Why Data Science is future job?
Data science is the process of using algorithms, methods, and systems to extract ideas and ideas from structured and unstructured data. It Uses analytics to help users predict, improve optimization, and improve operations and decision making. Today’s data science teams are expected to answer many questions. The company requires better prediction and optimization based on real-time knowledge supported by tools like these.
Data science keeps evolving as one of the most promising and demanded career paths for professionals.
Today, successful data professionals know that they need to go beyond traditional skills of analyzing large amounts of data, data mining, and programming. In order to discover the intelligence that is useful to their organizations, data specialists need to master the entire data science lifecycle and have a level of flexibility and understanding to maximize performance at every step of the process.
The data used for analysis can come from multiple sources and can be present in multiple formats. Now we understood what data science is, let’s see why data science is essential in the current scenario.
What is the History of data science?
It is a branch of computer science. The terminology was first used in 1960 by Peter Naur, a pioneer in computer science. He describes the fundamental aspects of techniques and approaches used in data science in his 1974 book, Concise Survey of Computer Methods.
In 1996, A computer scientist called William Cleveland introduced data science as a discipline in his 2001 paper entitled “Data Science: An Action Plan for Expanding the Technical Areas of Statistics”, published in the International Statistical Review in 2001. Over the years, it has become the most sought after and fastest research technique in modern technology.
Recently, the U.S. Office of (OPM) authorized organizations to use a parenthesis of (data scientist) as well as the professional title for positions that perform data science work as an important part of the work. The OPM has determined that data science work is included in a variety of professional series, including but not limited to work in epidemiology, actuarial sciences, operational research, statistics and information technology.
Why Data Science is future job?
Data science or data-driven science helps improve decision making, predictive analytics, and model discovery allows you to:
- know the root cause of a problem by asking the right questions
- Conduct an exploratory data study.
- Model Data Using Multiple Algorithms
- Communicate and visualize results through charts, dashboards, etc.
- Plan routes andconnecting flights.
- Create predictive analysis models to predict flight delays
- Offer personalized promotional offers based on customer booking templates
- Decide what type of aircraft to buy for better overall performance.
Modern data science has emerged in the field of technology, from optimizing Google search rankings and LinkedIn recommendations to the influence of the titles of Buzzfeed publishers. But it is on the verge of transforming all sectors, from retail, telecommunications and agriculture to health, truck transportation and the penal system. However, the terms “data science” and “data scientist” are not always easy to understand and serve to describe huge number of data-related work.
Demand
According to LinkedIn’s 2017 US Emerging Jobs Report, the number of data scientists has increased by more than 650% since 2012. However, there are still very few people who exploit opportunities in this area. Why did it grow up so fast?
Businesses need to use data to manage and grow their daily outcomes. The main goal of data science is to help companies make faster and better decisions that can get them to the top of their market.
In addition, you can apply machine learning to smaller datasets, such as those on a local company’s social media or purchase gift card history. This offers even more opportunities and increases the demand for data specialists. Employment growth over the next decade is expected to exceed that of the last ten years, creating 12 million jobs by 2026, according to the U.S. Bureau of Labor Statistics. Businesses are building their data science teams to integrate data analytics and make it an essential part of their success. Why are these analyses extremely important? Does it worth working for one of these companies? You will find the answer in the following two chapters.
Influence
Data science is changing the way decisions are made and companies are adapting a large-scale, data-driven approach. Data-driven thoughts, made with advanced data analytics, benefit all types of businesses, from global giants to SMEs, to local businesses looking to move forward. Lack of data is rarely a problem: mountains are collected every second, and we begin to understand the potential and influence they can have. Datasets in good hands can help predict and shape the future.
The problem is getting the mixed datasets. The role of Data Scientist is to transform organizations from responsive environments with static and old data to automated environments that continuously learn in real time. Forecasts are simple: data is a valuable resource and investing in it will certainly be profitable.
Data science is interdisciplinary practice involving a wide range of information and generally takes into account the overall picture more than other analytical majors .
In business, the aim of data science is to provide consumer and campaign information and to help companies develop strong plans to engage their audiences and sell their products.
Do we need more data scientists?
Now, knowing that data science is in great demand, you probably wonder who will do all the work. Do we have enough data scientists? Perhaps the market is already at the level of experts. Nothing can be further from the truth: data scientists are few and far away, and they are highly sought after. IBM expects that the demand for data specialists will increase by 28% by 2020. Machine learning and data science generate more jobs than experts to fill them, which is why these two areas are now the fastest growing areas of technological employment.
Data scientists establish a solid database for rational analysis. They then use online experiments, among other methods, to achieve sustainable growth. Finally, they create machine learning pipelines and custom data products to better understand your business and customers and make better decisions. In other words, in technology, data science is about infrastructure, testing, machine learning for decision making and data products.
Prerequisites for Data Science
Here are some of the terms you need to know before you start learning what data science is.
1- Machine Learning
Machine learning is the pillar of data science. Data scientists must have a strong understanding of ML, in addition to basic statistical knowledge.
2-Modeling
Mathematical models allow you to perform quick calculations and predictions based on what you already know about the data. Modeling is also part of ML and involves identifying the best algorithm for solving a given problem and how to form these models.
3- Statistics
Statistics are at the pillar of data science. A robust handful of statistics can help you extract more intelligence and achieve more meaningful results.
4- Programming
A certain level of programming is required to carry out a data science project. The most common programming language is Python especially becasue it is popular and it is easy to learn, and it supports multiple libraries for data science and ML.
Why should I become a Data Scientist?
Let’s start from the bottom of Maslow’s human needs pyramid, which is secured with money. According to Glassdoor, data science was the most paid unique profession in 2016.
If the data is money, as they say, it should not be a surprise. The combination of skills needed to make data science the right track is not common. If you want to become a data specialist and are ready to grow, it is very likely that you will succeed. An experience in mathematics, statistics or physics is a good foundation to build. You do not need to have completed a data science program.
Not everything is just the promise of autonomous cars and artificial intelligence.
Many guests are skeptical not only about the fetishization of artificial general intelligence by traditional media (including titles such as “An AI god will emerge by 2042 and will write his own bible” from VentureBeat. Will you like it? ), but also the buzz around machine learning and deep learning. While machine learning and deep learning are powerful techniques with important applications, as with all buzz terms, healthy skepticism is in order. Almost all of my guests understand that data scientists are doing their daily work through data collection and data cleansing, creating dashboards and reports, data visualization, statistical inference, communicating results to key stakeholders, and convince decision makers of its results.
Ethics is one of the biggest challenges on the ground. You can infer that the profession offers its practitioners a lot of uncertainty. When I asked Hilary Mason in our first episode if another major challenge faced the data science community, she said, “Do you think imprecise ethics, lack of rules of practice, and lack of coherent vocabulary aren’t enough challenges for us today? ”
What is Data scientist salary?
The additional responsibilities and expectations of working abstract on a massive scale represent more than double that of a data analyst. According to Glassdoor, the average number for data scientists in the United States was $117,346 in October 2019.
Challenges of the Job
Although it is considered one of the best jobs in consistent annual surveys, data specialists continue to face some of the setbacks of statisticians and those performing similar functions. Although they are often hired to make sense of large information systems, they are not always given specific questions to ask or instructions for conducting their research. Many companies ask their employees to complete their data science work without investing the money in a complete data science team. They also sometimes present incorrect or disorganized data, called dirty data, which can incorrectly distort the results of their models.
What is the differnce between Data scientist vs. data analyst job ?
The role of the Data Scientist is often confused with that of a data analyst. But while there are overlaps in many competencies, there are also significant differences. Although the role of a data analyst varies by company, these professionals typically collect data, process data, and do statistical analysis using conventional statistical tools and methods.
Analysts also identify patterns and correlations between datasets to identify new opportunities to improve business processes, products, or services. In some cases, data analysts also design, create, and maintain big data systems and relational databases which is alos in their job descriotion. The average salary of the data analyst in the United States in October 2019 was $67,377, according to Glassdoor.
Data specialists are responsible for these and many more tasks in their job. These professionals are equipped to analyze big data using advanced analytics tools and should have research experience to develop new algorithms for specific problems. They can also be entrusted with the task of exploring the data with no specific problem to solve. In this scenario, they need to have a good understanding of data and business to ask questions and provide information to business leaders to improve business operations, products, services or customer relationships.