DATA SCIENCE

WHAT IS DATA SCIENCE?

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models. The data used for analysis can come from many different sources and presented in various formats.

CURRENT DEMAND FOR DATA SCIENCE

There is a very high demand for data scientists in the corporate and academic world today. Data scientists are being actively sought out by businesses and organizations all throughout the corporate world. There is no sector or industry in which a high demand for data scientists does not exist. The field of data science is expanding all throughout the corporate and academic world rapidly. Data scientists and data analysts are discovering new applications of data science in every sector and industry on a daily basis. The rate at which data scientists are making innovations and discoveries in the field of data science is unparalleled by any other field of the domain of computer science and information technology. Data science is seeing more and more advancements and developments with every passing day. In this scenario, it can be very easily concluded that the demand for data scientists is very high and that it will remain high for the foreseeable future.

FUTURE SCOPE OF DATA SCIENCE

• Companies’ Inability to handle data: A data scientist becomes the savior in a situation of mayhem like this. Companies can progress a lot with proper and efficient handling of data, which results in productivity.
• Revised Data Privacy Regulations: In today’s times, people are generally more cautious and alert about sharing data to businesses and giving up a certain amount of control to them, as there is rising awareness about data breaches and their malefic consequences. Companies can no longer afford to be careless and irresponsible about their data. The GDPR will ensure some amount of data privacy in the coming future.
• Data Science is constantly evolving: Data science is a broad career path that is undergoing developments and thus promises abundant opportunities in the future. Data science job roles are likely to get more specific, which in turn will lead to specializations in the field. People inclined towards this stream can exploit their opportunities and pursue what suits them best through these specifications and specializations.
• An astonishing incline in data growth: The amount of data existing in the world will increase at lightning speed. As data production will be on the rise, the demand for data scientists will be crucial to help enterprises use and manage it well.
• Virtual Reality will be friendlier: Big data prospects with its current innovations will flourish more with advanced concepts like Deep Learning and neural networking. Currently, machine learning is being introduced and implemented in almost every application. Virtual Reality (VR) and Augmented Reality (AR) are undergoing monumental modifications too. In addition, human and machine interaction, as well as dependency, is likely to improve and increase drastically.
• Blockchain updating with Data science: The main popular technology dealing with cryptocurrencies like Bitcoin is referred to as Blockchain. Data security will live true to its function in this aspect as the detailed transactions will be secured and made note of. If big data flourishes, then lot will witness growth too and gain popularity.

Essential tips for starting a career in Data Science

1. Choose the right role: When starting a career in the datascape, it is critical to take on a role that aligns with your skill set, educational background, work experience and interests.
2. Update your skill set with courses: After deciding on a role, the next step is to understand it deeper by learning the nuances of that field, which will help you advance in that role. A great way of learning these is to engage with real-world case studies.
3. Choose to learn a Data Science tool/language: Selecting tools/languages may be a fundamental problem for Data Science enthusiasts. The answer is to select a mainstream tool/language to start a career in Data Science.
4. Find and join peer groups: Finding like-minded peers who are interested in the same field as you will help you stay motivated and explore more of your potential in the field.
5. Focus on practical applications: Practical and applied learning are more critical than just theoretical knowledge as it allows you to gain practical experience for more meaningful career outcomes. Real-life applications also add to the repertoire of a Data Science professional along with their current skill set.
6. Learn communication and soft skills: To become a well-rounded professional, you also need to develop the right kind of soft skills, such as the ability for critical thinking, listening, persuasive communication, and problem-solving.

Prerequisites for Data Science

1. Machine Learning: Machine learning is the backbone of data science. Data Scientists need to have a solid grasp of ML in addition to basic knowledge of statistics.

2. Modeling: Mathematical models enable you to make quick calculations and predictions based on what you already know about the data. Modeling is also a part of Machine Learning and involves identifying which algorithm is the most suitable to solve a given problem and how to train these models.

3. Statistics: Statistics are at the core of data science. A sturdy handle on statistics can help you extract more intelligence and obtain more meaningful results.

4. Programming: Some level of programming is required to execute a successful data science project. The most common programming languages are Python, and R. Python is especially popular because it’s easy to learn, and it supports multiple libraries for data science and ML.

5. Databases: A capable data scientist needs to understand how databases work, how to manage them, and how to extract data from them.

DATA SCIENCE CAREER AND SALARY

1. Data Architect and Administrators: Average Base Salary: US$121,606 per year

2. Data Engineer: Average Base Salary: US$92,245

3. Data Analyst: Average Base Salary: US $62,970 per year

4. Data Scientist: Average Base Salary: US$97,350 per year

5. Machine Learning Engineer: Average Base Salary: US$112,790 per year

6. Statisticians and Mathematicians: Average Base Salary: US $93,290 per year

7. Business IT Analyst: Average Base Salary: US$70,759 per year

8. Marketing Analyst: Average Base Salary: US$58,211 per year

9. Clinical Data Managers: Average Base Salary: US $75,562 per year.