Data science appeared not so long ago but took a great place in analysis and forecasting nowadays. Data existed always and everywhere, but there was no instrument before computers that could collect it and count it as quickly as necessary. The main data knowledge of previous ages was the number of citizens who pay taxes, the number of workers on your fields, the number of acres of your fields and gold you have in the bank or the dower chest. Even for these modest operations, there was a separate person who counted all the goods and was sometimes more respected and influential than his master. 

Times changed, data changed and the speed of information exchanged increased extremely. For the data scientist the main task is not to count (because machines do it quicker and more exact) but to ask the right questions and make logical conclusions after the results he got. If you feel excited after reading these words above, then you may be in love with data and believe in it like it is the only way for truth and clearness. Unfortunately, without technical skills, it is impossible to enter the profession of Data scientist so let’s make a list of such skills and find out how to improve them.

Ways to Strengthen Science Technical Skills

Is there such a thing as ‘too strong science technical skills’? We don’t think so, because tech develops like crazy and there is no way you can ever get overqualified. Any tech, robotics, AI, programming related profession requires mad tech skills and constant development. Fortunately, there is more than one way, more than one approach to develop technical skills.

  • Logical, structured, critical thinking. Let’s start with the skills given from birth. Logic, planning, building step-by-step instructions and noticing obvious connections between facts are necessary for the future data analyst. These skills must be developed and upgraded to call them technical, but that is what you should start with. Try to analyze if you are ready for such a job or not. Every day the world wide web gathers billions of bytes of information about its users, so someone needs to say what is going on with our world.
  • Statistics. Building models with a lot of variables, forming and testing hypotheses, performing regressions, etc. If you think that these are common words, then you are wrong. These all are exact terms of simple statistics that use powerful mathematics instruments to work with data, considering dependent and independent variables. Probability theory and mathematical statistics are hard subjects for understanding that are available only in universities.  
  • Basic knowledge of any programming language or Excel. Knowing the theory of statistics is necessary but not enough for successful analysis. When you choose the model, you must know how to put the data on it and get the appearance of the result that will satisfy you. For beginners, advanced Excel will be enough. It has a programming language inside of it and built-in basic statistical functions. For more serious analysis, you need more powerful and multipurpose instruments that work with spatial tables. 
  • Instruments of data visualization. The next step is to visualize the result. Almost all the time, you have a customer (even inside the company) who requires the answers based on data. That’s why your final goal is to show them the dependences and numbers you discovered. Even if the table is enough for you and you can read it without any picture or chart, it might be impossible for any other person who is not in love with data but wants to use its power.
  • Presentation skills. For technical specialists, this skill is always hard and sometimes even the hardest. After they finish their job and believe 100% in their results, they don’t understand what more they need to present. What should they prove? Math already proved everything! If the hypothesis was wrong, then the result would show it. Unfortunately, business doesn’t work like this and sometimes wrapper makes more effect than content. So if you want to be heard, then you need to work on presentation skills hard.
  • Being open to different points of view. You should be ready for discussions. Your point of view can differ from other people and even if you have logical proofs that you found with statistics, it doesn’t mean that you can win the fight. An obvious skill for every person who communicates not only with machines is the ability to compromise. This is the only way that leads the whole team to great results. Try to add to your model some comments and advice, and analyze how it changed the results. All the members of the discussion want to be a part of something huge, so don’t ignore them.  
See also  List of blues for the Olympics, with Tavin and Kignok

How to Balance Skills and Achievements

In the perfect world everything is simple. You develop a skill set and your achievements skyrocket. You get 10% more advanced in some discipline, and immediately assignments you do get at least 10% better. In reality it happens differently. You spend extra time developing some skills and at the same time you have to deal with lots of tasks. To balance the whole process, address and pay a reliable service MyAssignmentLab to get your assignments done by experts. Invest your time in science technical skills strengthening and let experts from this professional STEM academic help companies deal with your assignments for you. Focus on your goal and don’t neglect getting some extra assistance along the way. 

That is almost all that you need to be a successful data scientist. Seems impossible or extremely hard. However, it is not less hard than to become a top specialist in every profession. Data scientists work with real things that are possible to feel and estimate, but they use methods that came from deep theoretical science that are not simple and obvious. If you feel the calling to become a member of these magicians and have some or all skills above, then data science will become your lifestyle but only the job.

LEAVE A REPLY

Please enter your comment!
Please enter your name here