An average enterprise generates large amounts of data every day with hidden business insights. For that reason, a data scientist is required to tap into that potential and make sense of all that data. But what is a data scientist?
In today’s global and fast-paced day and age, there is a dire need for businesses and organizations to micro-manage any form of feedback or information they receive in order to stay updated with their systems, make informed decisions, and use that information to their advantage. A data scientist can help with that.
However, there’s still a lot of confusion surrounding this role and people often confuse them with data analysts.
In this article, we’ll break down the job of a data scientist, what it comprises of, and its significance.
Let’s get started.
What is a Data Scientist? [Role Overview]
A data scientist is an analytical professional who specializes in handling big data. They are primarily responsible for managing all the useful information that a business generates. That entails collecting the data, organizing it, and analyzing and processing it to extract insights.
The analysis then further helps the business in decision-making that is aligned with previous trends in order to generate specific reactions from consumers or clients.
Data scientists are competent in mathematics, programming, and statistics, in addition to data management and visualization. They also have a strong business acumen and should be able to convey their findings to the key business stakeholders effectively.
In addition to the mentioned tasks, they use and create algorithms for the optimization of their data analysis.
The work of a data scientist is not limited to these processes. Their foundational tasks can be put in a more comprehensive manner in the following terms:
- Identification of valuable data and data sources
- The collection of said data through different channels
- Data pre-processing includes cleansing, reduction, editing, and wrangling of structured and unstructured data
- The analysis of big data in order to locate trends and responses
- The construction of predictive models and algorithms
- Application of statistical analysis, machine learning, and AI (Artificial Intelligence)
- The presentation of sifted data via techniques for data visualization
- Utilization of all previous processes to come up with effective strategies and proposals for business decisions in accordance with their trends
We’ll dive deeper into the exact job description to better understand what a data scientist does.
What Does a Data Scientist Do?
The exact job description of a data scientist varies from company to company.
But there are certain key duties, roles, and responsibilities that all data scientists share.
Let’s go through them one at a time:
Data Structuring and Management
A data scientist plays their role in the structuring and management of a business or an organization as they lay the foundation for corporate decision-making and the advancement of a place.
Their role is fundamental as they enable the decisions regarding what data collection and data preparation, and in which direction the company needs to progress at a particular point in time according to the extracted useful information.
For this purpose, one of their main responsibilities is to prepare, structure, and manage the data. This mainly involves data mining and wrangling.
In data science, the extraction of information from various extensive structured and unstructured sets of data is known as data mining. This is an important component of the job of a data scientist.
Data wrangling is a subset of data mining and in particular, is a process in which data science unifies and organizes previously mentioned various structured data sets and unstructured data sets. Data scientists manually transform and map that raw data into different formats.
The converted data is accessible and comprehensive and is more useful as it can further be utilized for machine learning and data analytics.
Analytics and Data Visualization
Another core responsibility of a data scientist is analyzing all of that data and extract meaningful insights.
They do this by constructing and employing statistical models, and then base decisions on predictive analytics.
It also involves visualizing the data in a meaningful format and presenting their findings to the concerned stakeholders. Their goal here is to visualize the data in a way that’s easy to understand and doesn’t require deep technical/mathematical expertise, while successfully delivering the intended insights.
Strategizing and Collaboration
Smooth communication is a significant part of a data scientist’s job. As mentioned above, they don’t just have to discover solutions but also have to propose their validity and usefulness in a clear, concise, and accurate way.
For this purpose, they have to combine their business knowledge with their technical expertise. Regardless of where they are employed – whether public or private sector – they have to come up with insights that move the needle and drive things forward.
This involves the analysis techniques that we mentioned earlier, which can include segmentation analysis and customer profiling.
In addition to applying their technical skills, data scientists also need to collaborate with their seniors and the stakeholders to come up with the most high-yielding results.
It is the responsibility of the data scientist to convey their analysis and this can be done through visualization and illustration techniques. Furthermore, collaboration with data engineers, the IT department, informatics analysts, and various other data analytics teams are also expected.
A data scientist’s job involves a certain level of risk-taking and experimentation when it comes to their required skills and technologies.
Technology is an ever-evolving subject that improves beyond comprehension in short periods of time. Data science combines technology with business. There is a dire need to remain up-to-date with current trends in order to produce optimal results and relevance.
Therefore, a data scientist is responsible for keeping their knowledge vast, adaptable, and updated in order to do their job well and continue making an impact in their company.
Required Skills of Data Scientists
Data scientists are required to have a very specific skill set pertaining to the development of the world revolving around big data.
Following are the areas that data scientists are expected to be proficient in:
Knowledge of Programming Languages
Data scientists need to have a strong grasp on skills such as writing computer programs and utilizing programming languages in order to analyze big data.
They need to be fluent in writing code in a plethora of programming languages that include Python, R, SQL, and Java.
Python is the most flexible language, therefore, is more popular in this field, and SQL on the other hand is domain-specific designed for processes in a relational database management system. Data scientists should have strength in the knowledge of the employment of coding languages relevant to the process.
Management of Big Data
The rate at which data is being produced and collected is far beyond normal, manual capabilities. Therefore, it is compulsory for someone pursuing a data science career to have proficiency in certain software that aid in the tasks for an organizational framework.
The Hadoop platform is a collection of software utilities that are open-sourced and make it accessible for data scientists to process large data sets through non-complex models of programming across multiple computers.
In addition to Hadoop, data scientists should also be proficient at SAS – a statistical software suite that aids in data management, business intelligence, predictive analytics, and various other processes.
Data scientists are required to have knowledge of these data science programs so that they may execute their fundamental tasks as efficiently as possible.
Along with the management of big data and its relevant data science skills, data scientists are also required to be proficient in tools that aid in other tasks, such as the presentation of data. In this case, having proficiency in Tableau or any other data visualization tool is a must.
Machine learning and Artificial Intelligence
Machine learning is a core skill for a career path in data science. It includes the analysis of large chunks of data through predictive models and algorithms.
Predictive models that define a part of the analysis of data and information are developed via machine learning algorithms through observing historical data.
As a data scientist, you have to employ predictive modeling techniques such as regression analysis which is used to investigate the relationship between a dependent and independent variable in order to forecast behavior.
Machine learning also has a further developed subset called deep learning which is a preferable skill. Deep learning is a function in which data is processed through layers of non-linear transformations so that you may be able to find meaning in it.
Strong Math Skills (Statistics, Probability, and Calculus)
A wide range of data science elements relies on the principles of mathematics for executing certain tasks such as constructing models.
Algebraic calculations are necessary and along with that, statistics and probability are essential requirements in order to adopt a data scientist role and be able to work with large sets of data, their organization, and their presentation.
Because a data scientist’s job is to consult large sets of data in order to organize and find meaning from it, they act as advisors and consultants for the business.
Their competence in the advisory is derived from their observation of a situation which could perhaps be a particular trend noticed through information from the data, and the presentation of that observation.
But observation cannot stand on its own therefore, it comes alongside the need for improvement or a technique for the preservation and maintenance of existing trends that prove to be profitable. Therefore, a problem-solving aptitude is required from a data scientist who has to tackle obstacles in order to produce as positively streamlined of a report as possible.
Required Qualifications for Data Scientists
In order to become a data scientist and build a career in this particular trajectory, at the very least you need to have an undergraduate degree in data science or a degree in the field of computer science. An undergraduate degree is required for an entry-level position as a data scientist.
Further than that, a master’s degree boosts your chances of having a well reputable job in this field. Although, many particular careers in data science require you to have a master’s degree at the very least. A degree is required in order to form a network and intervene structure into your academic qualifications.
According to data from 2019 provided by Burtch Works, over 90% of data scientists have a graduate degree.
On the other hand, an alternative to the situation of an academic qualification, you can enroll yourself in a data science boot camp or take online courses relevant to data science that can be considered supplementary to your knowledge for the relevant field.
Moving even further ahead, data science jobs prefer employees to have a Ph.D. since it is exceptionally useful not just in the industry but in data science academia as well.
A PhD enables you to be an applied researcher and with a PhD degree, you will be able to execute mathematics and theory in a highly proficient manner, and comprehend what other data scientists communicate in their research.
Becoming a Data Scientist in 2021
The scope of a job in data science in 2021 can be determined through a walk-through of its demand, supply, and growth patterns in recent times. As mentioned in the beginning, in this global age, companies and organizations are increasingly data-driven and so are their decision-making factors.
Almost every step they take is based on information that has been processed, poked, prodded, and analyzed.
Almost all big companies of the world such as Amazon, Google, and Facebook are fueled by data science as they have mapped out their networks in a way that they receive an incomprehensible amount of data every single second.
This settles the debate about the demand for data science jobs as it is clearly a booming industry and even more so as society progresses toward a hyper-vigilant trajectory where everything and everyone’s data is recorded and stored.
And to balance, the booming demand with the scope of the job, the supply for data scientists is low as it is still a relatively new industry. Recorded data has risen in the past few years with a monumental surge which encouraged people to pursue data science and created an almost perfect dichotomy of demand and supply with plenty of job openings, at least for now.
And on top of that, according to current trends, it is a growing industry. Current trends as recorded by LinkedIn, that claimed a 650% increase in jobs related to data science since 2012.
How Much Do Data Scientists Earn?
The salary of a data scientist relies on several factors such as their amount of experience, their level of education, and the particular sector of data science that they are in, alongside others.
Based on experience, an entry-level data scientist salary falls around $90,000 while a mid-level data scientist’s salary falls around $130,000 – $195,000.
Data science professionals who have a lot of experience in manager-level positions earn around $250,000.
Generally, in terms of job titles pertaining to data scientists, data analysts, and big data engineers, salaries usually fall in the spectrum of $80,000 – $165,000.
In conclusion, data science is an industry that demands extensive skills, tools, and techniques for an extensive variety of tasks. It not only encapsulates a detailed inspection into one layer of the job and industry but rather dives deep into every single part of the performance.
It considers the unstructured and unsorted beginning and cleanses from the very start, enabling the data to make enough sense to be organized and sorted. Then data scientists sift through the data again in order to make it meaningful in terms of trends and patterns.
But data scientists themselves then extend their understanding to the relevant personnel and affiliated stakeholders. It is a high-stakes job that requires a deep understanding of complex problems and equally complex solutions.
In 2021, it is a profound and highly respectable industry and career path that is expected to see even better days in the upcoming years owing to the trends themselves of businesses and organizations.
This profession isn’t expected to go away any time soon because said businesses and organizations are reliant on their big data to move forward. Companies need data scientists to filter the data from their data.