Data Science has turned out to be one of the most demanded jobs. It has become a buzzword that nearly everyone talks about these days. In this Data Science tutorial for beginners, we are going to dive into this amazing field. The term Data Science has emerged lately with the evolution of mathematical facts and data analysis. Data Science is a combination of multiple disciplines that use statistics, data analysis, and machine learning to analyze facts and to extract knowledge and insights from them. Data Science is used in many industries in the world today, e.g. banking, consultancy, healthcare, and manufacturing. Data Science can be applied in nearly every part of a business where data is available i.e. in Consumer goods, Stock markets, Industry, Logistics companies, E-commerce. Data Science is needed:
- Route planning To discover the best routes to ship
- Through predictive analysis, It foresees delays for flight/ship/train, etc.
- It creates promotional offers
- To find the best suited time to deliver goods
- To forecast the next year’s revenue for a company
- Analyzing health benefit of training
- To predict who will win elections
What is Data Science?
Data Science is the area of study which involves extracting insights from significant amounts of data by the use of various scientific methods, algorithms, and processes. It helps you to find out hidden patterns from the raw data. The term Data Science has emerged due to the fact of the evolution of mathematical statistics, data analysis, and big data. Data Science is an interdisciplinary subject that allows you to extract information from structured or unstructured data. Data science permits you to translate a business problem into a research project and then translate it again into a practical solution.
Challenges of Data Science Technology:
- A high range of information & statistics is required for correct analysis
- Not ample data science talent pool available
- Management does no longer provide financial assistance for a data science team
- Unavailability of/difficult access to data
- Data Science outcomes not correctly used by business decision-makers
- Explaining data science to others is difficult
- Privacy issues
- Lack of large domain expert
- If an organization is very small, it cannot have a Data Science team
Why Data Science?
A few years ago, data was less and mostly available in a structured form, which could be easily stored in excel sheets, and processed using BI tools. Now, handling such a huge amount of data is a challenging task for every organization. So to handle, process, and analyze this, we required some complex, powerful, and efficient algorithms and technology, and that technology came into existence as Data Science. Data has emerged as the fuel of industries. It is the new electricity. Companies require data to function, develop and enhance their businesses.
Data Scientists deal with statistics in order to help organizations in making appropriate decisions. The data-driven strategy undertaken through the corporations with the help of Data Scientists who analyze a large amount of data to derive significant insights. These insights will be useful for organizations who want to analyze themselves and their overall performance in the market. Other than commercial industries, healthcare industries additionally use Data Science. Where the technology is in large demand to apprehend microscopic tumors and deformities at an early stage of diagnosis. About 11.5 million jobs will be created by 2026 in accordance with the U.S. Bureau of Labor Statistics. Also, the job of Data Scientist ranks amongst the top rising jobs on LinkedIn. All the statistics point towards the developing demand for Data Scientists.
Pros and Cons of Data Science
The field of Data Science is huge and has its own fair share of benefits and limitations. So, right here we will measure the pros and cons of Data Science.
Advantages:
1. It is in Demand and Highly Prestigious: Data Science prospective job seekers have several opportunities and are in excellent demand. It is the fastest-growing job on LinkedIn and is envisioned to create 11.5 million jobs by 2026. This makes Data Science a highly employable job sector. Data Scientists allow organizations to make smarter business decisions. Companies are counted on Data Scientists and use their expertise to provide better outcomes to their clients. This offers Data Scientists an important position in the company.
2. Data Science is Versatile: It is broadly used in healthcare, banking, consultancy services, and e-commerce industries. Data Science is a very versatile field. Hence, you will have the possibility to work in various fields.
3. Data Science Makes Data Better: Companies require knowledgeable Data Scientists to process and analyze their data. They not only analyze the data however additionally improve its quality. Therefore, Data Science deals with enriching facts and making them better for their company.
4. No More Boring Tasks: Data Science has helped various industries to automate redundant tasks. Companies are using historical data to instruct machines in order to perform repetitive tasks. This has simplified the hard jobs undertaken by humans before.
5. Data Science Makes Products Smarter: Data Science entails the usage of Machine Learning which has enabled industries to create better products tailored especially for client experiences. E.g. Recommendation Systems used by e-commerce websites provide personalized insights to users primarily based on their historical purchases. This has enabled computers to understand human behavior and make data-driven decisions.
Disadvantages:
1. Arbitrary Data May Yield Unexpected Results: A Data Scientist analyzes the information and makes cautious predictions in order to facilitate the decision-making process. Many times, the data provided is arbitrary and does not yield expected results. This can additionally fail due to susceptible management and bad utilization of resources.
2. Problem of Data Privacy: For many industries, data is their fuel. Data Scientists assist organizations to make data-driven decisions. However, the data utilized in the process may also breach the privateness of customers. The private data of consumers are seen to the parent company and may at times cause data leaks due to lapse in security. The ethical problems related to the maintenance of data privacy and its utilization have been a concern for many industries.
3. A Large Amount of Domain Knowledge Required: Another disadvantage of Data Science is its dependency on Domain Knowledge. Being a mixture of many fields, Data Science stems from Statistics, Computer Science, and Mathematics. A person with a considerable background in Statistics and Computer Science will find it difficult to solve Data Science problems without its background knowledge. It is an ever-changing, dynamic field that requires the person to keep learning the various avenues of Data Science. This allows the Data Scientists to make calculated decisions in order to assist the company. However, it becomes difficult for a Data Scientist from a different background to acquire specific domain knowledge. This also makes it difficult to migrate from one industry to another.
Scope of Data Science
There is a huge demand for Data scientists these days and some of the positions in data science such as data engineer, Senior Data Scientist, data science manager, and big data architect and the position that is offered to the candidate can depend on the expertise and skills of the candidate as well. The demand for data scientists is only increasing and will continue to increase in the future. According to IBM, an increment of 364,000 to 2,720,000 openings will be generated in the upcoming years. According to Glassdoor, Data Scientist is the number one job on its website and from PayScale.com the yearly salary of a data scientist in India is Rs. 818,099.
There is a huge gap between the demand and supply of data scientists in India. This role will stay unchanged in the future. Data Scientists in India are earning more than their contemporary IT positions. A Data Scientist in India earns an average of ₹650,000 in a year. This is well above the national average for software engineers who make ₹450,000 per annum. Therefore, data scientists in India have more scope in the sense of their salaries and added privileges. The requirement for the number of data scientists is developing at an exponential rate. This has resulted from the emergence of more recent job roles and industries. This is supplemented by the increase in data and its various types. Besides, the financial and insurance industries are becoming major players in recruiting data scientists. Foremost companies hiring them include IBM, Google, Amazon, Oracle, Microsoft, Apple, Facebook, Walmart, Visa, Bank of America, and others. About 11.5 million jobs will be created by 2026 according to the U.S. Bureau of Labor Statistics. Step up your Career in Data Science and achieve your Dream Job by Enrolling in Gologica’s Data Science Online Training NOW!!
