Study Get started with Data Analytics with this tutorial. Happy Learning 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: 269899 ACADGILD
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: 391983 Ann K. Emery
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: 922078 Dr Nic's Maths and Stats
How to tabulate, analyze, and prepare graph from Likert Scale questionnaire data using Ms Excel.
 
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This video describes the procedure of tabulating and analyzing the likert scale survey data using Microsoft Excel. This video also explains how to prepare graph from the tabulated data. Photo courtesy: http://littlevisuals.co/
Views: 127525 Edifo
How to Clean Up Raw Data in Excel
 
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Al Chen (https://twitter.com/bigal123) is an Excel aficionado. Watch as he shows you how to clean up raw data for processing in Excel. This is also a great resource for data visualization projects. Subscribe to Skillshare’s Youtube Channel: http://skl.sh/yt-subscribe Check out all of Skillshare’s classes: http://skl.sh/youtube Like Skillshare on Facebook: https://www.facebook.com/skillshare Follow Skillshare on Twitter: https://twitter.com/skillshare Follow Skillshare on Instagram: http://instagram.com/Skillshare
Views: 99678 Skillshare
Rstudio - an introduction for data analysis
 
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RStudio for beginners doing data analysis -R environment - creating objects -loading data (a txt file) - saving your code -How to refer to variables | plots for univariate continuous variables (histogram, boxplot) -Comments with # - Altering a commands default settings (eg 1 sample t-test)
Views: 7088 Phil Chan
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: 204389 Google
Excel and Questionnaires: How to enter the data and create the charts
 
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This is a tutorial on how to enter the results of your questionnaires in Excel 2010. It then shows you how to create frequency tables (using the countif function not the frequency function). The next stage is creating charts.
Views: 377352 Deirdre Macnamara
The do's and don'ts of data analysis and reporting
 
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F. Peter Guengerich, the Journal of Biological Chemistry interim editor-in-chief, discusses the journal's policies on image manipulation and shows several examples of inappropriate manipulation. Access the interactive presentation here: http://www.asbmb.org/video/2016/pguengerich/presentation.html.
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: 79652 Google Cloud Platform
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 GTM Copy Paste https://chrome.google.com/webstore/detail/gtm-copy-paste/mhhidgiahbopjapanmbflpkcecpciffa 🚀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: 18744 Measureschool
Part 1 - Using Excel for Open-ended Question Data Analysis
 
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Completing data analysis on open-ended questions using Excel. For analyzing multiple responses to an open-ended question see Part 2: https://youtu.be/J_whxIVjNiY Note: Selecting "HD" in the video settings (click on the "gear" icon) makes it easier to view the data entries
Views: 171528 Jacqueline C
What is OLAP?
 
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This video explores some of OLAP's history, and where this solution might be applicable. We also look at situations where OLAP might not be a fit. Additionally, we investigate an alternative/complement called a Relational Dimensional Model. To Talk with a Specialist go to: http://www.intricity.com/intricity101/ www.intricity.com
Views: 378765 Intricity101
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: 20130 Homer White
Data Collection: Understanding the Types of Data.
 
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Data falls into several categories. Each type has some pros and cons, and is best suited for specific needs. Learn more in this short video from our Data Collection DVD available at http://www.velaction.com/data-collection-lean-training-on-dvd/.
Views: 154458 VelactionVideos
QlikView Tutorial For Beginners | What Is QlikView | Qlikview Tutorial | QlikView Training | Edureka
 
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( Qlikview Certification Training - https://www.edureka.co/qlikview ) This video gives you an introduction to QlikView & how is it used for data discovery and data visualization. You will also learn briefly about Business Intelligence and understand Qlikview with a demonstration. About the Course Edureka's Qlikview course is specifically made for professionals who want to learn to use Qlikview for business intelligence and to bring visual insights to the data. This course covers all the concepts of Qlikview tools like Data Interpretation, Designing, Modeling and then dives into advance features of Qlikview like Analyzying the data, Discovering the hidden data and generating attractive graphs and charts. Course Objectives At the end of the course the participants should be able to: 1. Apply various visualization and modeling techniques 2. Implement Qlikview features like In-memory, Associate and Decision-making 3. Perform Data Analysis 4. Access the multiple charts / tables / objects for Dashboard analysis 5. Work on a real life Project, implementing data into image for Dashboard Content for Script-Modeling and Security will be given as complimentary self paced videos. Who should go for this Course? The course is designed for professionals who want to learn Dashboard techniques by using Qlikview. The following professionals can go for this course: 1. Analytics Professionals 2. BI /ETL/DW Professionals 3. Project Managers 4. Testing Professionals 5. Business and Reporting professionals 6. Software Developers and Architects 7. Graduates aiming to build a career in Visualization & Analysis What are the pre-requisites for this Course? There are no such pre-requisites for this course, but if you have some knowledge of file type, SQL, DWH, RDBMS then it will be beneficial. Project Work Towards the end of the Course, you will be working on a live project where you will be using Retail & Banking Dataset to perform various data analytics. Project #1: Sales Analysis Industry: Retail Data: Excel File/Oracle/Sql Server. Problem Statement: Creation of Dashboard via multiple data source using Qlikview. Project #2: Creation of Dashboard for Customer analysis Industry: Banking Data: Excel File/Oracle/Sql Server. Problem Statement: Analyzing customer data using Qlikview. For more information, please write back to us at [email protected] Call us at US: 1844 230 6362(toll free) or India: +91-90660 20867 Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 83554 edureka!
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: 29113 THE MIND HEALING