The terms and job titles in the data field are extraordinarily large, such as business analysis, data analysis, data science, etc.
They often stun everyone, so this article we will talk about all of them, especially the differences.
Business Analysis VS Data Analysis
Generally, data analysis means that using data to analyse a XXX problem. The pipeline includes data collecting, data warehousing, data cleaning, data visualisation, etc.
As we can see, there is a blank in the sentence.
Actually, data analysis can be applied to different areas, and that is also the meaning of ‘XXX’ .
It can be academic or commercial.
If ‘XXX’ is commercial, then data analysis will equal to business analysis. In other words, the business analysis is an application of data analysis.
There is a main difference between data analysis and business analysis, which is the data sources.
The data source of data analysis positions is often based on the company websites, Apps, ERP system, etc.
And those job descriptions will require candidates master SQL, Python or R.
However, the data collected from those platforms often exists several problems, mainly the data quality, such as some missing data or data noise.
Those are caused by weak IT infrastructures and ‘econnoisseurs’ who want to register more user accounts.
However, the data sources of business analysis positions not just include those internal data collected by companies but also include a large amount of external sources. For example:
Business Analysis VS Data Science
Because of Alpha Go, Artificial Intelligence is well-known to everyone.
However, the successful area of AI is not related to data analysis.
They focus on the computer vision and natural language processing, and their industrial applications are mainly in security and supply chain.
However, the usage of algorithms in commercial industry is limited because some departments in the company are hard to be represented by data and algorithm models.
This causes that algorithm can only solve the specific problems.
Firstly, algorithm is related to users directly. The famous example is risk control. Because the factors contributing to the user credits are easy to be built by an algorithm model, commonly logistic regression.
Secondly, forecasting algorithms. Business Intelligence is highly required to do forecasting.
Thirdly, dimensionality reduction algorithms. It is often to reduce dimensions so it is easy to evaluate a problem, such as new products.
In summary, algorithms are useful in business intelligence, however, it cannot replace it.
Business Intelligence is an application in commercial problems and algorithm models are tools to solve specific problems.
If you are interested in or have any problems with Business Intellgence, feel free to contact me .
After I completed my bachelor degree of Information Security in Xidian University and experience in Huawei, I start to look for a new challenge.
Then I came to New Zealand to study the Master of Computer and Information Sciences in AUT.
February 2018 – July 2018: Selected Courses
In the first semester of my master degree, I enrolled in two papers: Data Mining and Machine Learning, Natural Language Processing.
The two courses have assisted me in broadening my knowledge while showing me the diverse research side of Computer Sciences, especially Machine Learning and Artificial Intelligence.
July 2018 – December 2018: Master Thesis
Then I enrolled in the 120 points thesis in the second semester.
The thesis research of my Master degree is totally a different challenge to me.
It differs from previous teaching courses because its requirements of the abilities of critical thinking and problem-solving are much stricter.
Moreover, research is also an iterative procedure, so patience and persistence are also essential when it comes with persistent failure.
Luckily, I met my primary supervisor and second supervisor, who guided and helped me a lot.
In 14th November (my birthday day) last year, my primary supervisor suggested me to apply for the Summer Research Award at AUT, which can further augment to my capabilities.
Summer Research Award is awarded to students who have got satisfactory results in the three-month project in AUT. The evaluation is based on the final data analysis report. Only 10 students in the Faculty of Computer Sciences can get the award.
December 2018 – February 2019: Summer Research Award
Then I applied for it and my proposal got approved.
Through three months, I successfully classified and achieved a 92% accuracy result by building a neural network model in this project.
In the last week of February this year, I developed creative contents as academic reports with online LaTeX Editor (Overleaf) to get my final payment.
I recommend the Summer Research Award in AUT, because not only it can bring you some extra gains and honour, but also it can offer you an opportunity to gain some practical skills.
The most important thing is that I start to realise I am more interested in practical industry rather than academia!
Attachment is the Summer Research Award I gained.
February 2019 – July 2019: Two Publications
I also developed my research and analytical skills while publishing two papers
“An automated privacy information detection approach for protecting individual online social network users” to the Japanese Society of Artificial Intelligence (JSAI) journal
“Privacy Information Classification: A Hybrid Approach” to The 4th International Workshop on Smart Simulation and Modelling for Complex Systems (SSMCS 2019)
Maybe some people think I should take a PhD degree to enhance my research skills. However, I am not interested in academia any more.
It is time for me to continue my journey and gain more experience in the industry of Business Intelligence and Data Science industry!
If you are interested about New Zealand University, selecting papers, Summer Research Award, or how to publish papers on journals or international conferences, feel free to contact me and I can give you some suggestions.