SQL+Power BI+Excel Data Analysis Portfolio Project| For Beginners| Health Analytics

Estimated read time: 1:20

Summary

In this comprehensive tutorial video, Stacey Samoe walks viewers through a SQL, Power BI, and Excel data analysis project focused on a health analytics dataset. The dataset comprises various details about patients diagnosed with OCD, including their demographics and diagnostic scores. Stacey covers everything from data preparation in SQL, through detailed analysis, to creating visually appealing dashboards in Power BI and Excel, while sharing personal anecdotes and insightful tips for budding data analysts.

Highlights

Stacey introduces a health analytics project using SQL, Power BI, and Excel, focusing on OCD patient data. 📈
Discusses preparing data in SQL by altering and querying the dataset for analysis. 💻
Demonstrates creating dashboards in Power BI to visualize findings from SQL analysis. 📊
Replicates the Power BI dashboard in Excel, emphasizing visualization similarities. 🖥️
Shares personal experiences about starting and growing her YouTube channel. 📹
Offers motivational tips and encourages viewers to engage and subscribe for more content. 🗣️

Key Takeaways

Learning SQL is essential for manipulating and querying data effectively. 📊
Power BI offers more visually appealing and convenient dashboards than Excel. 🎨
SQL helps calculate specific metrics like gender distribution in dataset analysis. 👨‍👩‍👧
Excel can still be powerful for visualization if you master its tools. 🛠️
Stacey plans future content diversifying into more analytics tools like Looker. 🔍
Understanding aggregate functions in SQL expands analytical capabilities. 🔢
Personal growth stories from Stacey provide inspiration for aspiring data analysts. 🌟
Engaging with the community through comments can enhance learning and growth. 💬

Overview

Stacey Samoe warmly welcomes viewers to her channel, where she embarks on an insightful journey into health data analytics. This time, she tackles a dataset featuring patients diagnosed with OCD, leveraging her expertise in SQL, Power BI, and Excel to dissect and visualize this information comprehensively. Her step-by-step tutorial ensures even beginners can follow along and apply these techniques effectively.

The video delves into the nitty-gritty of using SQL for initial data manipulation, showcasing the power of aggregate functions and group-by clauses. Stacey intricately details how to set up dashboards in Power BI, turning raw data into actionable insights. She emphasizes the user-friendly nature of Power BI's tools, contrasting them with Excel's capabilities and nuances in dashboard creation.

Lastly, Stacey shares her personal journey into data analytics, offering viewers a glimpse into her learning process and the development of her YouTube channel. She candidly discusses the challenges and triumphs of creating content and connects with her audience by encouraging questions and feedback, thus fostering a learning community around her channel.

Chapters

00:00 - 01:30: Introduction The introduction chapter opens with a warm welcome to both new and returning viewers to the channel, setting a friendly tone. The speaker introduces the main project of the day, which involves a SQL, PowerBI, and Excel project focused on health analytics. The project will use a dataset containing information about individuals diagnosed with OCD, setting the stage for the analysis and exploration to follow.
01:30 - 03:00: Data Analysis Overview The chapter 'Data Analysis Overview' begins by introducing a data set that contains various columns such as patient ID, age, gender, ethnicity, marital status, and education, among others. The narrator expresses the intention to preview the data set before proceeding to analyze it using MySQL.
03:00 - 04:30: SQL Analysis Begins In this chapter titled 'SQL Analysis Begins', the focus is on answering five specific questions related to data analysis using SQL. The discussion involves calculating the count of females versus males who have Obsessive Compulsive Disorder (OCD) and determining the average obsession score based on gender. Furthermore, the analysis extends to understanding these metrics across different ethnicities by evaluating the count and average obsession score by ethnicity. Additionally, there's exploration into understanding the number of people diagnosed with OCD month over month. The chapter sets the stage for an interactive session where questions are read and addressed consecutively, suggesting a structured and iterative approach to tackling these data questions.
04:30 - 06:30: Calculating Gender Statistics The chapter titled 'Calculating Gender Statistics' features Stacy Samo, a data analyst. She expresses her passion for sharing her expertise and discusses her experience with various data tools such as SQL, PowerBI, Excel, and Python. Stacy is also learning to use Luca for an ongoing school project as part of her intention to diversify her skill set.
06:30 - 09:00: Calculating Ethnicity Statistics The chapter titled 'Calculating Ethnicity Statistics' appears to be a personal reflection or introduction by the speaker. The speaker invites listeners to subscribe and join them on their journey of learning and development in the field of data. They plan to start creating sit-down videos in 2024 to share their personal experiences, including how they entered the data field, what strategies were effective or ineffective for them, and how they managed to master various tools. The speaker's emphasis is on growth and skill development.
09:00 - 12:00: Date Formatting and Monthly Analysis The chapter begins with an introduction to the tools and topics that will be covered, including SQL, Power BI, and Excel. The instructor mentions creating a dashboard in both Power BI and Excel. The focus initially is on using SQL, followed by a transition to Power BI, and finally to Excel. There is a mention of analyzing data related to gender differences in OCD prevalence and obsession scores. The instructor considers whether to keep the analysis simple or more complex.
12:00 - 15:00: Obsession and Compulsion Analysis The chapter 'Obsession and Compulsion Analysis' delves into a detailed examination of behaviors characterized by obsession and compulsion. Starting with complex concepts, the chapter gradually simplifies to make the analysis more comprehensible. The initial steps involve selecting specific data attributes, such as gender, to conduct a patient analysis. This structured approach indicates a systematic methodology for dissecting the underlying obsessive and compulsive patterns among the subjects under study.
15:00 - 33:00: Power BI Dashboard Creation This chapter discusses the process of creating a Power BI dashboard with a focus on handling patient data. The key points include counting patient IDs by gender and calculating the average obsession score by gender. The unique identifier, patient ID, is crucial in differentiating and categorizing the data. Various strategies and considerations are explored to ensure accurate data representation based on these parameters.
33:00 - 54:00: Excel Dashboard Creation In this chapter titled 'Excel Dashboard Creation,' the discussion focuses on creating dashboards using Excel. The content suggests a focus on understanding and utilizing aggregate functions, with a specific interest in calculating average scores. It's implied that the audience may not be very familiar with these functions, suggesting an introductory or explanatory approach to the material. This could lead to further exploration of using Excel for data analysis and visualization, underlining the practical utility of Excel in building dashboards that effectively communicate key data insights.
54:00 - 60:00: Closing Remarks This chapter provides closing remarks on the topic discussed in the series. It mentions a tutorial series covering aggregate functions, SQL functionalities like 'group by', 'order by', and more, from beginner to advanced levels. The chapter encourages viewers to explore these resources for deeper understanding, particularly focusing on grouping data by gender.

SQL+Power BI+Excel Data Analysis Portfolio Project| For Beginners| Health Analytics Transcription

00:00 - 00:30 hello and welcome to today's video welcome back to my Channel or welcome to my channel if this is your first time here so today we're going to be doing a SQL power and Exel project as you've seen in the title down below so it's a health analytics uh data set that we're going to be tackling and it's a data set that contains the details of a number of people who are diagonalized with OC D so
00:30 - 01:00 yeah so this is the data set some of the columns include patient ID which is a unique identifier age gender ethnicity marriage Ro status education and so many others yeah I just wanted to get a i us to get a preview of the data set here but now let's go let's jump onto my SQL and get on with the analysis okay okay so we're going to be
01:00 - 01:30 answering five questions so what's the count of female versus males that have OCD and the average Obsession score by gender then we also get the count and average Obsession score by ethnicity so by ethnicity then we want to find the number of people di number of people diagonised month over month yeah and so on and so forth so I'll just read the question as we go but yeah before we continue if you're new here my
01:30 - 02:00 name is Stacy Samo and yeah I'm a data analyst I love sharing my knowledge and the skills that I've acquired so far hence why I create tutorials and projects around data so far I've majorly been working with of course SQL powerbi Tao Exel python yeah but with time I'll also diversify and get into tools like Luca cuz I'm doing a school project that requires luuka so I'm learning it I
02:00 - 02:30 might as well share my skills with you guys but yeah if got something you're into and you'd like to learn and grow with me feel free to you know subscribe and stay on with me in this journey and yeah 2024 I plan to start doing sit down videos just telling you guys how I got into Data what worked for me what didn't work for me how I am managing to up skill to grow how did I eventually master all these tools something you're interested in stay tuned guys otherwise
02:30 - 03:00 let's get into the video yeah so we're going to start with SQL then we jump onto powerbi then we'll create the same dashboard that you create on powerbi on Excel so SQL powerbi Exel okay so the count of female versus male that have OCD and the average Obsession score by gender H I'm just wondering if I should keep this very simple or if I should
03:00 - 03:30 show you guys something okay let me start with some something that's not very simple then we'll get into something a bit more simple so we want to count and per okay okay so the first thing we're going to do is to just uh select the gender column here and we want to count patient
03:30 - 04:00 ID so patient ID cuz that's the unique identifier so we count all the patient IDs and the ones that are attached to gender female will be counted differently the ones that attached to gender male will be counted differently yeah uh so yeah we'll name this patient count here and we are also interested in the average Obsession score by gender so we
04:00 - 04:30 do average uh I think know that's Obsession type we're interested in obsessions score as average okay so in case most of the things that I'm doing are very new to you like you you're not very familiar with aggregate functions you're not very
04:30 - 05:00 familiar with group buy feel free to go and check out my various tutorials on aggregate functions on group buy order buy having ITC I have a whole SQL series from beginner level all the way to Advan SQ feel free to go check it out so grouping by one so we want to group it by gender so after we count we want to group it by gender yeah then we want to
05:00 - 05:30 order by two so we want to order by patient count so order by default order by by default is in ascending order that'ss from the least to the most so let's run this let's run this and see what will happen so there you go so we have a total of 7 47 females and 553 females so as you can see the average score is in
05:30 - 06:00 four decimal places we can make this a bit neater by rounding it off to two decimal places so let's run that there you go so it's a bit later so we want to get the percent of so the percent of male versus female yeah who were diagnosed with OCD that's
06:00 - 06:30 basically from everyone on the data set what percentage is male what percentage is female so the first thing I'm going to do is I'm just going to download this data set here because we're going to use this data set uh as is so let me just I need to delete something so okay so I'm going to save this data set as is yeah because
06:30 - 07:00 uh okay save we don't actually need to actually calculate the percentages um for the visual but I'm just going to show you how to calculate percentages but for the visual this is the data that we're going to use okay so H we're going to use something called CTE yeah with data as so if you're not familiar with CTE again feel free to go
07:00 - 07:30 and check out my video on CTE so this are just temporary tables yeah if you want to learn more about it get more specific uh a few more examples Beyond this one feel free to go check feel free to go check out my tutorial yeah so we're creating a temporary table and we're naming it data and this table is going to contain it's going to store this our output here so if we do select
07:30 - 08:00 all from data and we run this as you can see that's what appears here so if we want to get the percent we can use um what is it called aggregate function inside a case statement yeah so we're going to use uh case statements plus aggregate function yeah so the first first thing we're going to do is to just
08:00 - 08:30 um case M actually start with because we are summing it yeah so sum case when gender do okay so case when gender
08:30 - 09:00 CL female then patient count so I advise you to avoid typing CU you know sometimes you could make like a
09:00 - 09:30 very small uh typo sometimes you could make a very small typo and debugging can be very annoying though okay yeah debugging can be very annoying so copy pasting is safer than typing from from scratch else zero yeah as so what this is Count Emil yeah let's run the and see what
09:30 - 10:00 [Music] happens in my syntax the case statement is not complete and okay there you go so case so we are summing the whole thing so case when gender is in is female then patient count else zero so as you can if you
10:00 - 10:30 [Music] remember if you remember our female count to 787 yeah so let's do the same but for males count mail ma yeah so let's run this and see what happens so that we have count of female count of male yeah so if you want
10:30 - 11:00 to get the percent count we have to uh take number of female divide by number of female plus male time 100 yeah so o I didn't name this or did I delete it by mistake okay so let's take email
11:00 - 11:30 divide by female so I'm going to put this down here last me okay uh as perent female here let's run that and see what happens let me just bring this a bit
11:30 - 12:00 [Music] closer so we can all kind of see what is going on so I can get rid this for now so we have a good view I'll bring it back when I need it okay so let's run that and see what happens and you can also save so there that's zero .49 yeah so notice it's not a percentage
12:00 - 12:30 yet so times 100 okay so to clean it up we can use the round function uh this time you also close the 100 so round to this to to two decimal places that's 49.8 0% yeah so this is as female so we can
12:30 - 13:00 put a comma there so let me just make this a bit neat okay so yeah we can do this so we can just copy paste cuz when you want to calculate for male it's the exact same thing the only thing is now here the numerator is male then of course we name this to percentage mail and yeah let's run that
13:00 - 13:30 so there you go so yeah if maybe you work [Music] on uh a SQL editor notebook and you're able to like do your visuals on the same place yeah so you would't do further calculations on like you would be moving to P you be moving to T so there are platforms like that yeah so you need to
13:30 - 14:00 calculate some of these things like in SQL and then visualize them yeah so that's why I was like you know what yes powerbi will automatically assign the percentages when we move to powerbi for visualization but I thought it would also be nice to just show you guys how to calculate this percentage or even sometimes you just need to extract a report and that report needs to have a column with percentages how you do it on MySQL this is how you do it on postrace I think it's easier or maybe it's cuz I used post SQL every other day so I'm
14:00 - 14:30 biased but yeah it has a function you just be like some uh some patient ID I mean count patient ID filter where gender is equals to female so it's definitely a less heavy syntax compared to this and yes guys uh that's it for the first question let's go to question number two so just going to comment this
14:30 - 15:00 out I'm going to comment this out and we go to the second question here count H count and average Obsession score by ethnicities that have OCD oh my God very bad English it's because this is not the original question but yeah we just want the count uh count the number of ethnic so like for from everyone who has OCD yeah
15:00 - 15:30 what's the distribution per ethnicity yeah yeah I need to improve this question I can't uh so that's pound of patients by
15:30 - 16:00 and there respective aage Obsession [Music] score
16:00 - 16:30 okay so we can do select uh ethnicity and count patient ID the unique identifier as patient count then you want the
16:30 - 17:00 average Obsession score as we'll just call it Obsession [Music] score room let's get our data set data sets data sets
17:00 - 17:30 this makes it look neater remember to say and we are grouping by one so that's ethnicity and we want to order by two so that's uh patient count so from the least to the most we can select that and there you go so we have the various ethnicity patient count Obsession score and yeah we can save our data set so this is
17:30 - 18:00 what city okay so number of people diagnosed withs OCD month or month I'm not even sure um this data set is from what year to what year but yeah let's see let's see the results we'll get and
18:00 - 18:30 then it will advise on any filters that we're going to add yes the first thing we need to do is that's the OCD diagnosis date is in text format here so we need to change this into dat type so the Syntax for that is Alter table so what's our table name
18:30 - 19:00 let's pick it from here so that's our table name and then we want to modify our specific column so that's and we want to modify it to date and yeah let's run that so it will take a while to run let's just give it a minute there you go
19:00 - 19:30 so just come here refresh all and then as you can see it's now in date format so we can comment that out and then start our analysis always remember to save and yeah so what do we want month of month yeah so we're going to be using a function called date for month H what happened why is it not responding
19:30 - 20:00 okay it's back so we're going to be using a function called uh date format yeah and we're going to have our OCD diagnosis date in there and then we want year month and then then the day will be replaced by
20:00 - 20:30 01 so all this needs to be in quotation marks and then so yeah it's basically just replicating like oh why does my date format have an error as month ah cuz I didn't do
20:30 - 21:00 select I was so to get into the question so select date format CH our data set okay so we want uh the diagnosis month over month so it's still going to be count patient ID because we want to count the number of people and attach them to their
21:00 - 21:30 respective month H I need to show you guys something so patient count so yeah let's finish this then I'll show you so Group by one and we're going to order by one also yeah let's just run this and see what happens and then I'll just take time to explain something to you so yeah this is from
21:30 - 22:00 2013 all the way to 20 20 November so notice that it's in order so uh what I want to do now is let me just put this down here then I comment this out so we have month then let's just take the diagnosis date uh but we need our
22:00 - 22:30 select and comment that let me run that so yeah so what this is basically doing is as you can see here this is 2016 July yeah but the date format ignores the date like the day yeah the day it just focuses on the month and the year cuz if you want to visualize this day by day by day imagine how that
22:30 - 23:00 visual would look so in this situation if there were like I'm looking for two months so like yeah this day uh they're not the same they're not the same uh let me do order by one let's order by one and then let's run that okay so yeah so this is this is perfect so the number of people who were
23:00 - 23:30 diagnosed um yeah so the number of people who are diagnosed on the 17 so let's say this is one person another one person on the 17th of Feb 20 2013 on the 17th of Fe 2013 so what's going to happen is uh it w be one one it will be aggregated so it's two people diagnosed in the month of November 2013 so it's aggregating this two into one
23:30 - 24:00 actually all these people who are diagnosed on on in February 2013 regardless of the day they'll be aggregated into one so yeah this is basically uh what this function is doing so I've just taken time to explain that a bit more because I'm not created tutorials on how to manipulate uh date
24:00 - 24:30 ah okay yeah this is all supposed to be here for the purposes of this so yeah so I've just taken time to explain that because I've already created tutorials to just explain how to manipulate dat functions in SQL so I thought it's fair for me to just take time to explain that and yeah as I said earlier if uh you'd like to learn more you want to go along this journey with me as I share my the skills the knowledge that I've gained so far yeah feel free to yeah subscribe yeah for sure a tutorial on how to manipulate date functions in in my SQL
24:30 - 25:00 specifically is going to come very soon yeah so if you're interested feel free to subscribe if you've liked this video so far yeah let me know down in the comments also just like that's another way of letting me know that this uh videos are useful for you and yeah if there's a specific piece of content you want me to create around anything data analytics and data science actually
25:00 - 25:30 please feel free to let me know down in the comments below otherwise guys uh that's it for the third question so we have two more questions then we jump on to our visualization tool oh let me see my water oops sorry what is the most common Obsession type so we want the count and its respective average Obsession score so the last two are very quick so we're
25:30 - 26:00 going to select um Obsession score no we want the obsession type yeah and we want to count patient ID as patients CH
26:00 - 26:30 count or then we want the average um Obsession score as wrong yeah so here it stays the same so that's our data set and we want to group by one which is
26:30 - 27:00 the obsession type and we want to order by two which is the patient count oh I didn't see this one so this [Music] is diagnosis
27:00 - 27:30 okay so yeah we can run this so that was three did I save two yeah I did okay so yeah let's run that and there you go so various Obsession types this is the count this is the obsession score so yeah the next thing we're just going to do is just clean the a bit
27:30 - 28:00 around to two decimal places okay session type okay and we go to the last question so the last question is very similar to the first question so I'm going to copy paste the whole code yeah then here instead of obsession type you're going
28:00 - 28:30 to have compulsion type yeah it is so we still want to group it by one we still want to order it by the second one so let's run that uh let's save what we've done so far and oh let's save this so it's compulsion compile compulsion
28:30 - 29:00 type okay so yeah let's save that and yeah guys we done with the SQL bit of this tutorial so yeah please note I'm going to be sharing the link to the data set down below in the comments not comment section in the description and I'll also share the link with the code in the description and yeah uh in my previous in one of my previous project project someone asked me to also be sharing this mini data sets and yeah so
29:00 - 29:30 I'm Al I'll create a drive and just put them all there with a with the what is it called with the uh project title and date it went up so just in case I do another Health analytics project you'll be able to track it but I'll also just put everything in the drive by year and quarters yeah so it will be like 2023 q1 Q2 Q3 Q4 but of course I'm going to
29:30 - 30:00 start with Q4 Q3 Q4 I don't know did I do when I create this YouTube channel I think it's in Q3 so yeah so yeah that's how my key to using my my drive but I'm not sure if I'm going to do that now so it's a bit late and I'm getting a bit exhausted so I will link the data set code but I'll link the many dat sets that we created together tomorrow for me tomorrow my time
30:00 - 30:30 tomorrow yes so just know by like 24 hours after this project goes up you should have the drive if you view this 24 hours before sorry but I might change my mind and just decid to put it now but yeah let's go into the visuals
30:30 - 31:00 okay sorry about that clearly the exhaustion is kicking in but yeah I think we're going to go on for like about maybe 30 more minutes slightly more slightly less but yeah so the first thing we going to do is to just load all the mini data sets that we created so it
31:00 - 31:30 doesn't take so long to load so we'll just do this together so this is the compulsion types yeah so let's give it a minute and it will appear here you can see it there then the next thing that we want to add is diagnosis month over month okay so we're just going to keep that data set as long as it was cuz I remember
31:30 - 32:00 we're supposed to look at how long it is and decide on a filter but it's okay like it's okay if you have any questions to like if you want to know how to limit it if you feel like it's messy if you want to know how to rename them yeah if you just have any further questions about anything we've done on
32:00 - 32:30 this project please feel free to put them down in the comments below I get back to almost all my comments by the way yeah so I'll get back to you CU it just hit me that we didn't do that but yeah we already here so let's continue so gender and then we're going to last one that we're going to be uploading is the obsession type [Music]
32:30 - 33:00 okay so we have 1 2 3 four five data sets so I want to start by just firstly labeling our dashboard [Music] yeah so so this is the
33:00 - 33:30 health analytics oh dash dash mod okay so let's highlight that bold It Center Line it let's see what size pretty decent here so I want us to start with the line chart yeah and I will let's see
33:30 - 34:00 how month and yeah okay makes sense so this would be so nice to have like a a slicer that can help us back plan but anyway so we're just doing this is like analysis at a point in time so we just
34:00 - 34:30 want to know everything about this D set so as you can see this is towards the end of the year what happened like why did the diagnosis go up towards the end of 2018 it was not the same towards the end of 2014 towards the end of 2015 actually even towards the end of 2014 spikes uh end of
34:30 - 35:00 [Music] 2017 almost similar I don't know like interesting like I'm curious like I'd want to know why this Spike so if you want to change something about the title so you can just come to title yeah so you could the only thing I want to do is honestly Center Line it and bold it no I don't want to bold it
35:00 - 35:30 here and there there you go we have our first visual so the next thing I want us to do is to just look at uh we can use a donut chart or a pie chart let's use a donut chart I'm thinking we on Blue so I just want us to use a donut chart to get a picture of the gender so in this this whole time period yeah was it more men or women oh
35:30 - 36:00 this would be so cool like if we did month over month then we Group by male versus female then we can see guys should we go back to SQL and do that let's go back you guys let's go back okay so firstly oh or should I just stick to my agenda you guys but I think it would be so cool anyway let's let's just finish let's stick to what I plan
36:00 - 36:30 to do this so that idea that has come into my head I will do it in the next project you guys can I just say something like my first SQL powerbi project did so well I didn't expect it it's my highest watched video right now uh last I checked which was some time last week it was like at [Music] 10.2k now I don't know I know it's at 10 something K it's not 11k but I don't know if it's like at 10.5 I don't know
36:30 - 37:00 and you guys thank you and let me tell you something like I learned data like my first bi tool business intelligence tool was tblo and I'm very comfortable with like SQL I'm very comfortable with python hence I even started with creating tutorials on those two and when I wanted to create a SQL powerbi cuz I was like if I just do an SQL project um please feel free to skip over this if
37:00 - 37:30 you're just interested in the projects and not my stories and I really need to start these sit Downs cuz clearly I have a lot to say but yeah when I first started that project like I was so scared you guys cuz I was like this is the first time I'm working on Tao so I watched I mean p I watched a quick tutorial shout out to her data project YouTube channel her tutorial is what guided her one of our projects is what guided me so to also go and check her out she creates very dope content very
37:30 - 38:00 nice projects and other tutorials yeah and when I create that project I was so scared and I felt like it's so basic I felt like I can do more than I did but the way you people received it 10K plus views and it's been just two months wow thank you and I'm just seeing and feeling how comfortable I'm getting with like creating content and doing like power powerbi table visualizations and I'm
38:00 - 38:30 just like like first of all thank you for being here and secondly I see why they tell you to just start like if you want to start doing content just that don't wait to be perfect cuz I was not perfect and that video still did so well and now I'm in my second video and I'm more comfortable I'm more confident and I'm just like I want to try this I want to try this but no no no no no and tip about being a data analyst stick to the scope cu the
38:30 - 39:00 trials things you want to investigate will never end so you stick to the scope and you can put whatever else you want to investigate in another a second project with a different scope but yeah I just hope you get my point like just start like the I'm getting very confident and comfortable in powerbi oh my God I can't believe this but yeah let's get back to it so what do we want to visualize here gender
39:00 - 39:30 gender yeah we can do gender then we make it shorter so gender will be in the Legends and then do we let's do count so we're going to stick to count in all the visuals there you go so the next thing I'm going to take is ethnicity and I want to use oops I undo yeah so the next thing I want is
39:30 - 40:00 ethnicity and I want to use a tree map so ethnicity patient count okay they're almost even distributed and yeah so the next the
40:00 - 40:30 last two things we're going to be doing I want to put them in should I put them in horizontal charts I'm not sure if I'm feeling this but it's communicating what needs to be communicated I'm just not sure if I'm feeling it I'm just wondering if um I can use a horizontal B here I think that's a bit better like you can quickly see that Caucasian well had the highest number of count like in terms of people who are diagnosed with OCD Caucasian were the
40:30 - 41:00 highest followed by Hispanic Asian then African but you know you should always uh okay so we come to General I just like Center aligning my titles and there you go so you can quickly see that so the last thing we going to do and I want to use uh B
41:00 - 41:30 chart is okay so I want you the BART is we still going to use count there you go so washing counting checking praying ordering so washing is the highest type of
41:30 - 42:00 compulsion and yeah this can be stretched a bit you can stretch it a bit because this ones I want them to be almost equal okay and we are going to select another bar chart and now we are going to Obsession types so Obsession type is our label and we want patient count ooh what if I change it to this and then I
42:00 - 42:30 have it's tucked okay so we come to
42:30 - 43:00 hm so the uh average Obsession
43:00 - 43:30 score is very like it's very close to each other so as you can see let me just click out somewhere the high the most occurring Obsession patient I need to I need to change the I need to change the
43:30 - 44:00 title so it's Obsession score against patient count obession score
44:00 - 44:30 I'm still not convinced if that's the best visual I might actually just prer
44:30 - 45:00 so you can clearly see who had the highest Obsession score and this is their respective average uh what it called average Obsession score so the most occurring Obsession type oh the title is wrong so it obs session type
45:00 - 45:30 against patient count Obsession count against average Obsession score aage obsession okay that's okay
45:30 - 46:00 now okay yeah okay and guys that's it for our powerbi dashboard so let me just save this and yeah we're going to jump on to excel next and uh create a dashboard that is communicating the same thing we might not necessarily use the exact same
46:00 - 46:30 visual but it will be communicating the same thing yeah so yeah let me just switch to okay so what I've basically done here is I've just copy pasted all the mini data sets that we created so I'm just going to bold them out so we know who is who so I just B the title so I
46:30 - 47:00 also don't get confused yeah and we're going to try and replicate the same dashboard that we did on power ba as you can see this is our month month of our month leure set this is a number of people uh grouped by the various compulsion types and their resp respective obsessive score the various ethnicity how many people by ethnicity
47:00 - 47:30 were diagnosed with OCD with the respective uh obsess obsessive score same thing here so this are just Obsession type number of people per Obsession type and their respective obsessive uh what is it called obsessive score so we're going to yeah so I guess I forgot to do this on
47:30 - 48:00 tickle so we'll just do it here okay so the first thing I like to do is to just um remove oh my gosh let me just minimize this a bit cuz yeah this is a bit better so the first thing I like to do is to just come to view and View and I want to remove the grid lines yeah cuz I want this to be clean yeah so we can come back to
48:00 - 48:30 home and the first thing uh the first one we're going to have here is uh D loses month over month here then we'll also just be here like Health analytics dashboard yeah so let me just confirm but I spelled that
48:30 - 49:00 [Music] correctly so Health analytics dashboard oh what's happening what's happening my people okay okay so Health analytics dashboard we have
49:00 - 49:30 diagnosis uh month over month and the next thing we're going to put there is so have visualizing this I want it to look kind of like the table one that we just did so we have the line graph at the top we have the line graph at the top and then the line at the
49:30 - 50:00 top who will we do next okay yeah let's no but I want to place them so okay so this okay we'll see how long it goes then we'll edit it then here we can have composion type so you can have composion type there then you can have ethnicity so feel free to give them better names honestly and then Obsession
50:00 - 50:30 can come somewhere here and then gender gender can come somewhere there watch I feel free to give them better names than I have and then we can just insert yeah yeah I'm trying to push that away but I'll push it more once we are
50:30 - 51:00 done okay so yeah let's start with this here so we're just going to highlight here and then we'll come to insert and then recomended charts but I already know what I
51:00 - 51:30 want so I want a line chart yeah okay so oh this will mess with our [Music] data
51:30 - 52:00 okay this will mess with our data so we don't want to CH title you don't want for
52:00 - 52:30 G I don't like how this looks but it's even more weird when you remove it so let's just keep it here and then the good thing is they like this so I want to do X and probably push them somewhere here and then I'm going to bring this here I want to stretch
52:30 - 53:00 it but I want it to come up going to cut this again and bring you here okay I really hate this thing it's so ugly but yeah so m you can bold then you can highlight this and
53:00 - 53:30 Mar and then we can color we can choose a [Music] color let's go with dark blue so I want the writing to be white so diagnos is month over month a bit ugly but you get it uh so I want it white
53:30 - 54:00 blue [Music] smudge bold it's already Center Line okay yeah let's quickly uh create our visual for compulsion compul compulsion am I saying typ ethnicity gender and [Music]
54:00 - 54:30 Obsession okay so compulsion compulsion I sound so weird when I say it I don't know okay so same thing insert let's see who are the recommended what did I use the the time so we can use a line chart here it's not going to become that tiny oh [Music]
54:30 - 55:00 okay so we don't want the title we don't want the grid lines that's all sorry undo okay so we're just going to make this a bit more tiny okay and it's actually working
55:00 - 55:30 aity as Tiny as possible
55:30 - 56:00 we don't want the title we don't want the grid lines because we already have the title here and the grid lines here yay so Obsession type is the next one I'll either use a horizontal bar or a vertical bar let me use a vertical bar as usual we don't want the title we
56:00 - 56:30 don't want the grd lines and we want it as Tiny as possible so who I'm related is the most common and then the last but not least insert so here I'm going to use a pie chart and yeah as
56:30 - 57:00 usual we don't the chart title but we want to keep the legend of course so male versus female in our data [Music] set okay so what I want to do is just start oh this one is getting bigger I don't want it to get bigger
57:00 - 57:30 and guys shrink this further I wonder what will happen I hide this oops we can't hide
57:30 - 58:00 it okay but yeah this is this is actually not too bad but yeah there is our visual there is our dashboard our health analytics dashboard so we have our oh one thing that I've not done okay so you want this white bold Center
58:00 - 58:30 Line H that looks so nice people
58:30 - 59:00 okay this is just guys we are done by
59:00 - 59:30 the way we are done this is just me trying to make the dashboard a bit more aligned a bit more organized but this is like this is done we're just doing something very basic and guys uh
59:30 - 60:00 as I mentioned earlier like this YouTube channel like creating content for you guys is really challenging me in a good way and I cannot wait to just come back and see how much we have all grown like I can't wait to come back and create for you guys some some amazing
60:00 - 60:30 tutorials what this do too just finish this it's I selected two I supposed to select one till here
60:30 - 61:00 so much blue point so this guy needs to go up a bit I can come back here we need to make you [Music] smaller
61:00 - 61:30 okay yeah so let me just give the closing address then I can keep on trying to make my dashboard pretty control save but yeah guys that's it that's it for this video thank you for staying till the end if you found this video insightful if you liked it please feel free to like let me know down in the comment below like what did you like about it what did you not like what more would you like what other type of projects content would you like me to see would you like to see from me yeah
61:30 - 62:00 I'm also telling to feel like we are yeah we are we are what is it called we're out growing the very basic dashboards I like uh the next thing I want to just sit down and practice is like people tables I want to come back and create for you guys a dynamic dashboard a more Dynamic dashboard than Exel and I'm more more Dynamic dashboard on powerbi but as you can see uh what forgive the ugliness what you can do on
62:00 - 62:30 powerbi can be done on uh on Exel yeah so yeah the only thing is yeah powerbi is a bit more visually pleasing compared to Exel it's easier to use you don't have to worry so much about positioning CU when you click the grid like the box that encloses your visual appears when you click out of it it disappears so you don't really have to
62:30 - 63:00 worry about alignments you can even just see like this visuals are more visually pleasing yeah so that's yeah like is just more convenient but if Exel is all you have to work with or what you know you can start by perfecting on your visualization and that making skills there before you jump on to powerbi Etc other otherwise guys that's it for me um so one thing is I'm going to do this same project but on python so
63:00 - 63:30 if that's something you're interested in yeah stay tuned and that means subscribe put on the notification Bell that when I do the python Health analytics project you are notified otherwise guys that's it for me thank you for staying to the end and yeah thanks thanks thanks for we at 717 Subs now and decent number of views across my projects and
63:30 - 64:00 tutorials I really appreciate it I really appreciate it motivation to keep creating tutorials projects for you guys and I cannot wait to start doing sit down videos and just telling you guys about my journey you know how I looked for this jobs creating my CVS building my portfolio projects I canot wait guys me I can't talk talk till tomorrow so you know what and my sister always tells me in Swahili ah if I told you I'm going to do
64:00 - 64:30 something today let's do it tomorrow never reaches but reach and I'll still say tomorrow so me I can talk till tomorrow let's close it here but yeah I I clearly have the energy for sit down videos but guys bye once again if you like this feel free to like let me know in the comments down below let me know what other types of videos you'd like to see yes subscribe and see you on the next one bye