Data science is a rather new entrant in the market. As a lucrative path, it witnesses a plethora of takers who are interested in launching their careers and create a niche for themselves. It has only recently emerged as a separate subject, which led to many, in the current professional scenario, missing out on intensive training. However, this does not equate to an end, as many are accepting data science as a new career path.
Deemed as the most sought after career option of the 21st century by Harvard Business Review, data science is a tempting option to choose, but due to its sheer popularity, it is a tough path to break into. However, with the right guidance and education, it shouldn't be so hard.
So how does one switch careers into data science? Let us break it down into simple steps.
Define your goals
A career transition is not an easy decision to make, nor is it an easy path to take. Planning and understanding of the new field are incredibly important. Before taking the plunge, ask yourself your reasons for considering this switch. What is the vision behind this plunge? Is it short-termed or long-term planning? Answering these questions truthfully can help clarify a lot.
Cultivating the right skills
The conventional image of a data scientist is a bespectacled guy crunching numbers and creating algorithms. However, the role of a data scientist more than that. Since it involves data, he/she should be able to warm up to the idea of constant learning and making data-driven decisions.
A data scientist must be equipped with the core technical skills, involving machine learning, statistics, analysis, and even having an in-depth understanding of Apache and Hadoop. Let us elaborate a bit.
Python is a versatile language which can comfortably accommodate the numerous steps involved in the data science processes, making it a vital programming language. It can take up different formats of data, and you can seamlessly import the SQL table with the help of this code. Thanks to python, data scientists can create a plethora of datasets.
Although, data scientists are not always proficient in ML, involving neural networks, adversarial learning and many others, however, to be a class apart, it is a skill worth investing in. Machine learning techniques encompass concepts such as decision trees, supervised machine learning, time series, outlier detection, recommendation engines, reinforcement learning, computer vision, adversarial learning and logistic regression to name a few. These advanced elements can help resolve complex data structures that are usually dependent on predictions of outcomes.
As a data scientist, you may encounter a condition where the volume of data you have outperforms the memory of your structure, or you need to send data to different servers. This is the spot Hadoop comes in. You can use Hadoop for data examination, data filtration, data analyzing and once-over.
You should be capable of SQL as an information researcher. This is because SQL is explicitly intended to enable you to get to, impart and deal with information. It gives you experiences when you use it to question a database. It has brief directions that can assist you with saving time and diminish the measure of programming you have to perform troublesome questions. Learning SQL will help you better comprehend social databases and lift your profile as an information researcher.
Organizations produce a copious amount of data that need to be analyzed and formatted frequently to understand the ongoing trends or preferences. To comprehend this data, it needs to be in an easily understandable format. Data visualization can help simplify this complex data and present in a more user-friendly version of visuals. These tools are indispensable when it comes to converting the unstructured raw data into something more legible. Knowing this element can help save time and make informed decisions.
Apache Spark is taking shape as one of the most prominent big technologies. Like Hadoop, Spark too is a big data computation structure. The difference is that Spark is much faster than Hadoop. This is because Hadoop details down data on disks, making it slower, however Spark performs its calculations in memory.
Apache Spark has been designed to speed up the algorithm calculation by breaking down the large data ocean into understandable chunks, thus, saving time. It additionally empowers the data scientist to deal with complex unstructured informational collections. You can utilize it on one machine or a group of machines.
Apache sparkle makes it feasible for information researchers to avoid loss of information in information science. The quality of Apache Spark lies in its speed and stage, which makes it simple to do information science ventures. With Apache flash, you can do investigation from information admission to circulating processing.
It is crucial to understand the vitality of raw data. Unstructured data is available in raw forms like blogs, audio content and others, which cannot be fit into current datasets but are essential to understand the user behavior. It helps unearth insights that can be vital for decision making. Understanding unstructured data can help data scientists gather information from various platforms and build it up for something important.
In addition to excellent technical skills, a good data scientist needs to be an efficient communicator, should possess off-the-charts critical thinking abilities and problem-solving abilities. These skills are more organic and cannot be learnt like formulae and theorems. However, there are skilled and experienced resources available online who can help achieve this abstract learning and boost your skills efficiently.
Dabble in data science in your current role
Since you are already working as a professional, you can try honing the data scientist in you by bringing in a bit of data science in your current vocation. Since data science calls for a professional to have a managerial perception, those with a business background can quickly fill the role.
Most data-driven projects are run by organizations that are looking for multi-disciplinary teams, and to lead these teams, managers with data science acumen are the perfect fit for amplifying project execution and driving decisions based on data.
On the other hand, those in tech roles need to extend their technical finesse and accommodate data science in their work which fine-tunes their expertise for the better. These small adjustments make the eventual transition much more straightforward. Professionals can also attend mentorships program and workshops to get a headstart into the data science world.
It is the blend of technical skills and business acumen, which can help data scientists understand their core responsibility and help organizations make data-backed decisions for overall growth and progress.
Have you made the switch to data science, and would like to share your journey? Leave us a comment, or write in to us. We'd love to hear from you!
Or if you are looking forward to entering the Data Sciences, Check out our course on Data Science here.