Top 5 In-Demand Data Science Subjects to Learn in 2023

A Business World report reveals that the Indian data science and analytics industry saw a 26.5% year-over-year growth in market value in 2021. Moreover, according to a Hindu Business Line report, the number of analytics and data science jobs in India has also increased by 30%. Given the incredible demand, there is no wonder that there is a spike in those interested in data science subjects.

Developing employable data science knowledge and skills requires aspirants to enroll in courses that increase their ability to look at problems and think about what data sets and statistical methodologies could help discover solutions. 

When choosing a data science course, look carefully for data science subjects that help establish one as an expert and increase their influence in data-related decisions. For instance, if an aspirant is interested in healthcare, choosing a course with subjects like healthcare analytics and the fundamentals of machine learning in healthcare can make them a sought-after expert in the field. 

However, how would one know which data science subjects would help them become experts? Here’s a detailed guide to the essential data science subjects in 2023. 

What are the Essential Data Science Subjects to Learn in 2023?

Gartner recently identified the top data science trends for 2023. Managing AI risk, data shareability, and data sustainability were some of the items on the list. Let’s look at the top five data science subjects to help aspirants navigate the dynamic data science field and use these trends to scale their careers and organizations. 

1. Data Visualization

Data visualization is a powerful and effective way to communicate data-driven findings to different business entities. It uses charts, images, and graphs to communicate its findings to internal and external stakeholders. It teaches aspirants how to leverage data and present it in a way that effectively reveals valuable insights to key stakeholders. 

2. Cloud Computing

Cloud computing uses cloud-based technologies and platforms like AWS or Google Cloud to store and process data. Simply put, it’s like having a virtual storage room that one can access from anywhere at any time. Instead of storing data and computing resources on local machines or servers, companies can use cloud computing to access these resources through the Internet. A data scientist generally analyzes and stores different types of data. Therefore, having sound knowledge of cloud computing helps them store large data sets safely. 

3. Machine Learning

Machine learning is the application of algorithms and statistical models to make data predictions. It’s a subset of artificial intelligence that enables computers to improve their performance on a specific task by learning from available data without any human intervention.  

Proficiency in machine learning allows data science professionals to extract valuable insights from large and complex data sets and develop predictive models to drive better business decisions. The recommendations that you see on Netflix are powered by machine learning and other algorithms. Did you know that its recommendation engine saves Netflix more than $1 billion every year? Imagine how valuable an asset would a data scientist be if organizations were to save billions

4. Data Governance

For data to deliver value and drive business, it must be readily available, high-quality, and relevant. Good data governance ensures data has these attributes, which creates more value for businesses. The last thing an organization wants is to use incomplete, obsolete, or irrelevant data. Therefore, data governance is a popular data science subject that helps professionals determine whether data is accurate and secure for analysis. 

5. Deep Learning

Deep learning is a subset of machine learning based on artificial neural networks. It focuses on creating high-quality algorithms that can keep learning from the data fed into them. Data science professionals are expected to use deep learning for image and video processing or computer vision applications.

ALSO READ: Data Science Course Eligibility Criteria, Syllabus & Skills

How to Develop Expertise in Essential Data Science Subjects

Here’s how aspirants can become experts in essential data science subjects:

Research

Aspiring data science professionals can look through academic papers, industry reports, and case studies to gain deep insights. 

Enrolling in a Course

Join a data science course that offers essential data science subjects, especially ones that are relevant to you. Look for courses that specifically cover the emerging subjects you wish to master. Choose reputable institutions or platforms that provide comprehensive and up-to-date content. 

Getting Hands-on Practice

To develop expertise in data science subjects, you should apply your learning to real life via projects and case studies. 

What are the Most In-Demand Data Science Roles and Their Salaries?

According to a Times of India report, the adoption rate of data science and analytics in India is 74.5%. Large companies are using data science to gain a competitive advantage. To make the most of data, companies need executives capable of drawing insights and using them to scale business activity. Here are five in-demand data science professionals who can help companies make the most of data generated from different sources. 

1. Data Scientist

Average Salary: ₹9,00,000 to ₹9,90,000 per annum

Data scientists collect, analyze, and interpret vast amounts of data. They use advanced technologies such as machine learning and predictive analysis. Data scientists usually work in collaboration with technology experts to mine big data, which companies can use to predict customer behavior and identify new revenue streams. 

2. Data Engineer

Average Salary: ₹7,00,000 to ₹7,50,000 per annum

Data engineers create the infrastructure for data scientists to do their work. They build and test big data ecosystems that data science professionals use to analyze and optimize vast amounts of information. 

3. Data Analyst

Average Salary: ₹4,00,000 to ₹4,10,000 per annum

Data analysts work with large volumes of raw data and transform it into a form that’s easy to understand. This involves data wrangling, where the analyst turns raw data into charts, graphs, or images that are easy for key business entities to understand. The ability to present data in visually appealing forms is extremely pivotal, as those in leadership positions might not be able to identify patterns with raw data. All big business decisions must stem from data. 

4. Big Data Engineer

Average Salary: ₹7,50,000 to ₹7,90,000 per annum

A big data engineer is a professional who is responsible for developing, maintaining, analyzing, and evaluating a company’s data. Big data engineers help companies improve their efficiency, profitability, and scalability by interpreting big data. 

5. Data Architect

Average Salary: ₹20,00,000 to ₹25,00,000 per annum

Data architects are IT professionals who leverage their computer science and design skills to review and analyze an organization’s data infrastructure. You will find data architects in every industry you can possibly imagine.

Note: All salary figures are extracted from Ambition Box. 

How to Stay Relevant in the Fast-Changing Data Science Industry

Data science is a competitive field, with thousands of people with similar skills, experience, and knowledge running to make a successful career. A LinkedIn report shows 79% of data scientists have earned a graduate degree. Aspirants can stay ahead of the competition by doing the following: 

• Follow industry trends 
• Attend industry events and conferences 
• Upskill by enrolling in short-term courses 
• Find mentors in the data science field

ALSO READ: The Importance of Data Science Courses for Success as a Professional

How Can Emeritus Help Accelerate Your Data Science Career

Emeritus’ data science courses have a comprehensive and hands-on learning curriculum that equips learners with knowledge about tools and techniques for handling, managing, analyzing, and interpreting data. Joining these courses also enhances aspirants’ skills to make effective data-driven decisions to scale their businesses.