The truth is that in the next decade, the knowledge of research data analytics you now have may not be as useful as it it now. The teaching methods you now use may not be much applicable. So the big question is this: Would you hold on to the traditional system or would you take steps to keep pace with the growing field of technology in research?
|Data Analytics Becomes Intelligent Data Analytics|
Unfortunately over the years I have seen some researchers loose their relevance due to not developing their skills in emerging trends of research data analytics. The problem with this is that as an educator, you end up passing an antiquated knowledge to other scholars who in time pass it to others and it becomes a vicious cycle. This has to be broken!
Some even keep spending on statisticians or data analysts each time they carry out a research. Then why are you a researcher in the first place? Even if you don’t have to do all the data analysis yourself, it’s necessary to have an idea of the current technologies in data analytics.
That is why I have chosen to tell you about about the new trend, Intelligent Data Analytics.
What is Intelligent Data Analytics(IDA)?
When I was moving to Europe for my studies in Computer Engineering, I never knew I would start from the scratch to learn a new aspect of Research Data Analytics. This is because I have been involved in data analysis for many years especially having knowledge of MS Excel, IBM SPSS and other tools.
When received an email from my supervisor telling me that one of the recommended courses is Intelligent Data Analysis, my first reaction was: “This sounds interesting!”. I have to go to the faculty website to get information about the course as well as the course schedule and these are some details/objectives
- It tends to harmonize statistical research data analytics, big data analytics, machine learning, statistics and artificial intelligence.
- Intelligent Data Analytics provides a systematic summary of intelligent methods applied in the data analysis process
- Brings you up to speed about machine learning schemes applied in modern data analytics
- It presents and discusses real-world applications of IDA, ranging from the field of system diagnostics, bio-medicine and pharmaceutical research
Theoretical Framework of IDA
- Data transformations using ontologies
- Semantic data repositories
- Data Visualization
- Data Cleaning and manipulation
- Data Clustering and Classification
- Probabilistic Graphical Models
- Supervised Learning
- Unsupervised Learning
- Decision Trees
- Sensitivity Analysis using re-sampling methods
- And many more
- Buy some modern textbooks: As a researcher, you need to have a number of modern data analytics textbook(at least 10)
- Learn to use the Applications: Learn how to use statistical data analysis packages, not just SPSS. MatLAB is what is currently used for Intelligent Data Analysis
- Learn a bit of programming. Get some practical knowledge of computer programming. Choose some random programming language and learn how to write basic programs.( I suggest Python as that is the language mainly used in the IDA course)
- Attend Workshops: Try to attend Data Analysis workshops at any opportunity (even if this involve a considerable cost). Datarmatics normally organizes such workshops in Nigeria.
- Study, Study, Study: One question, I normally ask is this: Who do you think should study more? The Instructor or the Learner? Leave your answer in the comment box below.
There is need for scholars generally (but especially African scholars) to keep up to date with current trends in technology. Of course you know of terms such as Computer Aided Learning(CAL) or Computer Aided Instruction(CAI). You can ask yourself how well you have adopted these at least in your own school. Even if we are limited in the much we can do in our domain, let join voices, write papers, articles etc that promote modern trends in our schools.
Thanks for the time spent in reading this. Do share with other researchers and do leave a comment if you have any questions or recommendations regarding this.