Most organizations are still in the initial stages of learning how to apply data science to gain business benefits and healthy returns. To maximize the benefits of data science, people need to have good technical knowledge of managing the data in a production environment, without which the awareness of full stack data science is insufficient. Full-stack refers to the skills required to complete a project where each component is treated as a stack. People working as full stack data scientists are responsible for implementing the project from start to finish. Read on to know more about this relatively new technology tool that is taking the world by stride.
What is Data Science?
Data science is a collaborative field that deals with the study of data using various tools and methods. It aims to find invisible patterns, extract meaningful information, and solve problems in various domains. Full-stack data science is a method of ensuring the end-to-end application of this technology in the real world.
For an organization, full-stack data science merges the concept of data mining with decision-making, data storage, and revenue generation. It also helps organizations to maintain complex data processing systems with machine learning. To achieve this objective, companies need to group the following four major verticals of data science. These verticals include data engineering, data analysis, data modeling, and model deployment, also known as data monitoring. Despite being an extremely complex process, it can provide fruitful outcomes when appropriately applied.
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Stack Data science helps extract the most relevant information from a vast volume of collected data to enable organizations to make sense of it. It would not be wrong to say that data science combines scientific methods like statistics, data analysis, and artificial intelligence to get something meaningful from seemingly unimportant bulk data.
Who Is a Full-Stack Data Scientist?
Most organizations seem to wonder what is a full stack data scientist and why they are needed. A full stack data scientist is someone who possesses comprehensive knowledge about a data science project. These professionals are capable of handling feature engineering, getting the data, and model building. They also ensure the efficient application of the model for making relevant predictions using the data collected through various methods.
The role is still in its development stage; hence, there is no standard definition for it. However, it is safe to say that role focuses primarily on major aspects of analyzing data as well as integrating its findings to help enhance business growth. A full stack data scientist must be able to create models, test them, run them to production, refine the model, and test them again. After this, data scientists should have the ability to convince the stakeholders about the profit their business would get from the full stack or data science. The professionals need to cover every feature of data science comprising role-collection, organization, storage of data, and deployment of these models to show their impact on business.
A skilled data scientist can directly apply the data collected through Machine Learning and Artificial Intelligence to businesses. They can validate the data and highlight how a particular strategy can directly or indirectly increase the return on investment in the current business.
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7 Key Skills for Full-Stack Data Science
Like most other professions, full stack data scientists need to possess key skills, including the following.
1. Proper delivery
Full stack data scientists should be able to communicate how the machine learning model will instantly boost the return on investment for the customer. Organizations are generally interested in growth, and most end-users have little technical knowledge. Therefore, a full-stack developer or data scientist should be able to deliver the message in a simple and meaningful language to enhance the trust and reliability of the model.
2. Proper mapping
Professionals of full stack data science need to have a proper grip on programming. This critical skill enables them to write and map an object-oriented code. Once the code is tested, it should be able to validate each function.
3. Well-versed with Statistics and Machine learning
Having adequate knowledge of statistics and machine learning is another essential skill that data scientists should possess. In addition, they should be able to deploy various machine learning algorithms to solve complex problems.
4. Flexibility
A professional working in the field of data science full stack needs to have a flexible approach. For example, in many situations, these professionals might need to use business logic instead of machine learning models to get instant results. Moreover, machine learning models keep evolving, making it essential for these professionals to be flexible enough to work efficiently per the changing business scenarios.
5. Recognizing data sources and TFL
To create the right models, data scientists should be able to identify relevant data and then extract something useful by transforming the data. Finally, a full-stack data scientist should be able to make the data available and deliver it in the right place.
6. Model categorization
Models only provide business value when they are deployed properly. A full-stack data scientist should be able to deploy the models from pipelines to production. That allows consumers or end-users to access auto-generated data from the models. Having completed a full stack data science course also enables these professionals to ensure smooth integration of data and set up a smooth deployment mechanism.
7. Creation of Databases
When collecting, cleaning, and manipulating data, data scientists should be able to understand how to design and interact with databases. In addition, they should be well-versed in writing queries using programming languages for efficient organization of the data.
8. Broader Knowledge of Computer Science
Full stack data science professionals should have a broader knowledge of data structures, algorithms, and discrete mathematics as they are key aspects of data science. Data science is one of the several branches of computer science and the models developed will generally be accommodated within a larger application. Hence having basic knowledge of various computer concepts is a necessity.
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Stage-Wise Role of a Full Stack Data Scientist
The key to attaining success as a full-stack data scientist is to understand the role of these professionals at different stages.
Resolving the business problem
Every strategy of a data scientist should focus on solving a business problem. The analytics, automation, and efficiency gain should help the professionals solve a problem or expand the scope of business.
Organizing data
The full-stack data scientist needs to organize and validate data in such a way that it can be transformed to build a quality model for machine learning.
Research on data analysis
While training at an affordable data science bootcamp, data scientists learn the importance of scanning the data, bridging interferences, and then giving it a direction and converting it into a machine learning model.
Machine learning
Data scientists should be able to develop a sample model based on the data analyzed by them. In addition, the model should have clear objectives in terms of solving a business problem.
Model confirmation
The model developed by the data scientists undergoes the first trial, and these professionals explain its deployment and functioning. They also need to share the model with the stakeholders and discuss how it would help the business grow.
Model categorization
Once approved, the data science or full stack professionals must implement the model successfully and efficiently and make it accessible to the end-user.
Keep proper track
These professionals also must monitor the model to ensure that it works as expected and gives the desired results. During down-the-line operations, they may also have to make minor fixes.
How to Become a Full Stack Data Scientist?
Data scientists generally come from different educational backgrounds and work experiences, but almost all of them need to be well-versed in four key areas explained below.
Domain Knowledge
Domain knowledge is very important in data science. For example, aspiring data scientists looking to work in the banking sector should have more information about stock trading, finance, etc. This knowledge will prove advantageous for them and help them get more preference as compared to a raw applicant.
Math Skills
Prior knowledge of Linear Algebra & Optimization Technique is one of the most important full stack data scientist skills. It helps them understand and design various machine learning algorithms that play a key role in this field. Similarly, mastery of statistics is also very significant as it is a part of data analysis. Knowledge of probability concepts is considered a prerequisite for mastering machine learning.
Computer Science
While computer science is a vast field, an aspiring data scientist or full stack developer must have a basic knowledge of programming languages to succeed. Python and R for data science are the two main programming languages they need to be proficient in. Both these languages have a rich set of libraries to apply the complex machine learning algorithm, visualization, and data cleaning concepts. Apart from the programming language, the other computer science skills that should be a part of a full stack data science program include the following.
- Basics of Data Structure and Algorithm
- SQL
- MongoDB
- Linux
- Git
- Distributed Computing
- Machine Learning and Deep Learning
Communication Skill
Data scientists should possess exceptional communication skills in both written and verbal forms. After concluding the analysis, the project must be communicated to others through a presentation to a group of colleagues or various stakeholders. Having good communication skills ensures that these professionals can convey the message and meaning of the project more efficiently to ensure its success.
Why You Should Become a Full Stack Data Scientist
Most people find choosing the best career option between fullstack developer vs. data scientist quite confusing. Here it is important to understand that the demand for proficient data scientists is constantly increasing. Data science is one of the most popular technologies being used for ensuring a smooth digital transition across diverse industries. This technology is impacting all the major sectors like hospitality, education, banking, finance, and retail, besides creating countless jobs. Some key reasons to become a data scientist include the following.
A huge gap between demand and supply
Data science is a relatively new technology, and the number of people with the best data science course certifications is quite low. As companies compete to attract professionals from this limited pool of talent, they continue to offer better salaries and work benefits. Moreover, a significantly large number of internship opportunities and entry-level positions make getting data science jobs much easier after completion of studies.
Flex time working plan
Flexible work timings are perhaps the best among the various benefits offered by companies to attract the best professionals in the field. This enables the data scientists to enjoy a better work-life balance and makes it easier for them to manage their personal and professional responsibilities more efficiently.
Adequate growth opportunities
People employed in entry-level positions in most jobs may take years to reach a position of influence over the macro-level decision-making. However, this is not the case with data scientists. These professionals get enough opportunities to earn a higher-level position within just a few years.
Lucrative Pay Scale
Everyone from beginner data scientists to experienced data professionals can expect outstanding salaries and compensation. In fact, according to Glassdoor, entry-level data scientists can expect a salary of nearly US$109,000 a year. For experienced data scientists, salaries often touch a quarter of a million dollars per year.
Conclusion
The market today is changing in amazing ways with an increased enthusiasm for AI and machine learning. Professionals with Knowledgehut’s affordable data science bootcamp training boosts these new technologies by providing solutions to problems using similar data for future use. Data scientists also stand out because they not only help companies solve problems but also help companies identify problems and requirements they never noticed before. Aspiring candidates must know efficient data manipulation well to become data scientists. They should also be able to implement their skills to benefit the organization. It is a booming field, and all people learning these skills can be assured of having a bright future with proper training.
