Here's how you can master research skills in both academia and industry.

Navigating networking pitfalls in the data analytics field requires a strategic approach. First, know your value by understanding your skills and confidently showcasing what you offer. Research contacts using LinkedIn to identify key people and learn about their backgrounds and interests. Engage genuinely by showing real interest, asking questions, and listening attentively to build meaningful relationships. Lastly, offer help by providing value through sharing knowledge, giving advice, or connecting others. By being supportive and a team player, you can foster a strong, beneficial network in the data analytics community.

Understanding what the basics are for any particular project regardless of its nature are crucial for its development process. This can be achieved by searching fundamental keywords of the project in various databases or any reliable sources of scientific information as well as alternatives (thinking outside of the box). Then it’s important to gather all that information in a document and identify which is relevant for the purpose of the project, defining what are the goals, the costs, how long its going to take to achieve those goals and any potential complications. Once approved, the next step would be to communicate to anyone involved or potentially involved in the project to discuss it’s development on a regular basis.

A fundamental step in research, starts with finding information, whether new or existing it is. What I always like about research, the idealistic approach and also the need for the academic discourse and lifelong learning experience to be made again. In research, you must know all of it, even from the start.

Definitely, one thing to keep in mind is to not rely on only one or few sources of information. It is important to gather as much information as possible without overloading yourself. Also note that online databases aren’t always the only sources and should consider contacting people that are directly involved in any of the topics of interest or experts in the field that may sometimes be more efficient with added complementary researched documents.

Gathering resources, depends on the data you will be using for your own research. All of the time, many people misunderstood the time they were researching for sources to gather for literature and related studies. In making the best research you can do and create will always be an expressive and effective one.

Data will usually be overwhelming to analyse and it takes times to ensure that every aspect of it is well understood. It’s important to not rush through it and take your time to understand everything at all scales. Strong keywords are a good way to distinguish relevant information from the less-relevant resources. For statistical data, it’s important to identify the correct tests that best fits the model to get a better understanding of the data.

This is especially important to keep track of all the documents of a project to not lose yourself so that when ever you need to present your findings you can quickly pull out all the information needed. In academia it will usually depend on your own way to organise everything, in contrast to industry where mostly everything is organised in a standardised way and usually imposed by the system already put in place. In any case, it’s good to identify each document types and add easy search tags to them so they can be easily found. In digital format, a “read me” file containing additional detailed comments and instructions relevant to the attached documents is also extremely useful to keep track what is contained in the file.

One thing I've found helpful in the field of research is to look more deeply and standardize the protocols of any experiment. With the proper help of suitable AI tools and high throughput technologies it has been easier for me to analyse the result and it makes my days simpler. In addition to that, we should be more focused on publishing an article with a strong hypothesis that addresses basic research. Instead of publishing a lot of papers over one or two years, one paper with qualitative research is more preferable

To master research in both academia and industry: 1. **Academia**: Focus on advancing knowledge, acquire research methodologies, and develop critical thinking skills. 2. **Industry**: Prioritize practical results, collaborate with cross-functional teams, and adapt to business needs. Remember, both paths offer valuable experiences!

In academia the research doesn't need to produce a product or be relevant to the real world. In industry you need to produce a product that can be used and is desired by the real world. The tools are the same but the end result is quite different.