Updated June 14, 2023
Difference Between Data Scientist vs Data Engineer vs Statistician
Data Scientist vs Data Engineer vs Statistician – Big data is more than just two words and is exploding in an unprecedented manner. It is growing in terms of velocity, variety and volume at an unimaginable pace. It has taken the entire world by storm and is now available in real-time, thereby allowing brands to generate analytics in a swift and fast manner. With the potential to change the world, big analytics is impacting governments, companies, brands and academic organisations as well. By changing the livelihood and manner in which people are living their lives, big data is rapidly evolving and changing, every single day.
The amount of data in the world is unimaginable and us equal to the litres of water in all the oceans of the world. Learning to surf this ocean of big data will help companies make use of the multiple opportunities that exist in the world. When companies are able to gain insights from raw data and by gaining valuable insights from them, brands can adapt better to the marker trends and take actions to empower and expand their base in a comprehensive manner.
Now, many of you would be wondering what exactly is big data? Big data is used to describe the process of applying serious computer power, especially those like machine learning and artificial learning to highly complex and big sets of information. What kind of information does big data tackle? Big tackles all kind of information, just name it. It can be used for comparison of utility costs with meteorological data to gain information about trends and inefficiencies. It can also be used to compare information about the location of ambulances, along with patient records and help hospitals make more informed choices about response time and survival. Big data can also be used by fitness enthusiasts to track their exercise and calorie count so that they can reach their goals in a faster and smoother manner.
Because big data is such a big field with immense opportunity, there are multiple job opportunities in this field as well. This article deals with three major job opportunities that are growing in prominence in the field of big data. These include data scientists, data engineer and statistician.
The Evolving Field of Data Scientists
The rise of new technology in the form of big data has in turn led to the rise of a new opportunity called data scientist. While the job of a data scientist is not exclusively related to big data projects, their job is complimentary to this field as data is an integral part of their duties and functions. A data scientist’s duties and functions have evolved as the duties and functions of brands have started to evolve in a rapidly competitive environment. The formal training is an integral part of becoming an data scientist and this requires a solid foundation in some basic fields like computer science and applications, modeling, statistics, math and analytics. A data scientist is different from other normal professionals because he has a strong business sense that is generally coupled with strong communicative skills that help them communicate their findings and insights with business and IT leaders so that they can meet the challenges and demands of their industry in a successful manner on one hand and add value on the other hand.
A data scientist is somebody who is extremely creative and curious, and can spot insights from large amounts of data in an easy and simplified manner. It is almost like a Renaissance individual who are really keen to bring about big change in the industry and learn big things as well.
A traditional data analysts will look at data generally only from one source, a data scientist is competent enough to examine data from multiple disparate sources. By sifting through all kinds of data, a data scientist has a major goal of discovering hidden insights and this in turn can help them to gain competitive advantage as well. A data scientist is not just responsible for collecting and reporting data, but also studies them from various angles and recommends brands, how they can use the said data to reach their goals and objectives as well create new goals as well.
Data Engineering and Its evolution
The role of data engineer sometimes overlap that of a data scientist. This is mainly because their tools and techniques are very similar and have almost the same set of functions in some companies. However, data engineering is also called data infrastructure or data architecture in multiple companies. The main responsibilities of a data engineer is to collect data, store data and batch process or process them in real time and relay them through an API to a data scientist who can easily understand and make sense of them. In other words, it is data engineering that truly help data science to perform their jobs in a smooth and easy manner.
The market is filled with multiple big data tools and each of them perform a unique function. It is important that a brand uses a particular tool to reach their objective rather than the fact that the tool is trendy and popular within the industry. That is why data engineers need to have a solid and firm base in the field of software engineering. They must be able to learn and use these tools in an effective manner and also improve on them in case that is the situation. In short, a good and efficient data engineer will have a vast and comprehensive knowledge about databases and is proficient in the best engineering practises. Some of these practises include handling and logging errors, monitoring of system, building pipelines that are human fat tolerant, understanding of the scaling process among other techniques and methods.
Skills needed to become successful data scientists
Becoming a data scientist is therefore in a lot of demand among a lot of professionals. At the same time it is important to remember that to stand out in the crowd. There are certain skills that will help professionals to gain the skills to help them become competent in the big data sector.
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A data scientists needs to have knowledge in basic tools
Before gaining better prominence in the big data industry, it is important to master basic tools related to big data. This means that professionals need to gain an in-depth understanding of statistical programming language like R or Python on one hand and a database query language like SQL on the other hand. These languages and skills will help professionals to create a strong foundation and thereby build a strong and successful career as well.
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A data scientists need to have proper understanding of basic statistics
Having a basic understanding of statistics is extremely important for those individuals who want to gain better understanding of the big data industry. Many data scientists are still not aware of the correct definition of p value That is why data scientists need to be aware of statistical tests, maximum likelihood, distributions among other things. In addition, things like machine learning and statistics knowledge will come in handy during all future learnings. Statistics in particular is extremely important in case you want to make data driven companies. While some companies may not be product driven, statistics is something that is vital for all brands and companies across sectors and economies.
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A good data scientist must be aware about the various aspects of machine learning
If you are a data scientist who wants to work for a large company, then you will need to work with data that are massive in size and structure. That is why you need to aware of how to work with machine learning methods. This includes various elements like k-nearest neighbours, random forests, ensemble methods, all these are terms that are gaining prominence among machine learning enthusiasts. While there are many techniques being implemented through R or Python libraries, machine learning is good, though not completely essential. It is more important to understand the broad strokes and use them them in an appropriate manner.
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A good data scientist is adept at data mugging
Analysing data is not as simple as it looks and sometimes when the amount of data is huge, it can become a difficult and complex process. That is why it extremely important and essential that data scientists know hoe to deal with imperfections in data that can include missing values, inconsistent string formatting, date formatting among other issues. This problem of dealing with discrepancies in data is a vital role in small and mid level companies or in cases where data plays a very important role in the company’s functioning. That being said, expertise in data mugging is something that will help data scientists to explore and grow their career in a successful fashion.
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A good data scientist will have strong data visualisation and communication skills
Visualising and communication skills are some of the most important skills that a data scientist can possess. This is especially true for new companies, which are just discovering the strength and power of big data and its applications. Communication skills are extremely important because if a data scientist is not able to explain his findings and insights, then the entire process will be futile. When data scientists can communicate the benefits of big data in a successful manner, they can help companies to realise their goals and objectives. Coming to visualisation, it is extremely important that data scientists are familiar with data visualisation tools that include ggplot and d3.js among others. While visualisation is important, data scientists must also be aware of the principles that govern encoding of data and communication information as well.
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A good knowledge of software engineering will stand a data engineer in good stead
A data scientists who is aware of engineering is critical for the growth of a small company. This is because they will be responsible for handling a lot of data logging and will eventually helm the development of strong and technologically advanced data driven products.
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The thinking of a data scientist is extremely important
All companies want to hire individuals who are able to solve problems and challenges in a successful manner. That is why they should be creative, analytical and problem solvers in all the situations. By asking relevant questions and finding relevant answers, data scientists can reach the pinnacle of success in their career.
The role and duties of a statistician
While the duties and roles of data engineer and data scientists overlap in more cases than one, the role of a statistician is relatively different and unique. Today, the world can be compared to a quantitive field. Many industries and companies are depending on data and numerical reasoning to make sense of various aspects of their growth and development. Data is no longer just numbers but numbers that carry information that can interpreted in a dynamic manner. This use of data has in turn led to the growth of statisticians who expertise lies in the following field:
1. Production of trustworthy data
2. Analysation of data so that their meaning is clearer
3. Inference of data so that solid conclusions can be made from them
Statisticians are needed in every possible industry and company. For example they play an important role in the functioning of Business and industries. There are four main areas in this field which require the expertise of statistician and they are manufacturing, marketing, engineering and statistical computing. In manufacturing, statisticians help brands to design products that meet the expectations of the customer, ensure consistency of quality and ensure continuous growth and development in the long run. By designing new products, conducting focus groups and gather feedback of clients/ customers, statisticians help companies to analyse sales and predict future trends, thereby ensuring better fulfilment of marketing goals.
Good and effective Statistical methods help engineers to create consistent products, detect problems before they arise, minimise chemical and other waste, and predict product life of a particular product.Statistical computing provides opportunities by developing software design and development, technical support, software testing, quality assurance, education, documentation, marketing, and sales among other fields. Statistics also play a vital role in fields like health and medicine by helping to monitor and report disease outbreaks, create vaccines, prevent the spread of diseases among many other things that are aimed at creating a better health standard for people across the globe.
In conclusion, whatever be the field, data is playing a very important role and it is helping to make life easier and more productive for all sectors. By creating new opportunities and addressing global challenges of energy, environment and development, big data has immense potential to help the world discover new opportunities for growth and development.
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