Reports suggest that around 2.5 quintillion bytes of data are generated every single day. As the online usage growth increases at a tremendous rate, there is a need for immediate Data Science professionals who can clean the data, obtain insights from it, visualize it, train model and eventually come up with solutions using Big data for the betterment of the world.
By 2020, experts predict that there will be more than 2.7 million data science and analytics jobs openings. Having a glimpse of the entire Data Science pipeline, it is definitely tiresome for a single human to perform and at the same time excel at all the levels. Hence, Data Science has a plethora of career options that require a spectrum set of skill sets.
Let us explore the top 5 data science career options in 2019 (In no particular order).
Data Scientist is one of the ‘high demand’ job roles. The day to day responsibilities involves the examination of big data. As a result of the analysis of the big data, they also actively perform data cleaning and organize the big data. They are well aware of the machine learning algorithms and understand when to use the appropriate algorithm. During the due course of data analysis and the outcome of machine learning models, patterns are identified in order to solve the business statement.
The reason why this role is so crucial in any organisation is that the company tends to take business decisions with the help of the insights discovered by the Data Scientist to have an edge over the company’s competitors. It is to be noted that the Data Scientist role is inclined more towards the technical domain. As the role demands a wide range of skill set, Data Scientists are one among the highest paid jobs.
BI Developer is a job role inclined more towards the Non-Technical domain but has a fair share of Technical responsibilities as well (if required) as a part of their day to day responsibilities. BI developers are responsible for creating and implementing business policies as a result of the insights obtained from the Technical team.
Apart from being a policymaker involving the usage of dedicated (or custom) Business Intelligence analytics tools, they will also have a fair share of coding in order to explore the dataset, present the insights of the dataset in a non-verbal manner. They help in bridging the gap between the technical team that works with the deepest technical understanding and the clients that want the results in the most non-technical manner. They are expected to generate reports from the insights and make it ‘less technical’ for others in the organisation. It is noted that the BI Developers have a deep understanding of Business when compared to Data Scientist.
Once the data is clean and ready for analysis, the machine learning engineers work on these big data to train a predictive model that predicts the target variable. These models are used to analyze the trends of the data in the future so that the organisation can take the right business decisions. As the dataset involved in a real-life scenario would involve a lot of dimensions, it is difficult for a human eye to interpret insights from it. This is one of the reasons for training machine learning algorithms as it easily deals with such complex dataset. These engineers carry out a number of tests and analyze the outcomes of the model.
The reason for conducting constant tests on the model using various samples is to test the accuracy of the developed model. Apart from the training models, they also perform exploratory data analysis sometimes in order to understand the dataset completely which will, in turn, help them in training better predictive models.
The pipeline of any data-oriented company begins with the collection of big data from numerous sources. That’s where the data engineers operate in any given project. These engineers integrate data from various sources and optimize them according to the problem statement. The work usually involves writing queries on big data for easy and smooth accessibility. Their day to day responsibility is to provide a streamlined flow of big data from various distributed systems. Data engineering differs from the other data science careers as in, it is concentrated on the system and hardware that aids the company’s data analysis, rather than the analysis of data itself. They provide the organisation with efficient warehousing methods as well.
Business Analyst is one of the most essential roles in the Data Science field. These analysts are responsible for understanding the data and it’s related trend post the decision making about a particular product. They store a good amount of data about various domains of the organisation. These data are really important because if any product of the organisation fails, these analysts work on these big data to understand the reason behind the failure of the project. This type of analysis is vital for all the organisations as it makes them understand the loopholes in the company. The analysts not only backtrack the loophole and in turn provide solutions for the same making sure the organisation takes the right decision in the future. At times, the business analyst act as a bridge between the technical team and the rest of the working community.
The data science career options mentioned above are in no particular order. In my opinion, every career option in Data Science field works complimentary with one another. In any data-driven organization, regardless of the salary, every career role is important at the respective stages in a project.
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The term ‘Data Science’ has become a buzzword in the past couple of years. A lot of people who work in various domains such as IT and Business wants to make a shift to this new career option. Even people with a lot of experience as much as 15 years want to make a career shift towards Data Science. Apart from the fact that the domain has now become one of the most popular domains relatively, let us look into what it actually takes to make a career shift towards this data-driven domain. But first, let us dive into the skills that a Data Scientist would require.
The above shown Venn Diagram shows the perfect mix of skill set that one needs to acquire to become a successful Data Scientist. Data Scientist, being one of the highest paid jobs in recent times, requires a wide spectrum of skill set. Data Science is a domain which demands an ideal mix of both Technical and Non-Technical skills.
A day to day role of an ideal Data Scientist is to coherently work with both the Technical and the Non-Technical team. In fact, a Data Scientist bridges both the team thereby playing a very crucial role in any Data Science project pipeline. Hence, a Data Scientist requires a strong domain knowledge so as to not only understand the problem statement of the client but also understand the technical feasibility of the problem with the technical department. For example, if a model has to be trained to detect the type of cancer in a person, it is crucial to know the correlation of the features in the dataset with the target variable. It will help in using only the most important features to predict the same thereby increasing the accuracy of the model.
Mathematics is the backbone of the Data Science domain. Any Data Science role would require a strong mathematical foundation. Probability and Statistics are an integral part of Exploratory Data Analysis and Machine Learning. It is important to note that, a data scientist will be spending around 10% of the entire time solving mathematical problems working on the project. Since all the algorithms are based on mathematics, having a mathematical foundation is usually to understand the various algorithms that will be implemented to solve the business problem. Although most of the machine learning algorithms can be applied even without a strong mathematical foundation, having a strong mathematical base will definitely help in understanding the nature of the model and improving its accuracy. So, mathematics is definitely used at some point in the data science project.
Most of the data science job roles will require programming skills that are related to the domain. All the technical work carried out right from data cleaning, data analysis to implementation of the appropriate machine learning algorithms is carried out using a programming language (Python or R). Apart from this, having a general knowledge of how a database such as SQL will be really useful. Having basic knowledge of object-oriented programming will reduce the Data Science learning curve. Programming is a vital skill but one need not necessarily have a strong background on programming.
Now the most common question that everyone who wants to start a career in Data Science is:
“Do I have to be a master of all the major knowledge domains?”
The answer is No! Data Science is not just about having technical knowledge. Being a domain related to both the Computer science world as well as the Business world, the latter has a fair share of skill set that is very vital for becoming a data scientist. In fact, non-technical skills that are mentioned below arguably sum up to 60% of the work as a data scientist. These are skills that were not mentioned in the Venn diagram but are equally important in any ideal data-driven project.
There is no purpose in just cleaning the data and getting insights from it as such. The insights will have a purpose only if the business problem has been identified and understood thoroughly. Business awareness is closely related to Domain Knowledge. In some cases, a person with high domain knowledge will be better recruitment for a company than a highly proficient technical engineer. Hence being a business acumen will enable a data scientist to be creative in analyzing the data to make better decisions.
An ideal data scientist will have to understand the technical nuances during the project. But it is not necessary that the client has to understand it. As a data scientist, it is necessary to have solid communication skills to explain the results of technical progress in terms of a layman as well as collaborate with the technical team given any point of time during a project. Data Storytelling is more important than obtaining insights from the data itself. There can be a lot of mind-blowing trends analyzed in the dataset but if the story-telling is not done properly ( if the result is not conveyed properly), the whole purpose of data analysis diminishes.
Data Science projects are usually carried out by a group of people as a team. Every individual will be working on different parts of the project pipeline. Therefore, it is essential that every individual work coherently with every other team member. Each and every role right from Data Analyst to Machine Learning engineer will have to work in a complementary manner. Data Science projects require a lot of creativity and only a collaborative team will be able to perform successful brainstorming sessions and obtain fruitful insights out of the data.
Now that the major skills for a data science role have been understood, there might arise a question as to — what should be the level of expertise such as programming?
In layman terms, programming is a way which is used to communicate to the computer to enable it to perform a certain task. It is as simple as that. The level of expertise in programming in a typical computer science role will involve complex data structures complimented with much more complex algorithms. So, is programming required at that level of expertise? The answer is No! Although it is amazing if a data scientist has a deep understanding of data structures and algorithms, an ideal data scientist will not necessarily be working on complex data structures for most of their time. The main goal of a data science role is to understand the syntax of the relevant programming language (say Python or R) and try to implement the mathematical concepts using the predefined functions available in the language. Learning to do so can be achieved with minimal efforts by someone even from a non-technical background who aspires to make a career shift towards data science.
“The more you know, the better it is”
In my opinion, data science is a field for everyone. From an application developer to a businessman, everyone will have a base skill set that enables anyone to start a fresh career in Data Science. Even those who do not want to learn to programme can hone their strengths in their business or mathematical department and still be a part of this wonderful domain. At the end of the day, a sense of problem solving and commitment is all that one will need to excel in any given situation.
Follow this link, if you are looking to learn data science online!
You can follow this link for our Big Data course, which is a step further into advanced data analysis and processing!
Additionally, if you are having an interest in learning Data Science, click here to start the Online Data Science Course
Furthermore, if you want to read more about data science, read our Data Science Blogs
