Azeez Olanrewaju Shoderu<\/p>\n
EntryLevel Data Analyst Program
\nUniversity of East London, UK through UNICAF Scholarship
\nDownload the Report
\nIntroduction
\nA new initiative has just started on this newsletter. Every month I would be releasing a data analysis report from one of my data science projects within and beyond my current job for practical study and learning by aspiring data scientists and machine learning engineers. Today, we would be educating ourselves on how I carried out a data analysis project through the use of Google Sheets and Tableau. Though, Excel can also be used for the same analysis on Sheets. If you would like to be able to do such data analyses yourself, join me on EntryLevel\u2019s Data Analyst programs.
\nProject Description
\nThe Business Project at hand
\nAs a Data Analyst working for A.O.S Migration Agency, I have been asked by the director, Head of Canada Immigration at the Agency, to present the data on the statistical ratio of migration between developed and underdeveloped\/developing countries, and have downloaded statistical data on migration from different countries to understand where business opportunities to increase digital marketing may lie, and track the travel performance of countries\u2019 applicants.
\nThe director has suggested marketing majorly to Nigerians and other Africans to charge more because he believes that these are the most likely Canadian immigrants. He needs to send a report to the CEO in the next two weeks on how they will increase their next quarterly earnings.<\/p>\n
What is the business problem?<\/p>\n
The existence of lack of knowledge and proper insights about the best countries to market Canada immigration services to generating income and base marketing on for the next 3 months<\/p>\n
How long do I have to work on this project?<\/p>\n
Just in two weeks\u2019 time<\/p>\n
What data should be collected to understand this problem? How should it be presented?<\/p>\n
Countries\u2019 migration statistics and their popularity presented in a manner of most relocating to less and in terms of developed vs. undeveloped countries migrants willing to pay for processing.<\/p>\n
What questions would you ask to better understand the business problem?<\/p>\n
Which countries are the top 30 that migrates to Canada worldwide?
\nWhich of these are African? Local\/International? Developed? Underdeveloped\/Developing?
\nIs Nigerian among these topmost countries?Data Design
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 After the raw Canada immigration dataset was downloaded and imported into the Google Sheets webpage, it went through the process of cleaning. The quality of the data was rendered high by removing the blank cells and filling up the ones that were mistakenly left blank if required. Duplicates were also removed from the data file to avoid dirty data. And, headers were regulated while the values of variables in the columns and rows were corrected if found erring from the other counterparts.
\nIn the data analysis software, the misleading continents headings like \u2018Latin America and the Caribbean\u2019 and \u2018Northern America\u2019 were removed completely and replaced with \u2018Americas\u2019 in order not to confuse the audience with their respective countries using the Find and replace function in Edit dropdown area of the software. Since, only 2 countries was listed under the Latin America and Caribbean which contrasts from the about 33 countries that South America (part of former Latin America and Caribbean) continent now has embedded in it. It was better to remove the former name not to cause confusion. Same idea goes for the Northern America continent. The Americas is better as it combines the two continents well enough for the purpose of this study. Furthermore, the term \u201cregions\u201d was deleted from the \u2018developing and developed\u2019 names so that another column can be created with it as its header.
\nThus, the new column with the header \u201cregions\u201d was added for the purpose of our analysis. It contains the two major distinctive parts the world is divided into solely for this study which are; local and international. The vLookUp function was used to instantly select all African countries as \u2018local\u2019 and other countries not in the black continents as \u2018international\u2019. The left\/right function was also applied to create a basic abbreviation column for all the continents.
\nFindings from the data
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Working with the cleaned dataset on the technological tools \u2013 Google Sheets and Tableau unveiled some interesting facts about the reality of Canada immigration. Citizens of various countries relocate to Canada more than presumed. In fact, many non-Africans were in the League of Nations moving out of their own homelands to reside and live permanently in Canada. Here are tables and charts with explanations about the noticed trends among these countries;Google Sheets Charts and TablesAll Migrating Countries Statistics\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 To begin with, the pivot table below shows that, the number of African countries participating in moving from their nations to Canada is more than any other continent singularly and even with the mixture of both the South America and North America continents using our time range of course (between 1980-2013);
\nTable 1: Total Countries by Continents
\nAs indicated in the above pivot table, this analysis focuses on the noticeable trends amongst 195 countries of the world in relation to Canada immigration.
\nFigure 1: Percentage of Countries by Continents
\nAfrican countries compared to countries in other continents show a percentage ratio looking at the pie chart above. Apparently, Africa is the highest relocating to Canada, followed by Asia, then Europe, while the least migrants to Canada is Oceania (present-day Australia).
\nThe percentages of every country involved in Canada immigration shows that the most part of the world (be it developed or developing\/underdeveloped) has participated in allowing their citizens either willingly or not to move from their home countries to Canada as indicated in the 3D pie chart underneath;
\nFigure 2: All Migrating Countries by Percentages
\nContinents with their Migrants Population
\nTable 2: Total Migrants by Continents
\nThe staggering vs. increasing rates of the population moving out of these countries from their continents shows yet another trend that is quite impressive. Even though Africa happens to be the continent with the highest countries\u2019 citizens relocating to Canada, the continent does not pass the examination of the migrant population indicated in the pivot table.
\nOn a contrary, many of the continents who were behind Africa now come defeating it when it comes to migrating population count. Asia is by far the continent with the largest amount of travellers with more than to half of the count going to it. Africa in this measurement find itself in the second to the last position surpassing only Oceania\/Australia in as diagrammatically illustrated in the column chart below;
\nFigure 3: Total Migrants by Continents
\nDeveloped vs. Developing Countries
\nTaking a step further, we would note that all African countries migrating to Canada fall under the developing\/underdeveloped countries due to glaring reasons as highlighted in the previous pivot table. However, there is a disparity between developed and developing countries in other continents except for Europe which appears to have all its countries already developed in the upper pivot table. The stacked bar chart below describes this concept better;
\nFigure 4: Total Developed\/Developing Countries by Continents
\nContinent Ratio of Local vs. International
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 From the previous visuals, it can be summed up that local black continent has just 54 countries but still amounts to more than one-fifth of the total count of countries in the whole world according to the dataset used during the course of this data analysis process as shown in the pivot table below;
\nTable 4: Total Countries by Continents and Levels
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Even with its limited number, it outperforms all other continents when it comes to the amount of participating countries migrating to Canada for the years 1980 \u2013 2013 as portrayed in the following stacked column chart;\u00a0
\nThe Top 40 Migrating Countries
\nOut of the 195 countries added to this general study, 40 of them were removed in order to go into detailed study especially to decipher the trend for Nigeria and other African countries. Sadly, African countries are only few in this subdivision. Only a small countable fraction (7) is in the top 40 which are; South Africa, Somalia, Nigeria, Morocco, Ethiopia, Egypt and Algeria as depicted in the line chart below;Figure 6: Top 40 Countries by Regions and Developed vs Developing Nations
\nIn the top 40, it is glaring that countries from Asia, Europe and the Americas like India, United Kingdom, China, Philippines, Pakistan and United States surpass African countries as displayed in the bar chart below;
\n\u00a0
\nFigure 7: Top 40 Countries by Migrants
\nDownload the Report
\nTableau Charts
\nFigure 8: Top 40 Countries by Developed\/ Developing Nations
\nA new initiative has just started on this newsletter. Every month I would be releasing a data analysis report from one of my data science projects within and beyond my current job for practical study and learning by aspiring data scientists and machine learning engineers. Today, we would be educating ourselves on how I carried out a data analysis project through the use of Google Sheets and Tableau. Though, Excel can also be used for the same analysis on Sheets. If you would like to be able to do such data analyses yourself, join me on EntryLevel\u2019s Data Analyst programs.
\nProject Description
\nThe Business Project at hand
\nAs a Data Analyst working for A.O.S Migration Agency, I have been asked by the director, Head of Canada Immigration at the Agency, to present the data on the statistical ratio of migration between developed and underdeveloped\/developing countries, and have downloaded statistical data on migration from different countries to understand where business opportunities to increase digital marketing may lie, and track the travel performance of countries\u2019 applicants.
\nThe director has suggested marketing majorly to Nigerians and other Africans to charge more because he believes that these are the most likely Canadian immigrants. He needs to send a report to the CEO in the next two weeks on how they will increase their next quarterly earnings.<\/p>\n
What is the business problem?<\/p>\n
The existence of lack of knowledge and proper insights about the best countries to market Canada immigration services to generating income and base marketing on for the next 3 months<\/p>\n
How long do I have to work on this project?<\/p>\n
Just in two weeks\u2019 time<\/p>\n
What data should be collected to understand this problem? How should it be presented?<\/p>\n
Countries\u2019 migration statistics and their popularity presented in a manner of most relocating to less and in terms of developed vs. undeveloped countries migrants willing to pay for processing.<\/p>\n
What questions would you ask to better understand the business problem?<\/p>\n
Which countries are the top 30 that migrates to Canada worldwide?
\nWhich of these are African? Local\/International? Developed? Underdeveloped\/Developing?
\nIs Nigerian among these topmost countries?
\nIndeed, the number of developed countries is nothing compared to the underdeveloped\/ developing countries of not only Africa but also Americas and some parts of Asia as well in the side-by-side bars above. It also shows the number difference between developed and developing countries in respect to Canada immigration.
\nFigure 9: Top 40 Countries by Continents, Regions and Migrants
\nIf we are to divide the top 40 countries into continents, Asia has more countries in for Canada immigration than any other continent. Its migrating population surpasses those of other continents too followed by Europe and only after them are the Americas. Unsurprisingly, Africa appears not to fall in the top but very least in terms of both countries and moving population as adapted from the horizontal bars above.
\nTopmost 7 out of the 40 Countries
\nTo be more conservative, it can be concluded that 4 countries stand out most from the rest \u2013 India, China, United Kingdom and Philippines. Extending the range further, 3 other countries are coming up behind the first 4 namely; Pakistan, United States of America and Iran as identified in the treemap below;
\nFigure 10: Top 40 Countries by Migrants
\nAnalysis of the data
\nFrom the analysis, many submissions can be made about countries\u2019 immigration patterns based on the data collected and analysed as we dig deeper into its analysis. To be specific, the submissions can be given through the following metrics;<\/p>\n
Africa has the highest number of countries involved in Canada immigration
\nThe majority (if not all) countries worldwide participate in Canada immigration
\nAfrica comes second to the last before Oceania\/Australia in continents ranking
\nAsia is undoubtedly the first and topmost continent leaving their countries for Canada
\nAll African countries involved in Canada immigration are all developing\/underdeveloped
\nContrarily, countries in Europe involved in Canada immigration appears to be developed
\nOnly seven (7) countries \u2013 South Africa, Somalia, Nigeria, Morocco, Ethiopia, Egypt and Algeria are in the top 40 countries from Africa interested in migrating to Canada
\nIndia, United Kingdom, China, Philippines, Pakistan, Iran and United States surpass African countries and appear to be the leading seven (7) countries within 1980 \u2013 2013
\nFour out of these 7 countries have the highest population moving to Canada within the three decades surpassing the 500,000 threshold
\nNone of the local African countries have reached 100,000 threshold for the 3 decades
\nNigeria appears to be the 36th country in the 40 countries\u2019 ranking for Canada immigration within the years 1980 \u2013 2013
\nLastly, Nigeria has even one of the lowest population moving to Canada within the years 1980 and 2013.Analysing the Root Cause
\nThe Root Cause Analysis developed by Sakichi Toyoda, founder of Toyota Industries, we use the 5 Whys technique to determine the root cause of the framed problem we applied during this data analysis process by repeatedly asking the question \u201cWhy\u201d;
\nWhat I see happening?
\nThe trend shows that Nigeria and largely African countries are not even in the top 30 nations migrating to Canada from 1980s \u2013 2013.
\nWhat are the specific symptoms of the problem?
\nSymptoms of this problem may be noticed in the development rates of these countries; as more\u00a0 number of citizens from developed countries are migrating to Canada as permanent residents from 1980s \u2013 2013 than those of the developing countries.
\nWhat is your hypothesis for the cause of the problem?
\nIt can be predicted now that the hypothesis for the cause of the problem is noticed in the large applications coming from countries in Asia, Americas and European countries and acceptance of their applications more. Cause of the following problem: A migration agency does not know countries\u2019 citizens to market its travel services. Why?
\nThe company lacks insight on where most Canadian migrants come from.
\nWhy?
\nMany of its agents work mainly with Nigerian immigrants
\nWhy?
\nThat is the local market available in our region
\nWhy?
\nOur organization has just been registered
\nWhy?
\nIt is rather a startup business that just commenced activities.
\nIn other words, a problem of lack of insights turned out to be a startup issue. However, the underlying problem can be solved primarily by carrying out a detailed research or data analysis study on past available raw data through Open Source data from Immigration, Refugees and Citizenship Canada (IRCC).
\nConclusion
\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 This detailed data analysis has attempted to bring up the salient information from the raw Canadian immigration dataset about the patterns conveyed by the numbers of population that have been involved in the process of relocation from the early 1980s up until the year 2013.
\nTo begin with, Nigeria does not share a fair chance with the likes of India, China and United Kingdom. However, it is not to say that migrants from Nigeria are not moving in their masses out of the country to Canada. In fact, the country is doing well amongst its peers. Hence, it can be a viable market for the agency to continue tapping into.
\nMoreso, Nigeria being the base of the company can be instrumental in helping get first-hand client reviews for the company (A.O.S Migration Agency) services. Although, in my own opinion, getting clients from countries in Asia like Indians, Chinese and\/or Filipino, will definitely fast-track the organisation\u2019s impact in the Canadian immigration space.
\nFor sure, the strategy will yield profits for the company within the next quarter of the year if applied almost immediately. In addition, some European and American nations are worthwhile as well though I predict it may be more challenging for us to convince some of them to patronize us considering the fact that we are Nigerians. Nonetheless, countries like the United States of America, United Kingdom and France may also increase our incomes.
\nFrom the analysis, it is evident that even with the popularity of Canada immigration amongst African nations, many are either not willing to migrate or cannot afford the expenses, hence I would advise to shy away from focusing more marketing on them.
\nLastly, this report can be used to encourage the publicity of Canada immigration amongst Nigerians, Africans, Asians and Europeans as it is indeed a good reference that helps us understand more about the countries participating heavily in relocation abroad.
\nUpcoming Data Analysis Projects
\nAs a keen data scientist in the fields of cross-disciplines and career paths of education, migration and research, at least once a month, there would be some general purpose professional project shared with my subscribers starting from now. So, if you want to be part of this learning quest, kindly subscribe to this newsletter\u00a0or our company blogspot\u00a0to get notified via your email address and\/or LinkedIn accounts when a new analysis is dropped. Since this analysis emphasized on 1980s \u2013 2013, there would certainly be a continuation from where it was halted here; that is from the year 2014 up until 2021 or even the first quarter or four months of 2022 will be analysed later on.\u00a0
\nCheck the links below for the datasets I downloaded, cleaned and worked on. Perhaps, these could guide your own learning and practice as well. Stay tuned! Please drop a comment, like and share to support our learning adventure. Thanks very much!
\nReference List
\nAmmara, A. (2021, October 9). Immigration to Canada Immigrants from 150+ countries between 1980 to 2013. https:\/\/www.kaggle.com\/datasets\/ammaraahmad\/immigration-to-canada
\nImmigration, Refugees and Citizenship Canada (2022, March 31). Permanent Residents \u2013 Monthly IRCC Updates Canada \u2013 Admissions of Permanent Residents by Country of Citizenship. https:\/\/www.cic.gc.ca\/opendata-donneesouvertes\/data\/IRCC_M_PRadmiss_0002_E.xlsx
\nOrganisation for Economic Co-operation and Development (2022, May 27). International Migration Database. OECD. https:\/\/stats.oecd.org\/Index.aspx?DataSetCode=MIG
\nShoderu, A. O. (2022). Canadian immigration cleaned dataset between 1980 \u2013 2013. Google Spreadsheets. https:\/\/docs.google.com\/spreadsheets\/d\/1FjemlU-CXrJi2C792VCO6hneTvp0S3-h9ITbm15bCnA\/edit?usp=sharing
\nShoderu, A. O. (2022). Countries migrating to Canada. Tableau Public Data Visualization. https:\/\/public.tableau.com\/app\/profile\/azeez.olanrewaju.shoderu\/viz\/CountriesmigratingtoCanada\/top40countries
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Azeez Olanrewaju Shoderu EntryLevel Data Analyst Program University of East London, UK through UNICAF Scholarship Download the Report Introduction A new initiative has just started on this newsletter. Every month I would be releasing a data analysis report from one of my data science projects within and beyond my current job for practical study and […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":2,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-120","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=\/wp\/v2\/pages\/120","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=120"}],"version-history":[{"count":0,"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=\/wp\/v2\/pages\/120\/revisions"}],"wp:attachment":[{"href":"https:\/\/backup.aosacademy.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=120"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}