If you are here, it means you want to explore a data science career options. Today everyone is surrounded with data- be it professional data or personal data, be it raw data or organized data. A couple needs to remember their first date, and HR needs to find out how many hours an employee is working, Fitness freaks need to know how many calories they burned today, F1 racer needs to know the car tire pressure in real-time and the list goes on…
Data science is a field that combines data-related analytical techniques and scientific theory to generate insights for business stakeholders. Its shape, elements, and dimensions allow organizations to optimize their operations, identify new business opportunities, and reduce the functional performance of departments such as marketing and sales.
The Word “Data Science” according to Google Trends is the most trending word from the last five years. We can see from the graphs below that the interest and searches for this term are only increasing with time.
Simply put, data science creates a competitive advantage over competitors by optimizing operations with unmatched data throughput. According to the Forbes report, data-driven companies are 23 times more likely to win customers and 19 times more likely to be profitable. Let’s read more about data science methods, methodologies, and the future of data science.
What is the global market value of the data science industry?
There are several reports that can be viewed to measure the global value of the data science industry. According to Grand View Research, the global market for data science platforms in 2019 was estimated to be the US $ 3.93 billion. According to other Statista data, the global big data market is expected to be worth the US $ 64 billion by 2021. In data analysis, another key area of data science, research shows that North America had the largest share of the market in 2019 with a market value of just over $ 10 billion. Europe was the second-largest market in 2019 and was worth about $ 6.43 billion.
What does the data science labor market look like?
The role of a Data Scientist is one of the most demanding jobs in both the United Kingdom and the United States. This role was featured in the LinkedIn Emerging Job Report 2020 in both countries and was ranked 3rd in the United States and 7th in the United Kingdom. In the United States, Lille’s annual growth rate is 37%. The report also emphasizes that “as some industries, such as insurance, prepare for the future, data scientists can extend the responsibilities traditionally held by statisticians.” In the UK, it was down from 3,162 in the same period last year. A similar trend was seen in the United States, where data scientist job listings stagnated between 2019 and 2020. But, this decrease can be easily explained by the effects of the COVID 19 pandemic. Other data for 2020 show a shortage of about 250,000 professionals with security and data science skills, clearly showing the need for professionals with the right skills.
Data Science and Analytics in Business
Data science for business decision-making is already a reality. It is the basis of the entrepreneurial spirit of the information age. This application goes beyond pure extrapolation of knowledge. Carefully selected results will help to maximize the effect. A common example is to reuse data to graphically represent a buyer’s persona. It can be especially used for marketing campaigns and brand building. Experts with
Data science skills know how to find meaningful information from the data they encounter. It leads the company in the correct direction. The company needs strong data-driven decision-making, for which he is an expert. Data scientists are experts in various fields of statistics and computer science.
They use their analytical skills to solve business problems. Some of the key benefits that data science professionals bring to their business with enhanced data science skills are improved ROI, improved sales, streamlined operations, faster product lead times, customer retention, and satisfaction. It is an improvement in degree.
Why Build a Career in Data Science or Analytics?
Over the last decade, the demand for data availability, data science skills, and data-driven decision-making has skyrocketed. Further highlighted by the dramatic changes in business operations and consumer behavior caused by the COVID 19 pandemic, analytics, and data science have established themselves as an indispensable navigation tool across industries and functions.
Earn High Salary
According to data from the big-paying Robert Half, the median starting salary for data scientists is $ 95,000, almost double the average salary in the United States. At around $ 70,000, even the median salary of data analysts is a more entry-level position, significantly higher than the median salary in the United States.
According to Burtch Works survey, work experience is the number one factor in data science payroll. Mid-career data science professionals with at least seven years of experience can expect an average revenue of $ 129,000. Experienced data scientists with leadership can earn over $ 250,000. But education, enterprise size, and industry are also key factors in determining data science salaries.
Solve Complex Problems
If you enjoy solving complex real-world problems, you’ll never get bored as a data scientist. The main responsibility of your job is to find answers and insights by analyzing and processing large amounts of raw data. Here are some examples of business problems you’ll have to solve:
- Find ways to increase sales
- Discover features that differentiate your target audience segments.
- Look for potential opportunities in different data sets.
- Identify undetected problems in the normal course of business.
- Build an infrastructure that helps an organization ingest and centralize all of its data.
Philip Rigoletto, associate professor of mathematics and statistical data science at the Massachusetts Institute of Technology, said: “It also applies to data science. Whatever your area of interest, you can be assured that you have the data to improve it. Being able to extract information from the data is actually from marketing to health, we are in a very strong position to collect data in all aspects of society, from sports to entertainment.”
Avoid Job Automation
The role of data science, especially data analysts, has a very low risk of automation for several reasons.
- The demand for data science roles is increasing at an average rate of 50%.
- Few platforms can create advanced analyses.
- Most automation is done by data scientists.
From Analyst to Data Scientist
There are two main ways to use your data science skills to grow your data-centric career. Either become a data science expert (pursuing a job as a data analyst, database developer, data scientist, etc.) or move to an analytic role like a functional business. Analyst or data-driven manager. Both career paths require basic skills and knowledge of data analysis, programming, data management, data mining, and data visualization.
Despite the dual-track, the evolving nature of the relatively new field means that the career path is flexible. Data science professionals, such as data analysts, can focus on the role of data science or data system developer, depending on where they are deepening their expertise.
By expanding our knowledge of artificial intelligence, statistics, data management, and big data analysis, data analysts can move to the role of a data scientist.
By building existing technical skills with Python, relational databases, and machine learning, data analysts can become data system developers. Many of these skills can be learned independently through hands-on experience or through online data science courses. This guide focuses primarily on the career path of data science.
What does a Data Scientist do?
Data scientists answer business questions from the context of the data. They tend to use the data to develop new product features and do more modeling and open research. We spend a lot of time cleaning up our data to make sure it can be used with models and machine learning algorithms. If you watch Netflix and see a personalized list of recommended shows, machine learning algorithms, and data science are working.
In addition, a subset of data science work is predictive analytics. “Predictive data analysis, as the name implies, is more complicated because it predicts what might happen in the future based on historical data or data crossovers between multiple datasets and sources,” Rafael said. Amazon Web Services Partner Solutions Architect and Instructor to Get Started with Data Analysis on AWS. “In short, we try to predict the future based on past behavior. The use of neural networks, regression, and decision trees is widely used in the diagnostic analysis.”
Core Data Science Skills
All data is large or complex datasets that traditional data processing software cannot manage. For this reason, data scientists should be familiar with the open-source distributed processing system Apache Hadoop or Apache Spark.
Data modeling is the process of formatting specific data in a database.
Data visualization is a graphic representation of the data to present trends and insights.
Machine learning is a set of techniques used to predict and predict data.
Knowledge of programming languages such as Python and R is essential for automating data manipulation.
You don’t have to be a statistician, but you need to know the format of the statistics that will be applied to interpret the data.
Data scientists do not work in silos. Data scientists are often part of a large data science team of data engineers, software developers, and more.
In Short, A Data Analyst is responsible for:
Answering questions about the data. Unlike data scientists, data analysts are not intended to use data to identify trends or understand the future of the business. Your job is to analyze historical data and create and run A / B tests on your product or design system. Data analysts need to understand how data is stored, stored, and consumed by tools such as Tableau.
Core Data Analyst Skill
A / B Testing
A / B testing is a statistical approach used to compare two versions of a variable in a controlled environment. A / B testing is used to determine which variable version is performing better.
Domain knowledge can be thought of as a specialty. For example, if you have extensive retail experience, you have retail domain knowledge.
Microsoft Excel is often used to manage small datasets.
Like data scientists, data analysts need to know how to use data to tell their stories to interested parties using data visualization tools such as Tableau.
Data analysts must have skilled programming skills in languages such as R and Python.
SQL is a database language used to manage data and build database structures. SQL is often used instead of Excel because it is suitable for processing large amounts of data.
As a data analyst, you need to report data insights. In short, you also need good communication and presentation skills.
Data analysts are generalists.
That is, work across multiple teams and roles. I enjoy working on well-defined and structured issues. Use the data to extract and create reports that are valuable to your business. Successful data analysts generally enjoy some complexity, but not as much as data scientists. Here’s how to determine if you are eligible to become a data analyst:
- You are a generalist
- You enjoy working beyond functionality
- Enjoys solving specific problems
Whereas, Data scientists love complexity.
You enjoy answering a wide range of amorphous questions. You are successful in project-based tasks and enjoy providing insights. Data scientists are less likely to tackle different tasks than data analysts. Therefore, you may be suitable for your career as a data scientist if:
- Enjoying the complexity
- You enjoy ambiguity
- You like delving into a single question
- You`re okay with not finding an answer to a problem
Data scientists are still in high demand and in low supply. According to IBM, this trend will continue strongly over the next few years. Another reliable source of information that agrees with this statement is the United States. Labor Statistics Bureau. The US Bureau of Labor Statistics has achieved strong growth in the field of data science and predicts that employment will increase by about 28% by 2026. Given that 28%, there are about 11.5 million new jobs in this area.
In the long run, it would be unwise to oppose data science as a career move, especially when expanding into relevant positions such as research engineers and machine learning engineers. So, is data science still an ambitious career in 2022? The answer is certainly yes! Global demand for data scientists remains unabated and there is no competition for these jobs, making data science a highly lucrative career path option. There are many indications that the data revolution has just begun. According to the latest big data and AI executive surveys, only 39.3% of organizations currently manage data as an asset. Many people are aiming for it, but only 24.0% have created a data-driven organization. These numbers are expected to increase over the next few years.
Technologies such as AI and machine learning are now commonplace, but when it comes to data science, there is plenty of room for improvement and they are widely used.
Demand for data science jobs will remain high for the next few years, as evidenced by the projected growth of various related industries. In addition, all the data science statistics presented here represent a thriving industry with great potential.
The above article is written by Naman Singhal as a Co-author under the guidance of our trainer, Bharti Goel.
Bharti is one of our trainers who takes training in Data Analytics (Excel, Power BI, DAX, SQL, HR analytics, etc).
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