Who is a Data Scientist and What Do They Do?
As the job title suggests, the primary responsibility of a data scientist is to analyse data from a scientific perspective. This aspect of the job is therefore inseparable from the concept of "data science," which serves as its foundation.
Because we live in a digital age, it is possible to reduce practically every aspect of our everyday life to a number. The field of study known as data science came into being with the intention of gleaning useful insights from large amounts of data.
Given that it draws on a variety of pre-existing fields such as computer science, statistics, mathematics, software development, machine learning, and more, the field of data science can be considered to be an example of an interdisciplinary field. Data scientists are able to generate actionable strategies for businesses and other organisations by precisely explaining trends and patterns in the data via the application of a variety of logical and analytic methodologies.
In a nutshell, data scientists are well-rounded professionals who are able to make complicated data more understandable and assist non-experts in improving the precision of their judgements.
What responsibilities do data scientists have?
Because the list of things that need to be done can seemingly go on forever, it is nearly impossible to summarise the work of a data scientist in a single paragraph. Let's not make this any longer than it has to be, so instead I'll just list the ten most important roles of a data scientist, which are as follows:
Determine the pertinent data sources in accordance with the requirements of the business.
Acquire data that is structured as well as data that is not structured.
Integrate data into readable formats
Perform analyses on the data using various predictive models and algorithms.
Conduct data analysis using programmes such as Python, R, SAS, or SQL.
Check the accuracy of the data and get rid of any irrelevant observations.
Identifying patterns and trends in the data might lead to the generation of crucial business insights.
Make use of interactive images in order to deepen your audience's comprehension.
Make a presentation of the final findings to the executive team and the project teams.
Maintain an awareness of the most recent advancements in the field of data science.
The Differences Between a Data Scientist, a Data Analyst, and a Data Engineer
Although it may appear that the work of a data scientist, data analyst, and data engineer is very similar, each of these positions plays a very distinct function in the process of data analytics.
Data Engineer
The work of data engineers lays a strong foundation for future analytical applications. They contribute to the process of laying the groundwork for a variety of data operations and form the framework within which data analysts and data scientists can interpret the data.
Data engineers are generally considered to have a higher level of expertise in the field of programming because they are responsible for the design and construction of data infrastructures such as databases, big data repositories, and data pipelines for the purpose of transforming data between systems.
Data Analyst
Working with the data that have been created by data engineers, data analysts are responsible for the following phase, which is to extract information that can be used from the given pool of data.
Data Scientist
Due to the fact that they are responsible for monitoring each and every aspect of data analytics, data scientists are frequently regarded as the most senior level of data analysts.
For instance, data scientists bring together data from a variety of disparate sources in order to find the underlying connections that exist between multiple data points. Data analysts, on the other hand, typically only look at data from a single source. In addition, data scientists are required to construct statistical models and machine learning algorithms in order to provide accurate predictions. This calls for a solid foundation in both mathematics and computer science.
The ability to effectively communicate orally and graphically is one of the most critical skills for data scientists to possess, as the final step in the process is to give an impressive presentation to decision-makers. It is of the utmost importance for data scientists to provide findings and recommendations in a way that is both clear and succinct because it is possible that the data will be too difficult for non-technical stakeholders to understand.
What kinds of skills are required of data scientists?
1. Data Visualisation
If you have the ability to produce graphical representations of information, it will undoubtedly help you become more productive in your work. This is because decision making is becoming increasingly dependent on data, which frequently comes with an overwhelming velocity.
Visual features such as charts, graphs, and maps are relatively straightforward and easy to read as compared to words, which can be monotonous. Data scientists should be able to show even the most basic data in a manner that is more visually appealing and interesting by employing a variety of data visualisation tools and approaches.
2. Automatic Learning Machines
It should go without saying that it is impossible for humans to manually handle enormous amounts of data because even the smallest inaccurate mistake can already lead to outcomes that are either useless or misleading.
For this reason, machine learning and data science are inseparable disciplines in the field of data science. Computers are able to automatically execute pattern recognition thanks to the application of algorithms to data; this results in an increase in the effectiveness of data processing.
3. Deep Learning
Deep learning is a subfield of machine learning that aims to classify data and provide predictions with an extremely high level of precision.
Deep learning, which takes its cues from the workings of the human brain, was developed with the express purpose of identifying patterns hidden within unlabeled datasets and differentiating relevant qualities without the assistance of humans. Data scientists should at least understand the foundation of deep learning in order to effectively handle the exponential volumes of data that are always changing. This is because deep learning algorithms can come to similar conclusions as humans.
4. Pattern Recognition
Discovering hidden patterns within large amounts of data is one of the most difficult tasks for data scientists. There are a number of creative new approaches to recognising patterns quickly and reliably, even if they are partially obscured, which can help to simplify a process that is simultaneously complex and essential.
Because of this, not only do data scientists need to construct the statistical model, but they also need to continue making advancements in the robotics and automation algorithms in order to achieve superior results.
5. Data Preparation
While the preparation of the data takes the longest amount of time in the life cycle of data analytics, having data that is free of errors is essential to developing insightful conclusions.
In order to clean and validate the data, the scientists need to get rid of any incorrect data and fill in any numbers that are missing. After the issues have been rectified, the data scientists will be able to proceed with the process of updating the format or the value entries in order to arrive at a clearly defined result. These jobs call for a significant commitment of resources and are impossible to do for those who could lack advanced information technology and logical procedures.
Textual analytic tools
According to SlickText, there are 5 billion people all over the world who send and receive SMS messages. This does not include other forms of textual material such as formal email, social media posts, customer care notes, and so on.
In contrast to the uniformity of numbers, the results of text analytics can vary depending on the methodology used. In order to achieve greater objectivity, data scientists may at times be required to manually define rules specifying how each word relevant to their industry should be understood and analysed by the system. This is done with the intention of enhancing objectivity.
Is a Career in Data Science a Good Choice?
As of April 2022, there are already 5,280 jobs on JobsDB alone that are related to data scientists. This indicates that demand for data scientist roles is on the rise.
In point of fact, data analytics is completely reshaping whole industries. Listed below are some of the most important areas in which data is causing significant shifts to take place:
1. Health care services
The range of blood pressure, the level of sucrose, and the body mass index are some examples of the types of data that healthcare experts have been collecting for years for the purpose of medical use.
As a result of the ever-increasing sophistication of today's technologies, the medical industry is now able to go well beyond the traditional practise of merely collecting data by producing exhaustive healthcare reports and transforming those reports into pertinent critical insights, which can then be applied to the improvement of patient care.
2. Financial
When computers could only process organised data, the amount of flexibility and the number of possible use cases were severely restricted.
New technologies enable modern investment firms to analyse both structured and unstructured data, including data that is not easily quantifiable or organised in a predetermined format. This enables investors to more easily identify robust businesses with attractive valuations and potential opportunities.
3. Logistic
The logistics industry places a particularly high value on big data due to the fact that the supply chain as a whole is heavily dependent on data. There are various data points that are worth looking into, and some of these include freight tracking and warehouse management.
Decision-makers can obtain new insights into sales, inventory, and operations planning by using statistical methods to both new and old data sources. This allows decision-makers to gain new insights based on a balanced blend of experience and analysis.
How Does One Get Into The Field Of Data Science?
Accreditation of Educational Achievement
A general step in becoming a data scientist is to earn an educational background that is relevant to the field.
Statistics, mathematics, information technologies, and computer science are the areas of study that are in highest demand right now because data science is still in its infancy as a discipline. If you are not currently enrolled in an undergraduate programme but are considering making a change in your line of work, it is recommended that you take some online courses or attend a boot camp in order to acquire essential knowledge such as management of SQL and MySQL, programming languages, and database architecture. You can even consider getting a master's degree to give yourself an advantage in the job market in the future.
Skill Sets
Data scientists need to equip themselves with a wide range of skills in order to be successful in their jobs. Some of these talents include statistics and probability, model deployment, machine learning, deep learning, data manipulation and analysis, data visualisation, and many more.
Because the time you spend inside the classroom is just as important as the time you spend practising these skill sets outside of it, you shouldn't be hesitant to put what you've learned into practise on projects that are relevant to the real world. You can contribute to a variety of open-source databases, such as those found at Kaggle, NASA, Wikipedia, and the UCL Machine Learning Repository, amongst many others.
Personal Qualities
Every successful hiring endeavour starts with candidates that possess a particular set of skills and qualities, and data scientists are no exception.
Because data scientists spend their days working with statistics, data, mathematical algorithms, and logical processes, having a mind that is attentive to detail is undoubtedly an asset in this profession. In addition, one of the primary focuses of data science is experimentation. Data scientists are required to test a variety of algorithms on a wide variety of data configurations, which means they will run into an extremely high number of unsuccessful attempts before they find the proper answer. It is possible that an individual who does not possess the resilience necessary to deal with consistent setbacks should not participate in this activity.
Find a job that starts from the bottom.
Even though there are a lot of various ways to get your career off the ground, acquiring a job at the entry level gives you the opportunity to take the initial step. You may enhance your expertise and get a deeper grasp of the industry as a whole by acquiring hands-on experience in data science and working alongside data science specialists at the same time.
Data Scientist Course
If you come from a completely other field and are interested in entering the field of data science, it is strongly recommended that you look for help from more experienced mentors rather than trying to teach yourself on your own.
In general, getting a degree from a boot camp is more flexible and more affordable than getting a degree from a traditional university. In addition, boot camps typically involve more hands-on data science projects that give students the opportunity to put their newly acquired skills and knowledge into practise. These benefits are available to students regardless of their current skill level or desired career path.
The Data Science and Artificial Intelligence with Python course from Preface covers a wide variety of subjects, including the following:
- Python for Scientific Programming
- The process of web scraping using APIs
- Data Crawling and Data Mining
- Elimination of Errors in Data and Supervised Machine Learning
- Deep Learning
- Natural Language Processing and Learning to Classify Images
- Data Visualisation
Because each of these topics builds on the ones that came before it, students will be able to acquire these skills in the appropriate order and will not waste time being confused or wasting time. Students will even have the opportunity to construct, train, and deploy their very own machine learning models at scale as part of the course's final module. This will be a terrific project showcase to add to your portfolio.
