8 Best Data Science Careers: For Freshers to Professionals (2024)

Countless businesses and organizations are looking to acquire qualified individuals in the data science field as the job market is expanding and is fiercely competitive. Data scientists that can analyze vast volumes of data, apply their findings to help companies take sound decisions, and promote corporate growth, are in great demand, making them one of the top-paid professions in the tech sector. 

Candidates need a good educational background, technical abilities, and expertise in data analysis, machine learning, and programming to land a position in data science. The demand for data scientists is growing, and there are plenty of chances for professionals to build a career in data science. Some of the most rewarding opportunities in data science include:

• Data Scientist 
  A Data Scientist is competent in drawing conclusions and knowledge from complicated and huge data sets. They analyze and interpret data using statistical and computational methods, offering useful information and solutions to businesses and organizations. Deep knowledge of data structures, algorithms, and machine learning methods are some of the essential Data Scientist skills.
  
  To develop data-driven solutions and foster innovation, a data scientist must be able to operate both independently and cooperatively with cross-functional teams. Technical and analytical skills and a creative and inventive approach to problem-solving are required for the position. 
  
  The average Data Scientist's salary in India is ₹6,901,259; in the US is $82,898; and in the UK is £67,976.
   
• Data Engineer
  Designing, constructing and maintaining the infrastructure required for the data Extraction, Transformation, and Loading (ETL) procedures are the duties of a Data Engineer. They collaborate closely with data scientists and analysts to ensure that the data infrastructure can handle the demands of sophisticated machine learning and data analytics algorithms.
  
  Building and maintaining data pipelines, creating and implementing data storage solutions, and ensuring data consistency and quality are among the main responsibilities of a data engineer. They also put security measures to safeguard private information and enhance data processing speed.
  
  Strong programming abilities, familiarity with data warehousing and big data technologies, and knowledge of data structures, algorithms, and database systems are requirements for a successful data engineer.
  
  The average Data Engineer salary in India is ₹6,642,684; in the US is $79,792, and in the UK is £65,429.
   
• Data Mining Specialist
  A data mining specialist is responsible for decoding and evaluating huge data sets to draw insightful conclusions and patterns. They employ various data mining methods and tools to locate patterns in the data, make predictions, and elucidate undiscovered relationships. They collaborate closely with the data science and business teams to comprehend the company's goals and choose the most effective strategy for deriving insights from the data.
  
  A thorough understanding of machine learning algorithms, data warehousing, and database management is crucial for this job. They must be able to explain complex data findings to decision-makers and stakeholders in an accessible manner. A data mining specialist must be technically proficient and have strong problem-solving and critical-thinking abilities. 
  
  The average Data Mining Specialist salary in India is ₹6,410,250; in the US, it is $77,000; and in the UK is £63,140.
   
• Data Analyst 
  Data Analysts analyze huge sets of data to find patterns, trends, and insights that can guide business decisions. They interpret data using statistical and analytical methods and then produce visualizations to help stakeholders understand their conclusions. 
  
  They might also be involved in data transformation, cleaning, and quality assessment to guarantee accuracy and comprehensiveness. Data analysts need strong communication skills and technical expertise to collaborate with stakeholders and effectively communicate their findings. 
  
  The average Data Analyst salary in India is ₹3,009,488; in the US is $36,150, and in the UK is £29,643.
   
• Machine Learning (ML) Engineer
  Designing, creating, and deploying machine learning models and systems are the duties of a Machine Learning Engineer. Their responsibilities include:
   
• Choosing appropriate data sources
• Comprehending business requirements
• Preprocessing data
• Selecting appropriate algorithms
• Training models
• Optimizing model performance
• Deploying models into production
  
  Additionally, they are in charge of maintaining and updating models, keeping an eye on performance, and solving problems. A strong foundation in computer science, mathematics, and statistics is required for machine learning engineers, in addition to competence in programming languages like Python or R. They should also be adept at solving problems and being able to explain intricate technical ideas to stakeholders.
  
  The average ML Engineer salary in India is ₹5,238,760, in the US is $62,928, and in the UK, £39,541.
   
• Business Intelligence Analyst 
  Working with data to discover trends, patterns, and insights that can be applied to guide business decisions is a key role of a Business Intelligence (BI) analyst. This entails planning and putting into practice data analytics procedures, collecting and processing data from various sources, and producing reports and visualizations to present findings to important stakeholders.
  
  In addition to optimizing data collection and reporting processes, the BI analyst ensures data integrity and accuracy. In addition, they could work with other divisions to create plans for enhancing company performance based on data analysis and insights. For organizations to achieve their business objectives, data-driven decision-making is crucial, and BI analysts are a key part of that process. 
  
  The average BI analyst salary in India is ₹₹5,238,506; in the US is $62,925; and in the UK, £51,599.
   
• Big Data Engineer
  Designing, constructing, testing, and maintaining intricate big data systems are part of a big data engineer's job. These systems are made to gather, handle, organize, and analyze sizable amounts of data from various sources. A big data engineer is responsible for choosing the right technologies for data processing and storage, designing and implementing data pipelines, creating and maintaining data warehouses, ensuring the scalability and reliability of the systems, and working with data scientists and other business stakeholders to comprehend their data needs.
  
  hey are also required to be aware of the most recent advancements in the big data industry. Monitoring and optimizing the systems' performance and ensuring data security and privacy is also their responsibility.
  
  The average big data engineer salary in India is ₹7,55,777, in the US is $88,604 and in the UK is £39,541.

Starting a career in Data Science as a fresher can be done by pursuing different degrees like bachelor’s and master’s. Some professionals transition into Data Science transition from another career and may find it difficult. But the journey of a Data Scientist - whether from a different career or scratch - is possible, and everyone’s journey can be unique.

Your aim, domain knowledge, current knowledge of programming, and disciplined dedication or excellent work ethic will be critical at this time. A Bachelor’s degree in Computer Science is the most common method to break into data science. If your Bachelor’s is done in the USA, you should definitely look at transitioning into a career unless you are extremely passionate about the subject and want to do a Ph.D. 

But even in that case, 2 years of work experience can make you independent of your family for finance and give you the money required to sponsor a very expensive Ph.D. accreditation in the USA. So work is always the better option after a Bachelor’s in Computer Science.

A Master’s degree is a good option if you want campus placement. Whether you are doing it in India or the US, look out for a highly reputed institute like the IITs, the IIITs, CMU, Caltech, and other Ivy League institutes like MIT, Harvard, etc. These are extremely competitive, and currently, a 100% score in the general GRE is the minimum mark criterion to be eligible for college. 

To get a job as a Data Scientist with no experience, you can enroll in certain courses. One could be incredible mathematical skills or computer science skills to publish some research papers while still doing your Bachelor’s degree. That is a wonderful way to automatically qualify for the positions that you are aiming for.

And to even achieve awards or scholarships for your work, that will take care of the financial burden on your family, which you always must consider. Another method could be to do internships during your holidays with top-level companies that are really doing incredible work, and even going on to work for the companies that you did internships with.

A good example could be doing a key component of the Internet Search Engines that are SOTA (state-of-the-art) that use NLP (Natural Language Processing) or a vital module in a Transformers Architecture derived from or an extension to ChatGPT (another SOTA technology now known worldwide). Finally, there are always courses you can take. With these courses, you can learn recession-proof skills that will be helpful in the long run. But it is not enough to merely take courses.

You should build a concrete working-level application with the expertise you have learned from the extra courses you have taken. It could be a mobile app in Flutter with a Firebase Machine Learning component or an Amazon Web Services microservices cloud computing application that does something amazing like translate Indian vernacular languages into English. 

But in my opinion, a better way (especially financially, since master’s and Ph.D. degrees are extremely expensive, is to join a work-and-learn program where you work for a MAANG (USA) company and do your Master’s or Ph.D. degrees sponsored by the company that you are working for. This, by far, is the best option out of all the other options on this list. This is one of the best ways to go about it.

To get a job in data science as a working professional, you can smoothly transition to a data science career mid-way or as a senior. Foundational steps vary based on your unique characteristics. Many top-ranking institutes offer fundamental courses like Python and R learning. You'll need Machine Learning expertise, including Scikit-learn, EDA, Deep Learning, Reinforcement Learning, Data Visualization, and Dashboards with tools like Tableau, QlikSense, Google Data Studio, or Power BI.

You can study Data Science by pursuing online certifications and courses. Here is a list of top Data science Certifications, both free and paid:

Introduction to Python

• Learn Python 3 - Codecademy: One of the best ways to learn Python is to enroll in Learn Python 3 by Codecademy. This is a 25-hour long, beginner-friendly, and completely free certification! Python tutorials need to be practiced along with projects. Do 2-5 projects in Python during or after the course to really learn Python.
• Python Basics for Data Science - edX: Enroll yourself into Python Basics for Data Science - edX This covers not just Python but also access to IBM Watson Studio to collaborate with leading data scientists and is a very valuable certification. Learn about the basics of Python in this course.

Introduction to R

• Learn R - Codeacademy: Enroll yourself into Learn R by Codecademy and start learning about R. Python vs. R is always the data scientist’s eternal question. The answer is - if you are a software engineer, learn Python. If you are a statistician, learn R. And if you are a data scientist - learn both - because some projects will require a software paradigm, and others will require a statistical paradigm. Also, statistics is a foundational cornerstone of data science. This is a 20 hour-long course which is free and beginner-friendly.
• Programming for Data Science with R - Udacity: This is a paid course but well worth the money. Udacity is an internationally famous certification organization and in this course, you also have highly skilled international faculty members and industry experts. Just as with the Python course, you need to realize that you don’t learn a language until you’ve done at least three projects in them. Programming for Data Science with R by Udacity has built-in projects and also a valuable bonus - An Introduction to SQL, the standard database querying language which every data scientist must know.

Introduction to Statistics and Hypothesis Testing

• Intro to Inferential Statistics - Udacity: Enroll yourself into Intro to Inferential Statistics by Udacity for free and learn Statistics and Hypothesis Testing. t-tests, tests of significance, the normal distribution, ANOVA tests, and hypothesis testing are so important for a data scientist is so fundamental that it is an absolute must-have in your data science journey.
• Inferential and Predictive Statistics for Business - Coursera: If you are interested in learning about Statistics and Hypothesis Testing, enroll yourself into Inferential and Predictive Statistics for Business by Coursera. Knowing what a p-value is and applying hypothesis testing is the last step in most machine-learning projects. And the examples given in a business context are excellent for your future.

Deep Learning in TensorFlow and Keras

• Introduction to Deep Learning in Python - DataCamp: Deep Learning is one of the most in-demand research fields today, and you can enter it by enrolling into Introduction to Deep Learning in Python certification. From self-driving cars to advanced image recognition, it permeates every corner of modern-day technology. And - it uses Keras 2.0, so it's up-to-date - and free!
• Deep Learning Specialization - Coursera: This is one of the most famous and popular deep learning specialization in existence. Deep Learning Specialization by Coursera focuses on TensorFlow, you get an immediate understanding in real depth because your instructor is Andrew Ng, one of the founders of Coursera and a hero of modern deep learning.

Deep Learning with PyTorch

• Intro to Deep Learning with PyTorch - Udacity: IIt is important to learn PyTorch because most industries, especially research papers, prefer it to TensorFlow because of the ease with which one can use a GPU (Graphical Processing Unit, originally designed for computer games, repurposed for machine learning, and especially deep learning) for high-performance computing. Also, practically every research paper in deep learning uses PyTorch because it's simpler to implement. So don’t miss out, and enroll into Intro to Deep Learning with PyTorch - Udacity.
• Intro to Machine Learning with PyTorch - Udacity: This is a paid course but highly worth it since it covers all of PyTorch and not just deep learning. Another reason to highly recommend Intro to Machine Learning with PyTorch by Udacity is that every module has a project associated with it and hence it is a hands-on applied course that will be highly valuable in the data science industry.

Fortune business insights reports that the Data Science market is projected to grow by global data science platform market is projected to grow $484.17 billion by 2029 at a Compound Annual Growth Rate (CAGR) of 29% in the forecast period.

Furthermore, a report by 365 Data Science states that 49% of the job ads on LinkedIn are in the Information Technology (IT) & Tech industry. This shows there are data science job opportunities in the industry. This trend is expected to continue in the future, with the demand for data scientists outstripping the supply of professionals with the necessary skills.

In conclusion, a career in data science can be highly rewarding, with a significant potential for job growth and high salaries. As organizations increasingly rely on data-driven decision-making, the demand for data scientists is likely to increase in the future, making it a promising field for aspiring professionals.