An Business Intelligence Analyst with a strong desire to help organizations clearly state their needs and make meaningful changes through data-driven decisions.

HI, I'M Sebas Londono

Repositories of this project

With this project, I´ll go into the database of adventure works from Microsoft and dive into sales, customers, and product data.

#### Get the data

I get the latest data from Microsoft, then start working with SQL server management and uploaded the files to Power BI

#### Data that I'll use

I start the exploratory queries to find what type of data I have

```
-- Cleansed DIM_Date Table --
--Calendar
SELECT
[DateKey],
[FullDateAlternateKey] AS Date,
[EnglishDayNameOfWeek] AS Day,
[EnglishMonthName] AS Month,
Left([EnglishMonthName], 3) AS MonthShort,[MonthNumberOfYear] AS MonthNo,
[CalendarQuarter] AS Quarter,
[CalendarYear] AS Year
FROM
[AdventureWorksDW2019].[dbo].[DimDate]
WHERE
CalendarYear >= 2019
```

```
--- Customers
```

```
SELECT
c.customerkey AS CustomerKey,
c.firstname AS [First Name],
c.lastname AS [Last Name],
c.firstname + ' ' + lastname AS [Full Name],
CASE c.gender WHEN 'M' THEN 'Male' WHEN 'F' THEN 'Female' END AS Gender,
c.datefirstpurchase AS DateFirstPurchase,
g.city AS [Customer City] -- Joined in Customer City from Geography Table
FROM
[AdventureWorksDW2019].[dbo].[DimCustomer] as c
LEFT JOIN AdventureWorksDW2019.dbo.dimgeography AS g ON g.geographykey = c.geographykey
ORDER BY
CustomerKey ASC
```

```
-- Products
```

```
SELECT
p.[ProductKey],
p.[ProductAlternateKey] AS ProductItemCode,
p.[EnglishProductName] AS [Product Name],
ps.EnglishProductSubcategoryName AS [Sub Category],
pc.EnglishProductCategoryName AS [Product Category],
p.[Color] AS [Product Color],
p.[Size] AS [Product Size],
p.[ProductLine] AS [Product Line],
p.[ModelName] AS [Product Model Name],
p.[EnglishDescription] AS [Product Description],
ISNULL (p.Status, 'Outdated') AS [Product Status]
FROM
[AdventureWorksDW2019].[dbo].[DimProduct] as p
LEFT JOIN AdventureWorksDW2019.dbo.DimProductSubcategory AS ps ON ps.ProductSubcategoryKey = p.ProductSubcategoryKey
LEFT JOIN AdventureWorksDW2019.dbo.DimProductCategory AS pc ON ps.ProductCategoryKey = pc.ProductCategoryKey
order by
p.ProductKey asc
```

```
--Intertnet Sales
```

```
SELECT
[ProductKey],
[OrderDateKey],
[DueDateKey],
[ShipDateKey],
[CustomerKey],
[SalesOrderNumber],
[SalesAmount]
FROM
[AdventureWorksDW2019].[dbo].[FactInternetSales]
WHERE
LEFT (OrderDateKey, 4) >= YEAR(GETDATE()) -3
ORDER BY
OrderDateKey ASC
```

<iframe title="Report Section" width="800" height="486" src="https://app.powerbi.com/view?r=eyJrIjoiZDM4MmViM2ItMTM4MS00YmEzLWI4N2EtZjc3YmQ4YzEwNjM3IiwidCI6ImU5OTQwNzJiLTUyM2UtNGJmZS04NmUyLTQ0MmM1ZTEwYjI0NCIsImMiOjR9" frameborder="0" allowFullScreen="true"></iframe>

## INFERENCES AND CONCLUSION

*   The sales went up 2.715 M more than expected over the first 3 years
*   Around 96% of the sales were bikes
*   The month of June is when the sales sky up
*   The best customer is Jordan Turner with around 16000
*   The bike with more sales is the Mountain-200 Black, 42
*   The majority of sales are in the west of the states, Uk, german, France, and Australia

\##RECOMMENDATIONS

*Increase sales of accessories and clothing, since after a year of introducing these two segments it had a share of sales of around 5%


SALES FROM ADVENTURE WORKS DATABASE

In this project, the objective is to analyze from the dataset sample OF MICROSOFT FINANCIAL STATEMENTS 2019 - 2022

# DIGITAL MEDIA SPENDS DATA

## RESOURCES OF THE DATASETS USED IN THIS PROJECT

### <li><strong> (contains all information about the financial statements of Microsoft)</strong> -  <a href="https\://www\.macrotrends.net/stocks/charts/MSFT/microsoft/income-statement">Source</a><br>

##DATA MODELING

<iframe title="Finalcial analysis Microsoft" width="800" height="486" src="https://app.powerbi.com/view?r=eyJrIjoiYzFhOTdjMTYtMDlkNi00MzRkLWE1ZWYtZTI5ZWRlZDIwYTE1IiwidCI6ImU5OTQwNzJiLTUyM2UtNGJmZS04NmUyLTQ0MmM1ZTEwYjI0NCIsImMiOjR9" frameborder="0" allowFullScreen="true"></iframe>

##INFERENCES AND CONCLUSION

*   Revenue has been increasing in the last 4 years constantly, at an accelerated pace.

*   Gross profit has been increasing in the last 4 years but in the last 2 increased the same amount, pointing to an increased cost of goods.

*   Net profit has been increased, but in the last year decelerated the pace increased the amount

*   taxes have doubled since 2019, but the net profit hasn't, meaning as well an increase in the cost of goods and operative expenses.


MICROSOFT FINANCIAL STATEMENTS

In this project, the objective is to analyze from the dataset sample HR ANALYTICS

# HR ANALYTICS

## RESOURCES OF THE DATASETS USED IN THIS PROJECT

The given dataset was taken from the dataset in Kaggle Datasets.

### <li><strong> (contains all information about the digital media data)</strong> -  <a href="https\://www\.kaggle.com/datasets/vjchoudhary7/hr-analytics-case-study?select=general_data.csv">Source</a><br>

### <li> <strong>hr_analytics.csv-  </strong> -  <a href="https\://www\.kaggle.com/datasets/vjchoudhary7/hr-analytics-case-study/download?datasetVersionNumber=1">Download</a>

## DATA MODELING

<iframe title="Report Section" width="800" height="486" src="https://app.powerbi.com/view?r=eyJrIjoiZjJhMDNlNGMtNmZlOS00ZTVlLWI4MGMtYThjNzU4YWQ1OTA4IiwidCI6ImU5OTQwNzJiLTUyM2UtNGJmZS04NmUyLTQ0MmM1ZTEwYjI0NCIsImMiOjR9" frameborder="0" allowFullScreen="true"></iframe>

## INFERENCES AND CONCLUSION

*   The company has 4410 employees and they cost around 287 M per month, they range in age from 18 to 60 years old, and with a monthly payment range from 10,000 to 200,000 dollars.

*   60% of the employees are men, 85% of employees have a performance rating of 3 out of 4, and 15% of 4 out of 4, despite this the job involvement is low at 30% of employees.

*   65% of employees belong to research and development, 30% to sales, and 5%to hr, despite this, the role with most employees are sales executives.

*   The work balance is good for around 70% of employees, job satisfaction is good for 60% of the employees, and environment satisfaction is 60% as well, the last 2 metrics could improve


HR ANALYTICS

In this project, the objective is to analyze from the dataset sample the spending of a digital media  campaign 

# DIGITAL MEDIA SPENDS DATA

## RESOURCES OF THE DATASETS USED IN THIS PROJECT

The given dataset was taken from the dataset in Kaggle Datasets.

### <li><strong> (contains all information about the digital media data)</strong> -  <a href="https\://www\.kaggle.com/datasets/yugagrawal95/sample-media-spends-data">Source</a><br>

### <li> <strong>media_data_spends.csv-  </strong> -  <a href="https\://www\.kaggle.com/datasets/yugagrawal95/sample-media-spends-data/download?datasetVersionNumber=2">Download</a>

## DATA MODELING

<iframe title="Media impressions & Sales" width="800" height="486" src="https://app.powerbi.com/view?r=eyJrIjoiMzgyNWY4MTAtNzg5OS00ODBmLWExZWItZDNjOWFjYTQxOWE3IiwidCI6ImU5OTQwNzJiLTUyM2UtNGJmZS04NmUyLTQ0MmM1ZTEwYjI0NCIsImMiOjR9" frameborder="0" allowFullScreen="true"></iframe>

## INFERENCES AND CONCLUSION

*   Sales in the span of 2 years have been around 570 M
*   There have been two sales peaks in November 2018 and 2019
*   Sales seem to be related to impressions from google and meta campaigns, in fact, google seems to be more incidental

## RECOMMENDATIONS

*   Detail in depth the correlation of affiliate and email campaigns, since it does not seem to see any correlation with sales


DIGITAL MEDIA SPENDS DATA

In this project, the objective is to analyze from the dataset sample FROM THE OLIST E-COMMERCE SITE

# DIGITAL MEDIA SPENDS DATA

## RESOURCES OF THE DATASETS USED IN THIS PROJECT

The given dataset was taken from the dataset in Kaggle Datasets.

### <li><strong> (contains all information about the digital media data)</strong> -  <a href="https\://www\.kaggle.com/datasets/olistbr/brazilian-ecommerce">Source</a><br>

### <li> <strong>olist_ecommerce_dataset.csv-  </strong> -  <a href="https\://www\.kaggle.com/datasets/olistbr/brazilian-ecommerce/download?datasetVersionNumber=2">Download</a>

## DATA MODELING

<iframe title="Report Section" width="800" height="486" src="https://app.powerbi.com/view?r=eyJrIjoiMDhmMDFmOWYtNDljMi00NDBmLTlmOWMtYzU2Y2QyMjk2NzAxIiwidCI6ImU5OTQwNzJiLTUyM2UtNGJmZS04NmUyLTQ0MmM1ZTEwYjI0NCIsImMiOjR9" frameborder="0" allowFullScreen="true"></iframe>

## INFERENCES AND CONCLUSION

*   The Olist platform sells many products and the best sellers include beds, bathroom tables, sports leisure, furniture decoration, beauty, health, home, and computer accessories, among others.
*   Most sellers and consumers are located in the state of Sao Paulo
*   Most sales are made through credit cards and the second is a method called boleto, which is widely used in Brazil.


OLIST E-COMMERCE

Hello, this project is for Fromsoftware fans, people interested in playing Elden Ring and open world games, especially those who want to build a character with physical strength or dexterity as their main ability, also search through the data and see the relationships or insights we can find.

## ELDEN RING (WEAPONS) - EXPLORATORY DATA ANALYSIS

## RESOURCES OF THE DATASETS USED IN THIS PROJECT

### <li><strong> (contains all information about the weapons in Elden Ring)</strong> -  <a href="https://www.kaggle.com/datasets/l3llff/-elden-ring-weapons?select=elden_ring_weapon.csv">Source</a><br>

The given dataset was taken from the dataset in Kaggle Datasets.

### <li> <strong>elden_ring_weapon.csv-  </strong> -  <a href="https://www.kaggle.com/datasets/l3llff/-elden-ring-weapons/download?datasetVersionNumber=2">Download</a>

## USED LIBRARIES/PACKAGES IN THIS PROJECT

*   **jovian** (to upload, save and share the contents of my notebook)
*   **numpy** (as np is one of the very famous packages for working with arrays in python)
*   **pandas** (Is greatly used in analysis of data and making dataframe)
*   **matplotlib** (Lets make our Analyzation fun and interative with the visualization library matplotlib)
*   **seaborn** (Adding more colours into matplotlib visualization)

## DATA PREPARATION AND CLEANING

### Import libraries

import pandas as pd

import matplotlib as mpl

import matplotlib.pyplot as plt

import seaborn as sns

import numpy as np

%matplotlib inline

### Downloading the Dataset

df = pd.read_csv('elden_ring_weapon.csv')

### Handling Null Values

df.isnull().values.any()

sns.heatmap(df.isnull(), cbar=False)
plt.title('Null Values Heatmap')
plt.show()

![](/images/output\_20\_0.png)

df.isnull().sum().sort_values(ascending = False)

Above in the heatmap and table, we can see that there are none null values in the dataset. . therre is no need to handle null data points, but there are some (-), so we need to change them for a value 0, then we can dive into EDA and modeling.

df\['Phy.1']=df\['Phy.1'].replace('-', '0').astype('int')

df\['Phy']=df\['Phy'].replace('-', '0').astype('int')

df\['Str']=df\['Str'].replace('N', None  ).astype('object')

df\['Dex']=df\['Dex'].replace('-', None  ).astype('object')

df\['Rst']=df\['Rst'].replace('NULL', 0  ).astype('int')

df\['Rst'] = df\['Rst'].fillna(0)

## EXPLORATORY ANALYSIS AND VISUALIZATION

### Inspect for data

df.head(5)

df.columns

df.shape

df.info()

df.describe()

pd.set_option('display.max_rows', None)

pd.set_option('display.max_columns', None)

df

### styling

sns.set_style('darkgrid')
mpl.rcParams\['font.size'] = 15
mpl.rcParams\['figure.figsize'] = (8, 4)
mpl.rcParams\['figure.facecolor'] = '#00000000'

## ASKING AND ANSWERING QUESTIONS

### Q1: what is the range of weight of the weapon that is repeated more & which is the heaviest weapon & How much the top 5 weapons weigh ?

sns.histplot(data=df, x='Wgt')
plt.ylabel("Count")
plt.xlabel("Weight")
plt.title('Weight of the weapons')

![](/images/output\_43\_1.png)

Most of the weapons weights are in the range  is between 5  > 0

df\[df\['Wgt'] == df\['Wgt'].max()]

The heaviest weapon is the Giant-Crusher

<img src="https://assets.gamepur.com/wp-content/uploads/2022/04/22193303/elden_ring_giant_crusher-1.jpg" />

df_wgt= df.Wgt.sort_values(ascending=False).head(5)

df_wgt.values

The heaviest 5 weapons weigh : 26.5, 23.5, 23. , 23. , 22.

<img src="https://static0.gamerantimages.com/wordpress/wp-content/uploads/2022/03/elden-ring-best-colossal-weapons.jpg" />

There are just a few weapons that weights a sustancial amount compared among the set of weapong, if you want build your character with pyshical streght, you should aim to obtain one of these

### Q2: what is the most repeated physical damage among the upgraded weapons & which weapon make the most damage ?

sns.histplot(data=df, x='Phy')
plt.title('physical damage')

![](/images/output\_54\_1.png)

The most repeated damage among the improved weapons is around 300

df\[df\['Phy'] == df\['Phy'].max()]

The improved weapon that makes more damage is the Jar Cannon

<img src="https://xboxplay.games/uploadStream/25747.jpg" />

### Q3: What is the group of weapons that have more weapons en compare the other groups & how many katanas there are and which is best?

df\['Type'].unique()

top_types= df.Type.value_counts()

top_types

plt.figure(figsize=(16,9))
plt.title("Weapons")
plt.pie(top_types, labels=top_types.index, autopct='%1.f%%', startangle=200);

![](/images/output\_63\_0.png)

The group of weapons with the most of them are the straight swords, followed by the gratswords, the glintstone staffs, halberds, and spears.

<img src="https://preview.redd.it/wzpfo60317x81.png?width=1846&format=png&auto=webp&s=150bfce86a263ab2f1cf04a5699e041cdbe650f6" />

top_types\['Katana']

There are eight types of katana

<img src="https://fictionhorizon.com/wp-content/uploads/2022/06/Elden-Ring-Katana.jpg" />

katanas=df\[df\['Type'] == 'Katana']
katanas

Best_katana=katanas\[katanas\['Upgrade'] == 'Smithing Stones']
Best_katana

For the ones who love Katanas you should get  the Nagakiba, thus is easier to upgrade with the Smithing Stones, which are more abundant, or the Uchigatana being  the starting weapon of the Samurai class, and could get from the beginning a decent amount of physical damage

### Q4: With which type of stone is more often improved the weapons Smithing Stones vs Somber Smithing Stones ?

df\['Upgrade'].unique()

top_stones = df.Upgrade.value_counts()

top_stones

plt.figure(figsize=(8,4))
plt.title("Smithing stones vs. Somber Smithing stones")
plt.pie(top_stones, labels=top_stones.index, autopct='%1.2f%%', startangle=75);

![](/images/output\_76\_0.png)

With the stone Blacksmith Stones, weapons are improved more often, therefore blacksmith stones are more abundant, and if some arams have good improvement with this stone, they are good options.

<img src="https://oyster.ignimgs.com/mediawiki/apis.ign.com/elden-ring/7/76/Somber1.jpg" />

### Q5: What percentages of scalability do you have with strength ability between the categories?

df\['Str'].unique()

top_str = df.Str.value_counts()

top_str

top_str = df.Str.value_counts(normalize=True).reset_index().rename(columns={'Str':'Count','index':'Str'})
top_str

top_Categories_str =top_str.head(6).sort_values(by = \['Str'],ascending = True)

sns.barplot(x = top_Categories_str.Str ,y= top_Categories_str.Count)
plt.title(' % Scalability with strength per category')

![](/images/output\_86\_1.png)

The majority of weapons that scale with strength are category D followed by C, B, E, and the best would be A, and S, the latter being the rarest and best.

### Q6: What percentages of scalability do you have with dextrous ability between the categories?

df\['Dex'].unique()

top_Dex = df.Dex.value_counts(normalize=True).reset_index().rename(columns={'Dex':'Count','index':'Dex'})
top_Dex

top_Categories_Dex =top_Dex.head(4).sort_values(by = \['Dex'],ascending = True)

sns.barplot(x = top_Categories_Dex.Dex ,y= top_Categories_Dex.Count)
plt.title(' % Scalability with Dextrous per category')

![](/images/output\_92\_1.png)

Most of the weapons that scale right-handed are category D followed by C, B, E, and according to this data set there would be no weapons with scalability in A or S

### Q7: what percentage of weapons have the most common critical damage?

df\['Cri'].unique()

top_cri = df.Cri.value_counts()

top_cri= df.Cri.value_counts(normalize=True).reset_index().rename(columns={'Cri':'Count','index':'Cri'})
top_cri

plt.figure(figsize=(10,5))
plt.xticks(rotation=0)
plt.title("% Critical damage among set of weapons")
sns.barplot(y=top_cri.Count, x=top_cri.Cri);

![](/images/output\_98\_0.png)

The most common critical damage among weapons is
91.25% with 100, 7.16% with 110, and only about 1.6% of weapons have a critical damage of between 110\<x<140 and those being the rarest and best.

### Q8:What kind of relationship is between weight and resistance?

ax = sns.scatterplot(x="Rst", y="Wgt", data=df, hue='Str')
ax.set_title("Weight vs. Resistence")
ax.set_xlabel("Resistence")
ax.set_ylabel("Weight");

![](/images/output\_101\_0.png)

If you remove some atypical data you can see a correlation between the weight and resistance of weapons positive, which is a very interesting correlation when wanting to also have a lot of resistance.

## INFERENCES AND CONCLUSION

Starting elden ring focused on maximizing character builds with strength ability is a very good decision, as they have better weapons to be scaled, in addition to having a good range of physical damage, and thanks to the wide variety of weapons you can choose between 30 types of weapons, and if you want to have greater resistance you should choose one of the colossal weapons or if you do not seek high resistance katanas are for you,  if you like katanas it would be good to get the nagakiba, so it's easier to improve with the smithing stone, which are more abundant for the physical damage it has, or the uchigatana being the initial weapon of the samurai class, and you could get from it Initially a decent amount of physical damage.

## FUTURE WORKS

*   Try other ways to build a character.
*   Try other data sets besides weapons
*   Do web scraping and collect the raw data from other parts of the game, and do EDA



ELDEN RING (WEAPONS) - EXPLORATORY DATA ANALYSIS

MY GOAL WITH THIS PROJECT IS TO MANAGE THE OFFICIAL DATA ON THE CORONAVIRUS AND FIGURE OUT WHAT HAD HAPPENED IN MY NATIVE COUNTRY AND THE WORLD TODAY.

#### Get the data

First off I went to ourworlindata.org the get the latest data from coronavirus 19, then I checked  in excel, and with power query, I extracted  the data required and put it in two files to make the queries more dynamic, start working with SQL server management and uploaded the files

#### Data that I'll use

I start the exploratory queries to find what type of data I have

```
SELECT location, date, total_deaths, new_cases, population, total_cases 
FROM portafolioprojectcovid..covidD
ORDER BY 1,2


```

```
SELECT location, New_Vaccinations, total_vaccinations
FROM portafolioprojectcovid..covidV
ORDER BY 1,2


```

#### Analysis in Colombia

###### This query is  to find the percentage of the population that has been infected

```
SELECT  location,CAST( date AS Date) as date,population, total_cases ,(total_cases/population)*100 as Cases_pertcentageFROM portafolioprojectcovid..covidD
WHERE location = 'Colombia'and  date ='2022-10-17 00:00:00.000'
ORDER BY   2 DESC




```

![](/images/CvsP.png)

*I find that around 12% of the population of Colombia has been infected*

###### This querie is  to find the percentage of the population that had died

```
SELECT  location, CAST( date AS Date) as date, total_deaths,  total_cases ,population,(total_deaths/total_cases)*100 as DeathpercentageFROM portafolioprojectcovid..covidD
WHERE location = 'Colombia'and  date ='2022-10-17 00:00:00.000'
ORDER BY  2 DESC




```

![](/images/CvsD.png)

I find that around 2% of the population of Colombia had died

*   This querie is  to find the percentage of vaccines given compared with the  population of Colombia

```

With PopvsVac (Continent, Location, Date, Population, New_Vaccinations, RollingPeopleVaccinated)as(Select dea.continent, dea.location, dea.date, dea.population, vac.new_vaccinations, SUM(CONVERT(FLOAT,vac.new_vaccinations)) OVER (Partition by dea.Location Order by dea.location, dea.Date) as RollingPeopleVaccinated--, (RollingPeopleVaccinated/population)*100
From portafolioprojectcovid..covidD dea
Join portafolioprojectcovid..covidV vac	On dea.location = vac.location	and dea.date = vac.date
where dea.continent is not null 
and dea.location = 'Colombia')

Select *, (RollingPeopleVaccinated/Population)*100 as PercentPeopleVaccinated, CAST( date AS Date) as dateFrom PopvsVacwhere date ='2022-10-17 00:00:00.000'
order by 3 desc


```

# ![](/images/PV.C.png)

I find that the vaccines given compared with the population of Colombia is around 97%

#### Analysis in the World

*   This querie is  to find the percentage of people that had died in the world

```
Select SUM(new_cases) as total_cases, SUM(cast(new_deaths as bigint)) as total_deaths, SUM(cast(new_deaths as bigint))/SUM(New_Cases)*100 as DeathPercentage
From portafolioprojectcovid..covidD
where continent is not null 
and location not in ('World', 'European Union', 'International', 'High income', 'Low income', 'Lower middle income', 'Upper middle income')
order by 1,2


```

![](/images/DpW%20\(1\).png)

I find that around 1% of the population had died in the world

*   This query is  to find the number of people that had died in the world per continent

```
Select continent, SUM(cast(Total_deaths as bigint)) as TotalDeathCount
From portafolioprojectcovid..covidDWhere continent is not null 
Group by continent
order by TotalDeathCount desc


```

![](/images/DpC-8c98fc25.png)

I find that America is the continent that has  more deaths, followed by Europe and Asia

*   This query is for looking at a map of the highest infection rate

```
Select location, Population, MAX(total_cases) as HighestInfectionCount,  Max((total_cases/population))*100 as PercentPopulationInfected
From portafolioprojectcovid..covidD
Group by Location, Population
order by PercentPopulationInfected desc


```

![](/images/Map.IR.png)

I find that Europe is the continent that has  more Infection rate among the others

*   This query is for looking at a graph of the infection rate percentage compared with the population of some relevant countries and some forecasting of those

```
Select location, date, Population, MAX(total_cases) as HighestInfectionCount,  Max((total_cases/population))*100 as PercentPopulationInfected
From portafolioprojectcovid..covidD
Group by Location, Population
order by PercentPopulationInfected desc


```

![](/images/PPI-2f9331f6.png)
Among these countries, Italy has the 37% of its population infected, followed closely by the united kingdom and New Zeland with around 35% of its population that had been infected

*   This query is to understanding how many vaccines have been given to the population of the world per continent

```
Select  d.location, d.date, d.population
, MAX(v.total_vaccinations) as RollingPeopleVaccinatedFrom  
portafolioprojectcovid..covidD d 
Join portafolioprojectcovid..covidV v	
On d.location = v.location	and d.date = v.date
where  d.date ='2022-10-17 00:00:00.000'and d.location  in ( 'Africa', 'Asia', 'Europe', 'North America', 'Oceania', 'South America','World')
group by d.continent, d.location, d.date, d.population
order by 1,2,3 DESC


```

![](/images/V.W.png)

This shows that almost in every continent the vaccines given have surpassed their population, but understanding that normally every person took around two or three, it should be seen with caution this graph

#### Conclusion

*   Colombia is expected to maintain the cases of people infected around 12% or lower, which is  great news since is my native  country, this could be for the cultural acceptance of the vaccine and the implemented protocols to prevent this disease

*   Even though this disease took all over the world, the death percentage is really low being 1%, this could be for the fact of the rapid creation of the vaccine and its spread around the globe

*   Around the world, the vaccines that have been given are almost double, but this should be even more since some predictions are that the cases are still going to growth


CORONA VIRUS AT COLOMBIA AND THE WORLD 2022

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Updated for 2020 and now in its fourth edition - this is still my go-to book on business analysis. An updated collection of works by renowned experts in the field, this book delivers an overview of the role and know-how of modern business analysis. A thorough examination of problems and solutions in the contemporary business environment has proven to be very useful for both students and experienced professionals.

### Business Analysis Techniques - Revised Edition, by Paul Turner, James Cadle, Debra Paul

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The development of business analysis as a professional discipline has extended the role of the business analyst who now needs the widest possible array of tools and the skills and knowledge to be able to use each when and where it is needed. This new edition provides 123 possible techniques and practical guidance on how and when to apply them.

### Business Analyst: a Profession and a Mindset, by Yulia Kosarenko

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This is a great insight into what it really means to be a business analyst. The book explores the skills and personal traits needed to be successful in the field. It will help you discover the best ways to contribute to your organization.

### A Guide to the Business Analysis Body of Knowledge (BABOK Guide)

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The most comprehensive and essential guide for anyone getting into the world of business analysis. Very helpful for students,  junior analysts, but also a great source for experts.


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