How to Become a Great Data Scientist
The story of the 21st century is that of data taking over basically every part of the corporate world. To manage this magnitude of data, data scientists are highly needed and so the job of a data scientist is a very lucrative one. One main reason why it is so lucrative is because of the shortage of highly skilled data scientists. The fact that companies, regardless of their size, are willing to do away with a lot of their revenue to get the right scientist is not so farfetched. However, to be qualified for the position of a Data Scientist, you need to prove to these companies that you are the best for them, and to do this, you need a lot of skills.
Ok, let us get more specific. Why is it that most HR departments in companies prefer resumes with exceptional data science skills? All they care about is revenue right? The truth is data scientists make global industries reach far more customers than they will do without data scientists. The job of these professionals is to transform random data into useful information. If you are aspiring to be a data scientist or you are a data scientist, here are the top 10 skills you need to be a great data scientist. These skills are generally technical and non-technical, but aren’t necessarily grouped as such.
To be a data scientist, you have to be highly educated and possess a top notch fundamental background. This is because you need adequate knowledge to develop the depth of skills necessary to be a data scientist. Most people will argue that a degree program is not necessary but you will most likely find data science as a profession easier if you earned a Bachelor’s degree in either of the following courses; computer science, social sciences, statistics, engineering and other fields. A bachelor’s degree is not the highest certification however, some of the top data scientists in the world have a Master’s degree or PhD. They also tend to undertake online trainings to always get their hands on the latest trend in the field. Education can mean different thing to different people however, but the main reason why you need an education is not because of the certificate, it is because in the course of going through a program, you tend to develop skills that will help you in data science.
R is an analytical programming language that has been in existence for about two decades. Most data scientists of the older generation prefer to use R but the current generation prefers python. However, being skilled in R is still an important first step to take if you intend on becoming a data scientist. With R, you can solve a lot of problems from data visualizations to data mining. It is a very broad tool that takes years to master and it is a very difficult language to learn if you aren’t already familiar with other programming languages. Nonetheless, there exists a lot of resources on the internet that can help you get started in R.
This is probably the foremost requirement these days in a data scientists’ job description. It is a great language for data scientists and when compared to R, it is flexible and easier to learn. You can use python and other built in libraries for almost every task that concerns a data scientist. You can use it to work on different types of data and you can easily integrate it with SQL tables. This is why it is very important for anybody that wants to be a good data scientist to have a working knowledge of Python.
Structured Query Language (SQL) is a programming language that is used in carrying out database operations like adding, deleting and the extraction of data from a database. A data scientist armed with SQL skills gets to carry out analytical functions and also execute the transformation of database structures. As a matter of fact, proficiency in SQL is a requirement for data science experts. This is because the language is specifically designed to help the expert mine, clean and work on data. It provides insights when it is used to query database and it has concise commands that will help you save time you would have otherwise expended while performing difficult queries.
Curiosity in simple terms is the desire to acquire more knowledge. To be a highly skilled data scientist, you need to be able ask yourself and other experts questions. In fact, the work of a data scientist is questioning what nobody is questioning. We also live in a world that is evolving at a very fast pace, you will get left behind if you are not intellectually curious. There is need to always update your knowledge, this you can do by reading, attending conferences and seminars on data science. You do not have to take everything in; you have to be able to curate the contents that are made for you alone. Curiosity will help you to sift through piles of data to find something that nobody is seeing.
Companies looking for a strong data scientist are looking for someone who can translate their technical findings clearly and fluently for the benefit of their non-technical teams such as the marketing and sales departments. A data scientist must enable the company to make choices armed with quantified ideas and information. A data scientist also needs to interact using informative storytelling, as well as speaking the same language the business knows and understands. As a data scientist, you must know how to simplify data for anyone to understand. For example, it’s not as effective to present a table of data as to share insights on the data in a storytelling format. Using storytelling helps you to communicate your results correctly to your employers.
Beware of outcomes and values incorporated in the information you analyzed when communicating. Most company owners don’t want to understand what they evaluated; they want to understand how their business can have a positive effect.
A data scientist requires a good understanding of statistics. You need an understanding of distributions, statistic tests, estimations of highest probability, etc. Statistics/Mathematics is crucial for all kinds of companies, but are more important in data-driven companies. Statistics are not only crucial in decision-making but are also used to assess progress and growth.
Linear Algebra and Calculus
Capturing ideas is essential for businesses where the information defines the essence of the product, and optimizing algorithms or improving the predictive output tally can contribute to the company’s achievement. Your interviewer may ask a few basic linear algebra questions or multivariable calculus for an interview on a role in data science. Or, some statistics or machine learning findings that you execute elsewhere will be requested.
Digital Marketing Analytics
Every community today; large and tiny, aspires to go digital. With this being the trend, founders and CEOS are hunting for digital marketers to analyze customer data and gain insights from such analytics thus creating a gripping digital strategy and measuring the return on investment (ROI) achieved against the Key Performance Indicator (KPI). Experts now feel that it is necessary to go beyond metrics and numbers and acknowledge the primary problems including website optimization and analytics of social media.
While this is not always a necessity, in many instances it is strongly preferred. It’s also a powerful selling point to have experience with Hive or Pig. It can also be useful to familiarize yourself with cloud instruments like Amazon S3. As a data scientist, you may find that the amount of data you have exceeds your system’s memory or that you need to send information to separate servers, that’s where Hadoop comes in. You can use Hadoop to quickly convey data to various points on a system. That’s not all. You can use Hadoop for data exploration, data filtration, data sampling and summarization.