What’s common between data scientists and business analysts? Both of them play with numbers. They’re both masters of calculations. They work parallelly to bring value to their respective industries. And the main objective of both professions is to grow business incredibly. Their jobs require them to do predictions and analysis. Let’s first discuss what their job profiles are exactly are their job profiles
A data scientist is a coder. A genius in statistics and mathematics, who finds conclusions based on statistical calculations. He’s mastered advanced tools like SQL and has in-depth knowledge in data visualization. Business analysts take care of all the requirements that could increase the productivity of the business. A business analyst expert might not be as technical as a data scientist. But they must possess a deeper understanding of all the business technicalities. He acts as a bridge between the IT and development teams. While both these jobs use different tools and skills, their work is essentially similar ౼ to predict the future growth and trends of the business.
The working format of both these jobs is quite tedious. A project requires numerous iterations that become time-consuming. When dealing with numbers and data interpretations, it goes without question that you have to be quite smart and proactive.
It’s not surprising that iterations can be frustrating if they require regular updates. Sometimes the model is six months old that needs current information or other times you miss out on some data, so the analysis has to be done all over again. In this article, we will focus on the ways by which the Data Scientists and Business Analysts can increase productivity without spending so much time on unhealthy iterations.
1. Focus on the big picture
Long-term goals should be considered a priority when doing the analysis. There could be many small issues rising up but that shouldn’t outcast the bigger ones. Be observant in deciding the problems that are going to affect the organization on a larger scale. Focus on those bigger problems and look for stable solutions. A data Scientists and Business analysts have to be visionary to manifest solutions.
2. Begin with an easy-to-understand presentation
Once you start working on a project, create a presentation either in a form of a layout or a simple word document. A proper presentation of your analysis will not only reduce your effort but will also balance the time effectively. A rough drawing of all the possibilities in your analysis could be of great help. You can always refer to that as your project outline.
3. Set up your data requirement beforehand
This is your next step after your presentation is ready. This could be done systematically:
- Collect all the required variables in advance that you might need in the future. This costs some extra time but you will have access to the entire variables in the data set.
- Give your data a structure instead of just listing them down on a paper. You can draw a table or a graph to showcase your data for analysis.
- Set the time frame of the data you will use.
4. Brush up on your statistical skills
This is a game of numbers and we might tend to forget some important formulas while we are using it in the live scenarios. It’s always better to revise your concepts and understand the theories by heart. You will never get stuck if your concepts are clear. The trends keep on changing, but a mathematician must know his calculations thoroughly to analyze these trends and outcomes. Being a student, aspiring to become an analyst, you may also do some crash course to learn to code. Data Scientists is a profile in demand and students often find it challenging in the initial phase only. To complete your dissertation work for a university degree in Data Science, you can take expert help who will make you understand the concepts brilliantly. These experts provide the best dissertation analysis enabling you to excel right from your college.
5. Keep your coding library ready and up-to-date
You cannot always go back to books to search for the standard codes that have to be used. Create a document or a file containing all the standardized codes and refer to them whenever required. This could avoid any syntax error. Share this document with your entire team to assure productivity and save time. This practice is going to land you all on the same page of codes.
6. Cross-validate your data to avoid over-fitting
Separate the data into two sets ౼ the training set and the testing set to get a stronger prediction of an outcome. Cross-validation is the most convenient method to analyze numerical data without over-fitting. It examines the out-of-sample fit.
7. Always log and keep a back-up of your work and data
You can take help of a notebook here to maintain the flow. This is an important step to make sure that your outcome is reproducible. This means along with the final result, the codes, computational environment, data etc. are intact. And, based on your work, you can either reproduce the final results or conduct new research. Also, if you happen to lose critical information or analysis, you always have a back-up to start again.
8. Take frequent breaks to help increase focus
As data scientists, your mind has to be always active. You cannot perform so many calculations in a go. Divide your time and work in such a manner that you get time even to rejuvenate. In order to focus on one specific project, take regular intervals in between. Work in chunks may be 2-3 hours’ window to work and then relax for a while.
This isn’t tough at all. I would rather say juggling with data and analyze the trends in an interesting work to do. What is required is to be prompt and acute. These are just some super hacks to ease your work. Above all, it is your knowledge that counts in this field. The more technically sound you are, the more apt your prediction would be. Befriend numbers and you are all set to achieve the target.