Back in 2012, HBR proclaimed Data Scientist as “the sexiest job of the 21st century,” and now we’re beginning to see why!
Data Science is an omnipresent power that has permeated almost every aspect of our daily lives. From education to healthcare, Data Science has applications across all parallels of the modern industry.
As netizens, businesses, and organizations worldwide continue to generate colossal piles of data (as we speak!), the importance of Data Science technologies becomes more pronounced in real-world scenarios. Technologies like AI, ML, and Deep Learning are transforming the world around us, giving birth to new opportunities in multiple industries, including eCommerce, Fintech, Healthcare, and EdTech.
Businesses of all shapes and sizes tap into this vast pool of data to innovate unique solutions and focus on personalizing customer experiences. With an increasing number of businesses leveraging Data Science technologies, the demand for skilled and qualified Data Science experts is skyrocketing.
While IBM predicts that by 2020 the job openings for all Data Science and Analytics professionals in the US would increase by 364,000 to 2,720,000, the U.S. Bureau of Labor Statistics maintains that 11.5 million new Data Science jobs will crop up by 2026.
If there’s anything that these statistics prove, it is the fact that Data Science is here to stay, and with time new jobs will emerge in Data Science, further pushing the demand for Data Science experts.
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What IBM’s dedicated company for AI means a boom in AI jobs?
IBM has long been at the forefront of Data Science, particularly Artificial Intelligence. IBM Watson is one of the greatest examples of its accomplishments in this domain. Being in the Data Science business for so long, IBM strongly believes that “fluid AI” is the need of the hour.
In simple words, the modern industry needs AI technology that is fluid, meaning it can quickly adapt to the situation at hand. This advanced technology can apply knowledge and models for a specific domain to new use cases and challenges. They need AI that can combine different forms of knowledge, unpack causal relationships, and learn new things independently.
To accomplish this goal, IBM has already started developing new-age AI engineering tools. The four core workstreams propelling this vision are:
Neurosymbolic AI is a higher and more advanced projection of Deep Learning. It aims to leverage neural networks’ power with symbolic methods to improve and optimize AI’s reasoning abilities.
IBM aims to deliver robust, secure, and trusted AI that focuses on explainability, accuracy, and bias reduction.
The goal is to develop AI engineering tools and capabilities to simplify and automate routine tasks like data preparation, model training, and software lifecycle management.
IBM is working to develop a disruptive class of AI hardware that is energy- efficient. This AI hardware can scale up computing power and potential without increasing energy demand and consumption.
Gradually, as these technologies roll out into the market, businesses in the US will come forward to adopt them into their Data Science infrastructure. Naturally, these new-age technologies will give rise to new job roles, further fuelling the AI job boom.
This will push the demand for experienced and talented Data Science and AI experts who can efficiently leverage these technologies to their full potential. In the future, AI will carve out a clear career path for employees by outlining specific skillsets for Data Science jobs.
Career Opportunities for Data Scientist in the US
The Data Scientist’s job profile ranked no.1 in LinkedIn’s most promising jobs in the US in 2019, reporting over a 56% increase in job openings YoY. Stats suggest that Data Scientist job roles have grown over 650% since 2012!
But, who are Data Scientists?
According to HBR, a Data Scientist is “a high-ranking professional with the training and curiosity to make discoveries in the world of big data.”
A Data Scientist is a multi-talented expert well-versed in mathematics, statistics, computer science, and information science. They specialize in collecting data from disparate sources and analyzing large datasets containing structured and unstructured data to gain meaningful insights.
Once they extract these insights and data patterns, they use a combination of advanced scientific, statistical, and analytical methods to interpret the findings and convert them into actionable business decisions.
Data Scientists combine the best of both worlds – business and IT. Their primary focus is to study and analyze Big Data to help companies and organizations to make highly-specific, targeted, data-driven decisions.
Thanks to Data Science’s extensive range of industry applications, Data Scientists can work in almost any US industry. They can find employment opportunities with high salary compensation and job growth in education, eCommerce, retail, IT, BFSI, healthcare, pharmaceuticals, manufacturing, supply chain & logistics, transportation, media & entertainment, aerospace/aeronautical research, and military.
Like we mentioned earlier, Data Scientists can work for companies of all sizes – from startups to large corporations, MNCs, and research centers.
Career Pathway to Becoming a Data Scientist
Here’s a rough sketch of how you can progress to becoming a Data Scientist:
1. Earn a bachelor’s degree
The best entry pathway to Data Science is to earn a bachelor’s degree in a field closely related to Data Science. You can choose mathematics, statistics, computer science/engineering, IT, and information technology as your undergraduate majors since these fields are intricately linked to Data Science.
2. Earn a master’s degree
Data Science is a highly complex field of study, and hence, it requires aspirants to complete their postgraduate studies in any one of the subjects mentioned above. Most companies look for candidates who have a master’s degree or higher (Ph.D.) for Data Science job roles.
3. Acquire the right skills
Data Science aspirants must develop niche skills, including programming; data structures; data mining, cleaning & munging; data visualization & Reporting; statistical analysis; risk analysis, and data warehousing. Apart from these skills, they must also gain soft skills like effective communication, critical thinking, problem-solving, organizational, and management skills.
4. Bag an entry-level job
After completing the first three steps, you should be ready to take on an entry-level position in Data Science (for example, Junior Data Analyst, Business Intelligence Analyst, Data Engineer, etc.). Make sure to choose a job role in a company that allows freshers room to grow and learn. It will help if you create a detailed online portfolio highlighting your skills, expertise areas, and projects. This will help you attract the attention of potential employers.
5. Opt for Data Science Certifications
Data Science certifications are an excellent way to gain industry knowledge and niche skills. You learn the nitty-gritty of Data Science skills like programming and data analysis using Big Data tools like Hadoop, Hive, Spark, etc. Companies usually tend to prefer candidates who have additional certifications along with a master’s degree since it shows their willingness to upskill and enrich their knowledge.
Top Data Science Jobs in the US
Data Scientist is not the only trending job profile in Data Science. There’s a whole range of challenging and well-paying Data Science job roles worth considering. Here are the top eight Data Science jobs in the US:
1. Machine Learning Engineer
Machine Learning Engineers are highly skilled experts specializing in Machine Learning. They design, build, operate, and manage intelligent models/systems that can perform a wide range of tasks by learning from experience. ML Engineers develop ML algorithms to train these models to perform human-like tasks with little or no human supervision. They work with Data Scientists and Data Analysts.
Machine Learning Engineers must be well-versed with mathematical, statistical, and computer science concepts. They must be proficient in multiple programming languages like Python, Java, Scala, R, C, C++, etc.
2. Data Architect
A Data Architect evaluates and analyzes a company’s data needs, outlines the roadmap to meet these requirements, determines the standards and goals for data management, and ensures that all the goals and needs align perfectly with the company’s overall strategy and business architecture.
They develop and implement database solutions to store and retrieve business data and oversee data migration activities. Data Architects also use different analytical and statistical techniques to analyze information and present their findings through detailed reports to the upper management and company stakeholders.
Data Architects must be proficient in applied mathematics and statistics. They must have extensive knowledge of data visualization, data migration, RDMS, database design, database management, and cloud computing.
Statisticians gather, analyze, and interpret numerical data to identify trends and spot patterns that can help companies understand quantitative business data and make informed decisions. They use mathematical techniques, statistical methodologies, and computer software to make sense of complex data and make accurate business predictions.
Statisticians must know how to employ statistical terms, concepts, and methodologies to different situations and business cases. They must possess analytical, problem-solving, and communication skills.
4. Full-Stack Developer
As the name suggests, Full-Stack Developers are highly skilled in full-stack technology, which means they can handle both server-side and client-side programming. They develop the core logic that runs in the background (powering the website/application), and they also design the user interface (UI) with which the users interact. Full-Stack Developers manage the end-to-end life cycle of software applications.
The average annual salary of Full-Stack Developers in the US is $95,308, and the typical salary range lies between $81,210 – $107,541 per year.
6. Business Intelligence (BI) Developer
BI Developers are professionals responsible for designing, deploying, and maintaining a company’s BI interfaces, including query tools, data visualization dashboards, and data modeling tools. BI Developers are aware of business domain peculiarities and challenges, which allows them to understand business requirements and implement BI solutions accordingly.
BI Developers must have a Database/Database Administration background and experience in working with BI tools. Besides exceptional data analysis and business analysis skills, BI Developers must be skilled in debugging/troubleshooting. They must be familiar with database management systems, online analytical processing (OLAP), and ETL (Extract, transform, load) framework.
The average annual salary of BI Developers in the US is $94,897, and the typical salary range lies between $80,150 – $107,575 per year.
7. Big Data Engineer
Big Data Engineers specifically deal with all-things Big Data. In this respect, they perform a host of functions. They design, build, test, implement, and maintain Big Data solutions.
They also develop large-scale data processing systems using Big Data tools like MapReduce, Hadoop, Spark, MongoDB, Cassandra, etc. Big Data Engineers evaluate the software, hardware needs of a company. In essence, they are experts who manage the complete data infrastructure and architecture of an organization.
Big Data Engineers are usually proficient in Software/Data Engineering. In addition to that, they are also well-versed with coding, software development, object-oriented design, data mining, data visualization, and statistical & quantitative analysis.
8. Data Analyst
Data Analysts collect, organize, process, and analyze large volumes of data. They use different analytical and statistical tools to study data and extract relevant patterns from within massive datasets.
Data Analysts must present their findings in consolidated reports and visualizations in ways that are legible to technical and non-technical members of an organization. Thus, data analysis and data visualization are the two most crucial job responsibilities of Data Analysts. Through their findings, they help companies to devise data-centric solutions to business problems.
Data Analysts must possess strong business acumen along with other technical skills like programming, data mining, cleaning, & munging, data visualization, data warehousing, and business intelligence.
The average annual salary of Data Analysts in the US is $76,920, and the typical salary range lies between $67,585 – $85,873 per year.
Learn Data Science online: The upGrad Advantage
By now, it’s evident that Data Science is one of the most happening and promising domains of the 21st century. Data Science jobs boast of fast career growth and hefty annual packages. Thus, anyone who wishes to capitalize on this fantastic opportunity must enroll in data science courses or certification programs.
upGrad offers three fantastic Data Science courses in association with IIIT-Bangalore that can help you kickstart a successful career in Data Science:
This diploma course is a 12-month program that extends five specialization options – Deep Learning, NLP, Business Intelligence/Data Analytics, Business Analytics, and Data Engineering. The course includes 14 programming tools/languages and over 60 industry projects.
This is a 7-month short term Data Science course covering Python programming, predictive analytics using Python, visualization using Python, basic & advanced SQL, basic & advanced ML algorithms, and EDA. It includes more than 7 case studies and industry projects.
This is an 18-month Master’s program in Data Science that also offers five specializations – Deep Learning, NLP, Business Intelligence/Data Analytics, Business Analytics, and Data Engineering. The course includes over 60 case studies and projects.
These programs are designed for working professionals, and hence, these courses are the perfect choice if you’re looking for an exciting turning point in your career. upGrad courses come with the promise of dedicated career assistance, personalized mentorship, timely doubt resolution, and global access opportunities.
We hope this helps!
What is the difference between data science and data analytics?
Though data science and analytics are two sides of the same coin, each have their own unique approach. Data science is a combination of multiple disciplines including mathematics, computer science, statistics, machine learning and artificial intelligence. It includes concepts such as data mining and predictive modelling. On the other hand, data analytics is mainly concerned with statistical analysis and mathematics. While the former focuses on finding meaningful correlations between large datasets, the latter aims at finding unique solutions to complex questions to drive data-driven innovation.
How to get an entry-level data analyst job in 2021?
If you’re looking to establish a successful career as a data analyst, it is important you already have certain skills including communication skills, strong analytical skills, pattern recognition, self-motivation and problem-solving abilities. Besides this, fluency in computer programming languages is an added advantage. Even if you don’t have real work experience, if you’re proficient in areas such as machine learning, artificial intelligence and data warehousing, you can bag an entry-level position.
Is it worth doing a data science certification?
If you’re someone who is just starting off with little or no experience, doing a data science certification is worth it. However, it depends on what your level of expertise is. Instead of doing a full-fledged program, you can even choose a diploma or PG certification like the one offered by upGrad. It is a seven-month program that includes Python programming, data analytics, data engineering and advanced SQL. Aspirants also gain real experience by working on case students and industry projects.