Hello, my people! Today, we go talk about wisdom. No be the kind wey you dey use to dodge traffic for Lagos o, but the kind wey dey make you stand gidigba for God.

You see, being wise like a serpent no mean say you go dey sly or dey do corner-corner things. E mean say you go hold tight to wetin God talk, no shaking, no matter the gbege wey dey happen around you.

Na so the Hebrew boys, Shadrach, Meshach, and Abednego, dem talk say dem no go bow for any golden statue, even if fire dey wait for them. Dem trust God pass how fish trust water. That na the kain wisdom we dey talk about!

Some people don think say serpent na bad news because na im the devil use deceive Eve. But no be so! To be wise as a serpent mean say you sabi the game well-well, you no go let anything shake your faith.

So, my brothers and sisters, make we choose to stand with God. No let the small-small wahala of life make you fold like say na gala wrapper. Stand firm, be like soldier for Christ. Even if the whole world dey go left, you, my friend, go dey go right, follow the way wey God don show you.

And remember, no matter the palava, no matter the katakata, God dey your side. You be soldier for the Most High, no be time to slack. Choose to be wise like the serpent, choose to stand with God.

Make we laugh small, but carry this message for mind: no let your faith fold like say na origami. Stand strong, my people, stand strong.

BRATISLAVA:




That allergic to Python?

All I can say is thank God for Power BI, SQL and other analytical tools available.

BRATISLAVA:

Very necessary.

We love the ease of explanation and colors.

If you were using Python, I believe you wouldn't need to go back and do a dashboard, as you could have them inline. Would be interested to see you pull off the same SQL stunts strictly with Python, no SQL imports

Lol. Thou shall not tempt me. To pull that off I would have to do a collabo with chat gpt. Python not my go to for analysis, unless necessary. I'll pass on this your challenge

BRATISLAVA:


Nice stuff.

But why don't you like dashboards?

Lol. I like dashboards. For every dataset I analyse with MySQL, I always complement it with a dashboard or visualize using Python. Always. But not this particular dataset. Don't think a dashboard is necessary.

Final Analysis

After examining the dataset provided, I have gained insights into whether the use of the gamified app has positively impacted the students' experience and academic performance.

From the results above you can see that:

* 47.83% of the students demonstrated an improvement in their grades after using the gamified app.

*27.54% of students also demonstrated an improvement in their grade even without utilizing the gamified app.

*15.94% of students experienced a decline in their grade using the gamified app.

*8.70% of students also experience a decline in their grade without making use of the gamified app.


Based on the analysis, it appears that the gamified app had a positive impact on the performance of the students who used it, as indicated by the 47.83% improvement in their grades. However, it is also important to note that some students who used the app experienced a decline in performance, which suggests that the app may not be effective for everyone.

To fully evaluate the impact of the gamified app on student performance, it would be helpful to conduct further analysis and consider other factors that could have influenced the results. For example, it would be useful to compare the performance of students who used the app with those who did not, taking into account any differences in their prior knowledge of statistics, motivation, and study habits. Additionally, it may be beneficial to collect qualitative data from students to understand their experiences with the app and how it affected their learning.



Thank You For Your Time. If I find myself in that analytical mood I might analyze this data again but with a dashboard this time.

Thank You.

Peace

Two more to go

g) The 7th image displays the number of students who showed no improvement in their grades without the gamified app.

h) The final image just complements the above image by showing us the students that showed no improvement in their grades without the gamified app.

e) The 5th image depicts the number of students who did not show any improvement in their grade despite using the gamified app.

f) The 6th image shows data of the students who did not improve in their grades despite using the gamified app.

c) The 3rd image displays the number of students who experienced better performance without making use of the gamified app.

d) The 4th image provides further insights into the number of students who improved their performance without using the gamified app.

Now let's delve into the analysis

a) The first image displays the number of students who experienced better performance after utilizing the gamified app.

b) The second image complements the first one by providing additional data on the students who saw improvements in their performance after using the gamified app. Not enough space to show all 33.

The image below provides a visual representation of the total number of students in the dataset. It shows the distribution of the students who used the gamified app and those who did not, as well as the total number of students who participated in the study. This information can give us a better understanding of the sample size and help us to make more accurate conclusions about the impact of the gamified app on student performance in statistics.

Have you ever wondered if gamification can improve students' grades in statistics? In this analysis, we'll take a closer look at a dataset from a gamified learning application with interactive flashcards and badges. The dataset includes data on various variables, such as practice exam grades, final exam grades, and user engagement with the platform. Using MySQL, I was able to analyze the data and uncovered some interesting insights.


Ready to learn about gamification? You're in the right place.


Gamification is the use of game design elements in non-game contexts to engage and motivate people to accomplish tasks or achieve goals. In education, gamification is often used to make learning more fun and interactive by adding game-like features such as points, badges, levels, and leaderboards to educational activities. The goal is to increase student engagement and motivation, which in turn can lead to improved learning outcomes.


So, let's dive in and see if gamification has made a significant impact on students' grades.


Below is a visual representation of the dataset, giving you an idea of how the information is structured.

Thank You For Staying With Me....

More real sample analysis coming and maybe more collaboration with ChatGPT and other AI tools.....

Peace.

FINALY...….

I asked ChatGPT to analyze the dataset and this is what this AI tool had to say about the dataset it generated.

Cool Stuff....

More Analysis......

Lets look at the artist with the most revenue and albums from this dataset (please remember its not real )….

a) The first pic shows the artist with most revenue running from 2014 - 2020. And as you can see wizkid is topping the table with most revenue made.

b) The second pics shows the artist the most number of albums running from 2014 - 2020. And you can also see wizkid has the most number of albums. Ladies and gentle I don't how true... so please remember its a fake dataset.

P.S I don't know why I keep shouting its FAKE. I don't wanna get into trouble o plz. Wizkid, Davido and Burna boy fans this numbers are not real oooo. THANK YOU.

NEXT...…

I copied this "FAKE ChatGPT" dataset to Microsoft Excel, changed some things and moved it to MySQL for further analysis. Here is what it looks like:

a) The first pics showed how I created a database and what the table looks like on MySQL

b) The second pics shows the distinct years in the dataset. This dataset runs from 2014 - 2020.

ChatGPT! ChatGPT!! ChatGPT!!!

What is ChatGPT?

ChatGPT is a large language model developed by OpenAI. It is trained on a diverse set of internet text, and can be fine-tuned for a variety of language tasks such as question answering, language translation, and text completion. It is based on the GPT (Generative Pre-trained Transformer) architecture, which uses deep learning to generate human-like text.


ChatGPT is in vogue right now and its crazy what this AI tool can do.

So, as a data analyst I decided to play around with this tool and see what I can do with it.


So, HERE WE GO...…..

First I wanted to see if this tool could generate a sample dataset. I asked ChatGPT to generate a sample dataset on Nigerian music revenue and this is what it showed.

a) The first pic shows the result from the question asked. It was able to generate a table with 5 columns and 20 rows just like I asked it to. cool

b) The second pics is just like the first but please look at the note below.... This is not a real dataset and should not be used for real analysis or decision making. IT IS NOT REAL. Thank you.

AND FINALLLY...…..

The Fourth(4) Table.

Like I said earlier I joined 3 tables instead of the 4 listed above.

WHY? coz this table really didn't fit for my analysis. It is an independent dataset . Joining it with the others will modify my analysis and affect my insights from the dataset.

BUT.

This is what the table looks like. And as you can this table shows the teams and the number of times they have been in the top four (4) i.e. reaching the semi-finals. It shows the number of times a country has won the cup (first-place), runner-up(second-place), third place and the fourth place. The asterisk on the year means the country hosted the world cup that year.


And That is all I have for you today.

Thank you for you time.

More sample Data Analysis Projects coming soon...…

Peace.

One More....

Lets look at the champions and also the runners-up of all world cup tournament.

a) The first pics shows the countries and the number of times the have won the the world cup. And you can see Brazil still holds that position as the country with the highest number of world cup.

b) The second pics shows the countries and the number of times they have been runners-up. And you can see countries like Argentina, Germany and the Netherlands have lost the final 3 times.

I am almost done

Now Lets look at the total attendance from each world cup and also the game or games with the highest number of attendance.

a) The first pics shows the total number of attendance from each world cup tournament. Not a single attendance from a game but total attendance from all games played in the tournament.

b) The second pics shows the list of game(s) with the highest attendance.

Now Lets look at the golden boot and the golden glove winners

a) The first pics shows the list of all golden boot winners and also number of goals scored by each player. I ordered the goals in a descending order i.e. from largest to smallest. And as you can see from the result "Just Fontaine" is the player with the highest number of goals in a single world cup tournament.

b) The second pics shows the list of golden glove winners and the number of clean sheet kept by this goal keepers. Looking at the query you would notice the "where golden_glove <> not awarded" this means that from 1930 - 1994 there was no golden glove award. The only started giving the golden glove award from 1998.

I am not done yet, just getting started.....

Lets look at the golden ball winners.....

a) The first pics shows the number of players that have won the golden ball more than once. And as you can see only one player has done that.. the GOAT himself Lionel Messi.
Note: the "not awarded" which appeared 11 times means that no golden ball was awarded in the first 11 world cup tournament (1930 - 1978).

b) The second pics shows the full list of players that have won the golden ball since the award began. (1982 - 2022)

Now let the analysis begin

I can't say for other data analyst but I love to analyze my data from field to field i.e. analyzing each column from the beginning to the end.

So, Here we go...…

a) The first pics shows the country that have hosted the world cup more than once.
b) The second pics shows the countries from pics (a) and the years they hosted the world cup.

Working on the tables separately can be stressful and time consuming, so I joined the first three table (since those are the tables I will be working with) using the INNER JOIN function. I also created a virtual table using "view" to help simplify the my analysis.
Please note: the table is way wider than this. You can't see it but all three tables were successfully joined. Trust me
This is what I got from joining all three tables.

The FIFA World Cup is an international soccer tournament held every four years. It features teams from around the world, who compete for the title of world champion. The World Cup has a rich history, dating back to the inaugural tournament in 1930. Over the years, the tournament has seen many memorable moments and legendary players.

So today I will be conducting an analysis of the FIFA World Cup from 1930 to 2022.

This Dataset was gotten from Kaggle.

This dataset came in four different files i.e. four different tables and as you can see from the pictures below it shows the four tables....
*fifa_world_cup_attendance
*fifa_world_cup_award
*fifa_world_cup_finals
*top_four_world_cup_teams

Now lets look at the dataset from each table:
a) The first pics shows the dataset for the fifa_world_cup_attendance
b) The second pics shows the dataset for the fifa_world_cup_award
c) The third pics shows the dataset for the fifa_world_cup_finals
d) The fourth pics shows the dataset for the top_four_world_cup_teams

For full summarization of this dataset Please check out the video I did.

This videos shows the step by step process and it is more detailed than the ones I shared here.

Check it out.

Thank You.



https://www.youtube.com/watch?v=GR73n4O09ec

I'm not done yet

Please note that the pics below does not show the total number of incidents, deaths and injuries running from 2014 - 2022. The analysis and summarization and the pics I have shared so far are just total values running from 2020 - 2022.



More Sample Projects Coming Your Way

Thank You For Your Time.

And Finally

The picture below show the total number of incidents, killings, and injuries from each day in the month of January. And as you can also see, it show the address from each incidents. For example January 1st 2020 shows a total of 9 incidents, 0 deaths and 6 injuries and it also shows the different address where each incidents occurred.

More Summarization

a) Like I said earlier this dataset is all about gun violence incidents in the past 8 years i.e. 2014 - 2022 but I filtered the data to show me the information from 2020 - 2022 as you can see below. That means each state and cities and have filtered to show the information from 2020 - 2022 alone.

b) The second pics shows the quarter and the months from each year. You can get the total number of incidents, deaths and injuries from each quarter and also the total number of incidents, deaths and injuries from each month.

Now Lets Summarize This Data Using Excel Pivot Table

a) The first pics show the summarization of the table. The table has been grouped based on each state in America and each states shows the total number of incidents, killing and injuries.

b) The second pics show the cites under each states. There are more than there cities in Illinois and the other states but I filter the data to show me the three major cities in each state with the highest number gun violence incidents. As you can see, Chicago, Rockford and Decatur are the major cities under Illinois with the highest number of gun violence incidents.

Hello Fam!! So good to be here again

Today I will be sharing with you a simple data analysis/summarization using Excel pivot table.

Pivot table is one of the most powerful tool in Excel. It helps with the calculation and summarization of data (especially numerical data), which in turn helps you see patterns and trends in your data.

I got this dataset from kaggle. This dataset is all about the Gun violence in America. This dataset shows the states, cities, number of killings and injuries obtained from Gun violence in the past 8 years.


Here is what the dataset looks like.....…

You can't see the full table but this table has 448,402 rows and 7 columns

Rahyberg:
I resumed

.

Rahyberg:


Sir, I have downloaded the video but couldn't get it using the MySQL workbench. He uses the cloud9 IDE...

I'm having issue with MySQL workbench anytime I want to open folders of SQL files I download from the internet.




I'll be glad if you can mentor me on this.

It's still the same thing. Every query used by the guy on cloud9 IDE woks on MySQL as well. You just have to follow the queries step by step. If you still don't get it, you can learn using MySQL command Line client. It has the same similar background features with the guys own. But I'd prefer you learn with MYSQL.

What Kind of Errors does it show?
Having you tried increasing the Open files limit.