How to Improve Data Analysis Skills for Academic Research?

by admin

Whether you are writing a research paper or dissertation, without conducting data analysis, you can never succeed in achieving the research goal. Data analysis is the systematic process of applying logical and statistical techniques to illustrate, recap, evaluate and condense the scientific data.

Collection of meaningful data, data consolidation from multiple sources, data security, selection of the right analysis tool, and interpretation of the data visualization are some important steps of data analysis that demand special skills from students. Some of these data analysis skills include machine learning, statistical programming skills, data management, statistical visualization, and many more. This article aims to describe tips to improve data analysis skills after briefly describing some of these skills.  

Data Analysis Skills:

Data analysis skills refer to the capabilities of researchers to organize, summarise, and conclude findings from the collected data. Analysis of data is a technical task that demands a lot of expertise from a researcher. Getting full command of only one skill is not sufficient to cope with the challenging nature of such a skill.

You may need to hire a best dissertation writing service or get some guidelines available online Thus, the following is a list of data analysis skills that you must polish if you want to succeed in academic research.

  • Excellent problem-solving skills: It tells how an issue happens and what the possible ways are to resolve it as well. 
  • Solid numerical skills:  It allows us to calculate and quantify the problems by using a number of mathematical formulae. 
  • Good statistical programming skills: These are abilities to use statistical tools for data analysis. 
  • Ability to look inside the wall: These are the skillsthat help you interpret data more efficiently.  
  • Data management proficiency: The skills that you need to effectively use and manage large datasets are called data management skills.
  • Good knowledge of machine learning:  Machine learning helps data analysts to know programming languages such as Python, SQL, and Java.

Pro-tips to improve data analysis skills: 

Good data analysis skills are not only useful in making a good academic research record; rather, it brings a lot of career opportunities for you as well. Thus, it is necessary for students to polish these data analysis skills during academic life. However, a million-dollar question is ‘How I can learn new data analysis skills and polish the existing ones?’

Many of you may think, should I hire a tutor? Whether I should get myself enrolled in online skills development program? Or, should I get admission in some skill development program in my vicinity? All are pretty well options if you can afford them. Yet, considering the following tips is a great option to polish some data analysis skills.

  1. Start from learning Excel: Without becoming proficient in Excel, you must not go for any advanced statistical analysis tool learning. Excel is the simplest tool that can help you get familiar with the worksheet- a common element of the statistical tool.
  2. Once learn Excel, start learning another statistical tool: Excel is the general data analysis toolYou can use it for anytype of analysis, irrespective of the discipline. However, learning only Excel is not sufficient; you must learn another tool that specifically relates to your field of interest. For example, if you are social sciences student, you should learn SPSS, Stata or any other statistic tool unless and until you start considering yourself an SPSS or Stata expert. Youtube lectures, video tutorials, statistical analysis books and guides are one-place solutions to all statistical tool learning problems.
  3. Master machine learning: For mastering machine learning, knowing the programming language is essential to foster as a data analyst. Data analysis in 2022 is impossible without having an advanced level of understanding of machine learning. You should be inevitably working on computers and software; thus, learning programming languages of Python, PIG, Matlab, and Hive is of utmost importance in data analysis. Even if you plan to use statistical packages such as R, SAS, and SPSS, you still need to learn a statistical language for performing the research tasks. Online courses are the best ways to know machine learning. 
  4. Understand the real meaning of ‘analytical skills’: There are many definitions of analytical skills. It is necessary for data analysts to know the real meaning of analytical skills to polish them. In general, most professionals think that analytical skills are the ability of a person to deal with information in an inductive or deductive manner. For others, it is the ability to collect, analyse and interpret information to sketch a big picture. All in all, it is the ability of a person to take big pieces of information and break or analyse them to study the minor details. Playing mind games, solving mathematical problems and joining debate clubs are some important ways to improve the data analysis skills, such as analytical skills.        
  5. Develop database design concepts to improve data management skills: In order to develop data management skills, a proper understanding of database design concepts is also necessary. The data analyst must know about the limits and benefits of discipline-specific databases. Long-term and short-term planning related to how to analyse and store information in a different form is another form of improving data management skills. 
  6. Improve problem-solving skills by creating problems:  The word problem refers to a situation or a matter that seems harmful, and it needs to be handled properly to minimize its effect. Problem-solving is an important data analysis skill that every analyst must learn to cope with the research problem. Research and brainstorming are the two most authentic ways to improve problem-solving skills.

Bottom lines: 

In a nutshell, data analysis skills are the abilities of researchers to observe trends or patterns in data to extract useful consequences. Data analysis is a multifactorial process. It simply means that to analyse data, we must have to focus on a number of factors.

To tackle a large number of tasks at the same time, data analysis demands extraordinary skills such as excellent problem-solving skills, solid numerical skills, good statistical programming skills, ability to look inside the wall, data management proficiency, and good knowledge of machine learning. On the same ground, to polish all these data analysis skills, you should start working on them now.

Related Posts

Leave a Comment