Introduction 

One of the most popular and rapidly expanding tech career paths is Data Science. Due to the high demand for the position, many professionals and recent graduates are attempting to enter it to fill the talent gap and establish successful careers. 

Making judgments and predictions via Machine Learning, prescriptive analytics, and predictive causal analysis is the major application of Data Science. It is a field that aids companies in getting closer to their clients and helping them identify their markets and make better decisions. 

What is Data Science? 

To solve business challenges, the area of Data Science combines the various fields of Machine Learning algorithms, data inference, programming, mathematics, and statistics. 

We are acquiring data at an astonishing pace and need Data Science to add value to this information, make it applicable to real-world situations, and make it helpful. 

Data scientists, who resemble super-humans, interpret unstructured, disorderly, raw data gathered from sources like emails, social networking feeds, and smart gadgets that don’t neatly fit into databases. They gather, purge, and arrange data that can eventually be leveraged to make business growth strategies. 

Requirement for Data Science  

Given the shortage of skilled personnel and the rate at which the volume of data is doubling, Data Science is currently the most popular field. 

The US Bureau of Labor Statistics predicts that by 2026, there will be a 27.9 percent increase in the number of employees needing Data Science expertise. 

By 2026, the Data Science market is projected to reach a value of USD 322.9 billion, up from USD 95.3.9 billion in 2021. 

Recent statistics indicate that data engineering is one of the technology fields with the highest growth, with an increase in job postings of over 88.3 percent. 

The IT, finance, insurance, and related professional services account for 5% of all data analytics and Data Science positions, according to the most recent reports from Forbes. 

Around 80% of businesses worldwide are devoting a significant portion of their profits to building out a capable data analytics section and hiring the most intelligent individuals in the field. 

Why Are Data Science Professionals so Highly Demanded Worldwide? 

Here are the main explanations: 

• Data Abundance: Organizations worldwide are struggling to manage the massive volumes of data to their access, and managing future databases that will become exponentially greater presents an even bigger difficulty. 
• Lack of Talent: It is difficult to find skilled talent in the field of Data Science. Companies struggle to obtain individuals skilled at comprehending and utilizing data to promote commercial benefits. The need for data analysts & scientists is like an unstoppable torrent of water, but the supply is a trickle. 

• Wide-ranging and Extensive Skillset Needed: Working in Data Science involves far more than just knowing how to code. You need to be skilled at using tools like Spark, Hadoop, and NoSQL. Additionally, you need to have solid training in programming, statistical modeling, and machine learning. Finding someone with all these skills is really difficult. 
• Non-inclusion for professionals without any background in the related fields: For professionals or students without a background in Computer Science, Engineering, Mathematics, Statistics, or General Science, entry is forbidden. Data Science is an interdisciplinary field that calls for knowledge in all the disciplines mentioned above. 
• Handsome Salary Packages: there are many monetary benefits at large for being a Data Science professional in any organization. 

Who is a good Data Scientist? 

A good Data Scientist should possess the following skills: 

1. They have an analytical mindset. 

An analytical mindset, a solid statistical foundation, and solid knowledge of data structures and machine learning techniques are essential qualifications for a Data Scientist. They should be proficient in Python or R and at ease handling huge data sets. Data preparation takes up around 70% of a Data Scientist’s time, including data cleansing, munging, and preparation so that machine learning algorithms may be used on the data. Since volume, velocity, variety, veracity, and value are the five Vs of Big Data, it is crucial that a Data Scientist can handle all of them. 

1. They are well versed in the domain of knowledge, especially statistical and programming skills. 

Sound domain knowledge is essential for the Data Scientist. They must comprehend the underlying business issue to select the best Data Science model. They should be able to fast iterate to produce the final model and analyze the output of their models. Additionally, they must be strong communicators since they must explain their findings in terms that a larger audience can comprehend. To make it simple for another person to build on their work, they should be able to articulate clearly their methodology. They ought to be able to comprehend research published in their field and use it to solve their difficulties. 

1. They have great problem-solving skills and are excellent at resolving real-world problems. 

A Data Scientist is anticipated to possess strong software engineering abilities as well as solid statistical, mathematical, and algorithmic expertise. They should take a foundational course in statistics and mathematics first, emphasizing probability, set theory, algebra, functions, and graphs. Following that, they must learn a computer language, preferably Python. 

Prerequisites to Learn Data Science  

You need to be skilled in both technical and non-technical areas to succeed as a Data Scientist. Some of these talents are necessary for success, while others are nice to possess and will make your job as a Data Scientist easier. The degree of skill-specific expertise you need to have depends on your job role. The technical and non-technical requirements for Data Science to launch a career in Data Science are elaborated below 

Education 

The profiles of Data Scientists depend on your level of expertise, background, and experience. To enroll in a Data Science course, you must have at least a Bachelor’s degree. A Bachelor’s or Master’s degree in one of the STEM fields is advantageous since it lays the groundwork for the fundamental statistical or mathematical understanding that will be crucial in the future. 

The expertise and work description will simultaneously increase with the rise in qualifications. However, there will always be a gap between what is being taught in the classroom and what you learn from professional experience. 

An applicant with a Ph.D. but no expertise would not be comparable to one with a Master’s degree but three years of work experience. 

Technical 

• Mathematical 

The field of business development attracts professionals and students from a variety of disciplines, including computer sciences, engineering, economics, mathematics, operations research, and research. 

For a career in Data Sciences, not all of it is necessary. The most important math requirement for Data Science is to have a thorough understanding of mathematical and statistical principles. 

Clear statistical notions of data are in high demand in the field of Data Science since they necessitate examination to yield practical solutions to issues. Any background research will therefore be successful, but the best starting point is a refined and solid statistical and mathematical foundation. 

• Programming 

You don’t have to be an expert coder who works hard at it. However, it would be ideal if you had a solid foundational understanding of the programming ideas. You can learn Data Science programming faster by using concepts from languages like C, C+, or Java. 

Hardcore programmers are not required to help analyze large portions of data, produce effective quotes to illustrate the issue, or work with enormous data. Python and R are two common computer languages used in Data Science. These ideas will enable the applicant to advance considerably in the field of Data Science. 

• SQL 

SQL or structured query language is one of the key tools needed to engage in Data Science programming. 

Data Scientists invest significant effort in writing SQL and the script that goes along with it so they can stand firm in the task that has to be done. You must be able to solve SQL queries, write simple SQL, and be at ease with joins, groups, and constructing indexes. 

Because the fundamentals of SQL are unconcerned with the layers on top, you don’t need to become an expert in data management to function as a Data Scientist. For the Hadoop cluster, data analysis needs a solid basis that can be accessed from a database (example of language used). 

• Machine Learning 

One of the core ideas in Data Science and its crucial component is Machine Learning. Any course you take online to get a university degree will have Machine Learning in the curriculum. Thus, before beginning a career in Machine Learning, it is not essential to be familiar with its fundamentals. 

• Data Visualisation 

One of the characteristics of Data Scientists is their love of data and their ability to work with vast volumes of it. The job demands you to do mathematical operations and statistical analysis on the variety of data you have in front of you. Thanks to your obsession and love for data analysis, you can solve complex business problems that no one can. 

Interpersonal and Analytical Skills 

• Communication Skills 

When you work in business, having a firm grasp on personal qualities like leadership, networking, communication, and listening is crucial. 

Personal attributes give you the knowledge and training to act and interact with the different collections of individuals in your team, whether it be a little business or a major international corporation. The end goal is successful outcomes. 

• Business Strategy 

It would be useless to have a technological edge in the data analysis area but zero in the business acumen area, given the premise that Data Science is there to help businesses solve problems and uncover problem areas. 

Your bachelor’s or master’s degree in technical science will not give you an understanding of how businesses operate. Therefore, taking an online course to guide you through the fundamentals of business administration will be helpful. 

It will give a more comprehensive picture of how things must function within an organization from a business perspective. 

• Teamwork 

In the field of Data Sciences, it is important to communicate well with the people around you. Also, they are supposed to work under a team of 4 or 5 as mainly, and their work is related to data study and analytics. Teamwork not only distributes workload but increases efficiency per person. 

Conclusion 

A combination of many academic fields, including computer science, mathematics, and statistics, is known as Data Science. Being an expert in every topic and equally knowledgeable in each is impossible. An individual with a foundation in Statistics won’t be able to pick up computer science rapidly enough to be an effective Data Scientist. Therefore, Data Science is a dynamic, constantly evolving field that necessitates ongoing research into its various facets. If you’re interested in a successful career as a Data Scientist, do check out the Data Science certification course by UNext Jigsaw. This course contains all the Data Science prerequisites and much more to help you excel in your career.