Overgrowing in each sector, and if we are not smart enough to act in this overly crowded world, it is quite difficult to face future challenges. Nowadays, organizations are mainly relying on data to make informed decisions which will impact their growth positively.
Earlier, it was a challenging task to manage the enormous data sets. Having said that, Big Data is termed as large volumes of data delivering from different sources and has potential to help in the process of business development. When it comes to managing it, the primary challenge was to store and process these large sets of data. But the invention of Hadoop and other data storage devices eliminated this challenge.
Processing these vast data sets is vital for finding the hidden insights out of it. To find useful information from these databases, the final option today is Data Science. Data Science is a process of data collection, analysing, identification, and extracting meaningful information out of the big data.
Data engineers use database and data storage to execute the data mining, data munging for the purpose of analytical processes. Therefore, it is very important to understand what is Data Scientist and how can it add value to your business. The topics which we are going to cover in this Data Scientist article are as follows.
|Table of Content - What is Data Scientist|
Data Science is the concept associated with a wide variety of fields, and hence, there is no single way to define it. Data Science is a multidisciplinary concept which includes data interface, algorithm development, and technology useful for solving technically complex problems.
Data acts as a significant source for data science, and by mining this data, we can build advanced capabilities. The ultimate aim of Data Science is to bring out the meaningful information from the database which helps in heading the organization towards growth.
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A Data Scientist is a person who is responsible for gathering and analyzing data and use various types of analytical and reporting tools to extract the hidden trends in the collected data.
Data Scientists work as teams to mine big data and to dig the useful information resided in collected data which is helpful in predicting the opportunities available in the market. It also helps in identifying the risks and opportunities that are associated with the business.
The primary ambition of the data scientists is to process these multiple data types to predict the future outcomes. Let's have glance over various types of data sets.
Structured data is a type of data which is predesigned to store the collected data in an organized way. Information is stored in a defined way, so it is effortless to retrieve and easy to analyse. A structured data is deposited into a repository which is useful for a specific purpose.
A structured data model is designed in a simplified way in which different fields are available. We can draw conclusions without much difficulty. Experts around the world are estimating that the structured data has occupied very low percentage of the entire data which is available. It is usually flexible to store in a database.
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Unstructured data is one of the three forms of data, and it is delivered on a consistent basis. There is no specified or predefined way to store this type of data. It is in different formats and very difficult to fit into the Relational database. A separate method is mandatory to process these data sets.
Fortunately, there are different methods available to process this kind of data such as batch processing, online processing, real-time processing, distributed Processing, etc. There are some separate set of tools available to turn this unstructured data into a useful one.
Semi-structured data is a type of data which contains semantic tags, but it does not coordinate with the relational database conventional structures. Even though semi-structured entities are related to the same class, they may have various attributes. Some of the examples are email, XML, and other markup languages.
It is quite challenging to find a person with the matching skills required for a data scientist role. There are minimal human resources available who can have the capability to perform the data scientist roles; thus the demand for the Data Scientists has been increasing over the years. Data Scientist job is termed as the Sexiest job of 21st century, and on-demand job in the modern tech world.
The essential responsibilities of Data Scientist include gathering, and analysing data. They use different tools to pick out the information to estimate trends in customer behaviour. Data Scientists are mainly assigned to developing statistical learning models for data analysis, and they should have hands-on experience in using analytical tools as well as the ability to create and evaluate complex predictive models.
A Data Scientist uses advanced data to create hypotheses and makes interfaces to read the customer minds, and market trends. The Data Scientists must be good at using data analytics in suggesting the ideas which helps in making decisions such as improving product or process, or developing a new product or service, or changing course of doing business.
They are associated with the core areas of business such as development of software, new product suggestions, embracing the new opportunities, and avoiding the risks with the help of predictive models.
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As we already know, a data scientist is a guy who acquires multiple skills to perform the data analytics. Soft Skills play a very crucial role in any job role, and the same thing applies to a data scientist. A data scientist needs soft skills such as curiosity combined with doubtfulness and enthusiasm to work.
Interpersonal skills: These skills are crucial for a Data Scientist. One should be good at storytelling and presenting the analytics information diagrammatically which would easily understandable by employees at each level. And, they should also possess leadership qualities which helps them to take part in data-driven decision-making processes.
Education: To become a Data Scientist, one has to complete Bachelor's degree either in Data Science, or Statistics, or Mathematics.
Hard skills required: It is essential to acquire hard skills to become a Data Scientist, which are data mining, machine learning, and the capability to coordinate structured and unstructured data. Experience in statistical research techniques like clustering, modeling, and segmentation is also required.
Technical skills: A Data Scientist requires sound technical knowledge in big data platforms and tools, including Hadoop, Hive, Spark, Pig, and MapReduce, and programing languages that include structured query language (SQL), Scala, Python, and Perl, as well as statistical computing languages such as R is also critical.
Often, people confuse the role of data scientist with an analyst, but both are unique, and perform separate set of functions. Here, in this segment, let's find out the significant roles of both professionals.
The role of a Data Analyst varies from a company to company depending upon their needs, but in general, these professionals have to collect data and process it with the help of different statistical tools and techniques.
Analysts spot the patterns to make correlations, thereby identify the new opportunities in the business field. In some companies, data analysts also take responsibility for designing, building, and maintaining a relational database and big data systems.
When it comes to Data Scientist roles, it goes beyond the functions of a data analyst. These professionals are entitled to analyse big data using advanced analytics tools, and even they have to research and build algorithms to solve specific problems. Sometimes, they have to conduct experiments to find new algorithms. They do proper research on data to place organization products and services in a better position in the market.
There are many Data Science professionals who have no proficiency in the areas of Machine learning and Artificial Intelligence. which includes neural networks, adversarial learning, reinforcement learning, etc.
To stand apart from the entire crowd of other Data Scientists, one should acquire knowledge of different machine learning concepts such as supervised machine learning, logistic regression, decision trees, etc. Knowing machine learning concepts could help in solving the complex data science problems.
Data Science demands machine learning skills in different areas of its execution. Advanced machine learning skills such as time series, outlier detection, recommendation engines, computer vision, survival analysis, supervised and unsupervised machine learning, natural language processing, etc., are the areas of machine learning an expert data scientist should have knowledge on.
Data Science is a huge added advantage if an organization can utilise it optimally. It influences and suggests improvements from core to non-core areas of a business. It enables authorities in making the data-driven informed decisions which will show a positive impact on future endeavours. Having excellent technical, interpersonal, analytical, and leadership skills is an added advantage for a Data Scientist role which makes them professionals in the field of data science.
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Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.
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