Thanks to rapidly evolving technology and digitization across industries, many companies’ operations have witnessed massive advancements. Among several career opportunities, data science has emerged as a popular career choice today. A data science job is buzzworthy and rightly termed as the “Sexiest Job of the 21st Century” by Harvard. Tech-giants like Google, Facebook, and Amazon have generated tremendous growth opportunities for eager data nerds with challenging job roles in the field of data science.
Enterprises ranging from small businesses to multi-million industries have implemented data science to elevate their business.
Scientific use of data is nothing new. Since the past, the process of discovering trends has continued through generations and is relevant in various fields such as economics, sciene and statistics. However, with the widespread adoption of the internet and the ability to track much more data, the need for understanding big data has emerged. Online shopping, social media, and digital marketing has led to the availability of enormous amounts of data in recent years.
So what is the value in all this data? What should these companies do with all the metrics they track from their users? These questions have paved the way for forming a multi-disciplinary field known as data science.
The core of data science comprises of different educational backgrounds, namely mathematics, computer science, and statistics. Although the definition of data science is constantly evolving, it can be termed as the field that deals with structured and unstructured data to extract meaningful information for data-driven decisions with the use of statistics and scientific tools for achieving business growth.
Growing Popularity of Data Science
Data science is an asset to any enterprise. According to a recent report by Towards Data Science shows that the daily generation of data stands at 2.5 quintillion bytes. It’s clear that the amount of data is going to continue growing with time and the field needs more experts to make sense of it.
As per current projections on data generation, it is likely to witness a rise of up to 133 zettabytes by 2025. In recent years, the source of data generation has ranged from financial data, healthcare data, multimedia, sensor-based data, and logs on intelligent systems. It is crucial to handle large volumes of data effectively to derive meaningful information to add value to a business. An enormous amount of data science jobs have been listed in the job market and it has tracked a 256 percent increase in roles in the past 7 years.
Data science is multidisciplinary, therefore the inclusion of statistical techniques, math, and computer science makes this field well-equipped for working with large amounts of data. The organizations that have implemented data science into their mode of operations have gained an upper hand in the global market. By utilizing their data, they are able to find insights to help inform their decisions and predict what the future will hold. This could lead to competitive advantages that help them survive the business world.
The potential of data science has started being considered essential for organizations, thus creating a huge demand for data scientists across the world.
Is the Field of Data Science Future Proof?
A career without growth becomes monotonous and at risk of becoming redundant in the long run. This scenario drives professionals to look for changes in their career. As you are aware, the job market is a competitive place and it can be unforgiving for professionals as well. The struggles to find a suitable job while staying relevant and employable is an ongoing challenge for many individuals. If you are among the professionals looking for a career switch or a student, you need not worry, as there is a staggering increase in job roles in the field of data science. Also, a promising outlook for data science has been provided by the Bureau of Labor Statistics, which states a 15% growth in the next decade.
With a staggering increase in job roles in the field of data science, there is a constant demand for data scientists across the world. Aspiring data scientists should start to specialize in their respective area of interest, as you are likely to witness the evolution of specialized job roles and job titles within the data science field.
Technical Requirements of a Data Scientist
- Some prerequisites help in establishing a successful career as a data scientist.
- A background in mathematics or understanding of mathematical models is helpful.
- Statistical know-how.
- Proficiency in coding languages. The most popular among them is Python.
- Ability to work with large databases.
- The knowledge of analytical and data visualization tools, i.e. SAS, HADOOP, R, Tableau, SPARK, etc.
- For professionals that are seeking to climb the hierarchical ladder in the field of data science, it is of utmost importance to have a good understanding of machine learning and related algorithms in this specialization.
Data Science Skills and Usage
|Highly Preferred Skills||Technical background||Usage|
|Scikit-learn||Python||Classification, Regression, Clustering.|
|Model Building||Big Data||Statistical Analysis|
|Unsupervised Learning||Data Science||Clustering|
|Supervised Learning||Machine Learning||Prediction|
|Data Engineering||R, MATLAB||Data Pipeline|
Data Scientist Salary by Skills
The above sections highlight some of the core competencies that are essential for excelling in your career as a Data Scientist. It is important to highlight some of the skills that are high in demand with a good range of salary being offered for such experts.
Python is the most sought after skill in the data science job market. You must be aware that proficiency in Python broadens the scope of a data scientist. The average salary for Python along with R programmers in the US is $120,365.
The knowledge of data science combined with big data know-how will elevate your career earning potential. The most likely hike that you can expect with such a specialization is 25% in salary.
The familiarity with the statistical analytical system and software analytical software is a huge bonus and are expected to receive a salary of around $61,452-77,842.
Machine Learning Engineers have a base salary of $111,855 per year. However, highly experienced Machine Learning engineers can earn around $146,085 per annum. A Data Scientist with Artificial Intelligence expertise can earn an annual salary between $100,000 to $150,000.
Specialized Job Roles in the Data Science Domain
You should be aware of the existing job roles that have evolved in the world of data science. Some of the most lucrative roles are listed below.
- Data Scientist
- Data Engineer
- Data Architect
- Data Analyst
- Business Intelligence Analyst
- Machine Learning Engineer
- Database Administrator
Common Interview Questions
1) What is a p-value?
When a hypothesis test is performed with the use of statistics, a p-value determines the strength of the results. A p-value is considered as evidence against a null hypothesis. The smaller the p-value, the greater is the significance of the evidence for rejecting the null hypothesis. A p-value is a number between 0 and 1.
2) What is the difference between over-fitting and under-fitting?
In machine learning models, the occurrence of over-fitting or under-fitting is common. When a machine learning model is highly complex such as the inclusion of excessive parameters concerning the number of observations then overfitting occurs. An overfitted model tends to have poor performance for predictive tasks. On the other hand, underfitting occurs when the model can’t capture the relationship between the features of the data and the targeted variable. Such models perform poorly on the training data.
3) What is selection bias?
An error that is linked to the selection of non-random population samples. It can cause inaccurate analysis as the sample is not representative of the intended research. There are several types of selection bias namely sampling bias, time-interval bias, data-related bias, and attrition.
4) What is a Decision tree algorithm?
Decision tree algorithms are categorized under supervised machine learning algorithms. This algorithm is primarily used for regression and classification problems. The purpose of a decision tree algorithm is to be able to break down the data set into subsets. A decision tree comprises nodes and leaf nodes and capable of handling categorical and numerical data.