The queer thing about the field of data science is that both the number of vacant positions in data science roles and the number of people with data science certifications have been increasing simultaneously. What does not add up is the fact that there are many certified candidates who are unemployed even though there are positions to be filled. This is where the quality of training, the relevance of curriculum, the choice of tools, and most importantly the correct and compelling application of the earned skills, come into play. If you are planning a career in data science you need to know certain things upfront. Here is a fatless list of skills that you need to succeed in data science today. These lists get out dated pretty quickly considering the pace at which the industries are evolving. So, keep a tab on AI and machine learning trends to watch out for.
You may have come across ads in the line of ‘this tool does data science for you’ or ‘become a data science expert without knowing a letter of statistics’. The hard truth is if you are doing data science without statistics, you are probably doing something else. Whether you are trying to translate a business situation into a tangible problem, or trying to create a simple model to find a solution to that problem, you cannot move an inch without statistical concepts. Even if you are not a master of linear algebra and matrix vectors, you need a foundation in statistics. Do not let anyone fool you. If there are tools that eradicate the need for data scientists to know statistics they will probably also eliminate the need for data scientists altogether.
As a data science professional of any serious note, you will need to code, especially if you are working with applied data science. We are not here to learn why Python as a coding language is better than most others if that is the case at all. Let us think of it as choosing a popular option which is in rampant use across various industries. In fact, if you conduct data science using Python, the preexisting Python libraries like SciPy, Scikit learn, and matplotlib, can come really handy. There are actually a whole bunch of Python libraries dedicated for use in data science. These libraries come with preloaded code for certain statistical and numerical functions which are in regular use.
Multivariable calculus and linear algebra
A company which uses data to modify products and strategies can gain a lot of value through small tweaks in algorithms in predictive models. Multivariable calculus and linear algebra play a pivotal part in making these adjustments. While it is totally fine to use preset implementations in tools like R and Python, you should be able navigate your way through these areas of mathematics. You may be asked questions related to these in interviews. A company may decide to create its own implementations, or if you are working with neural networks at some point you may need to manually tweak the dials and knobs to set parameters.
Data is at the heart of all data science processes, of course. This data comes in pretty bad shape more often than not – unclean, unstructured, scattered, hence, unusable. Data wrangling refers to a set of procedures that clean the data and prepare it for the data science models. This is different from data mining as the latter refers to a larger set of tasks. Of course, data wrangling can be used within the context of data mining but not the other way round. You, as a data science professional need data wrangling skills because only a handful of companies will have a different person doing it for you.
Effective communication skills
This includes explaining a problem, expressing the probable solution, and demonstrating the possible method and plan, to a group of people who have very little or no idea about what it is that you do. This is just one part of it. You also need good habits like taking notes while speaking to stakeholders, carrying notes with yourself while meeting them so that you do not miss important stuff. Data visualization is kind of an extension of this skill set, hence we will talk about that a little. Your work finds meaning through data visualization – it works as proof that you are actually making a significant contribution. Few would notice the work done by data scientists if it were not for the compelling visualizations.