5 High-Paying Data Science Career Paths in 2024

After training thousands of students and providing them with the best placements, we have experienced that a practical approach to learning data science is important. This career path has numerous opportunities for students but only if they have their foundational knowledge cleared and practice regularly.

With our experience of training data scientists over 6 years, we have carefully designed the data science syllabus along with putting together multiple real-world projects to prepare you for the industry.

We have shared some of our thoughts and research data on the data science career path with you through this guide that will help you make better decisions.

Here are the in-demand data science career paths

1. Data Analyst

Data Analyst is an entry-level data scientist job role, where most of their tasks revolve around collecting, processing, and analysing data to help organisations make informed decisions. As a data analyst, you can work on different projects in multiple industries.

Years of experience required: 0-1 years

Job responsibilities: 

• Data Analysts are responsible for collecting data from various sources like internal databases of companies, publicly available data, and third-party datasets
• They need to perform exploratory data analysis to understand underlying data patterns and characteristics, statistical analysis to interpret data, identify trends and make predictions, and predictive analysis to predict future trends based on historical data 
• You need to use different data visualisation tools like Tableau and Power BI to create dashboards and reports, providing easily understandable complex data insights to stakeholders
• They take part in the strategic planning process of an organisation and provide valuable data-based insights, for identifying opportunities for improvements and areas of growth 

Key skills required:

• To begin with a data analytics career, you must have training in data analytics or in related analytics domains
• Acquire knowledge in advanced Excel including Pivot tables, vlookup, and macros, 
• To become an expert you need to have experience in handling large datasets and relational databases and have the ability to write and optimise SQL queries
• Learn programming languages like Python, R, and SQL to manipulate data, perform statistical analysis, and create data visualisations
• You must know how to use data visualisation tools like Tableau and Power BI to transform complex data into understandable visual formats 
• Get a proper understanding of statistical analysis to summarise and interpret data, make predictions and inferences, and evaluate theories or hypothesis
• To become a professional data analyst, you need knowledge of machine learning and big data technologies 
• Besides technical skills, you must have communication, problem-solving, and attention to details skills

Average annual salary: ₹6.4L in India, $1,08,455 in the US

2. Data Scientist 

A data scientist is an expert in collecting and analyzing large data, generating valuable insights, and converting them into actions. They use advanced analytics techniques like machine learning and predictive modelling, statistical methods, and data visualisation techniques along with the use of scientific principles. 

Years of experience required: 1-3 years

Job responsibilities: 

• You will be responsible for mining and analysing data from company databases for improving and optimising product development, marketing techniques, and business strategies
• Data scientists often need to collaborate with software engineers and product development teams to integrate different data science models into the production environment
• Certain data science jobs require professionals to design and execute data-driven experiments, interpret results, and make data-based recommendations
• They must monitor different data science model performances continuously and refine their algorithms whenever necessary 
• Such professionals must be able to communicate complex data science concepts,  analysis results, and recommendations in a clear and effective manner

Key skills required:

• You must have knowledge of machine learning and deep learning principles 
• These professionals need knowledge of programming languages like Python, R and SQL
• You require data-wrangling knowledge such that you can clean, preprocess, and manipulate large datasets 
• Acquire necessary experience with machine learning frameworks like TensorFlow, Keras torch
• Data Scientists require skills in creating image classification, object detection, multi-modal neural networks, semantic segmentation 
• Learn how to use different Big Data technologies and tools like Hadoop, and Spark for processing large data

Average annual salary: ₹14.6L in India, $45T in the US

3. Data Architect 

A data architect is a senior-level data science job role where a professional is responsible for designing, creating, and managing the data architecture of an organisation. They ensure the data is structured, stored, and accessed properly, meeting an organization’s goals and supporting its business processes. As the data architect you often need to get involved in key decisions like the use of new technology and data governance in the company and support a business’s current and future needs.

Years of experience required: 5+ years

Job responsibilities: 

• They are responsible for designing different data models that define how data is organized, stored, and retrieved 
• Data architects are required to collaborate with cross-functional teams, analyze data needs and implement effective data solutions
• You also need to develop and manage data pipelines, address data complexity, and volume management
• Architects must create and deploy effective data models and reporting solutions using Power BI
• Sometimes you also may do lead performance tuning, error handling, and anomaly detection in the data system to ensure high data quality 
• You will manage a team of data professionals, oversee project allocation, work process, and professional development of the team
• You may be required to act as a trusted advisor and provide direction to clients in different verticals on their choice of data and machine learning platforms, data strategies, expected results, and relevant tools and methodologies

Key skills required:

• You must have good experience in data architecture with a strong knowledge of SQL server platforms and application architectures
• It is good to have expertise in Azure data services, including Data Lake, Databricks, and Azure Data Pipelines 
• Experts must have expertise in data modeling techniques like entity relationship diagrams, and knowledge in designing relational and NoSQL databases
• You must understand the Big Data platforms like Hadoop, Apache Spark, and distributed data storage systems 
• Acquire experience with data warehousing concepts and tools like Amazon Redshift and Snowflake for managing large data sets 
• Such professionals must have proficiency with cloud services like AWS, Azure, or Google Cloud, especially their data storage and processing services
• Additionally, you must be an expert in Python, R, and SQL  languages
• You should learn data ingestion techniques using different file formats like CSV, TXT, and JSON

Average annual salary: ₹27.7L in India, $1,86,066 in the US

4. Data Engineer 

These individuals are responsible for developing, implementing, managing, and optimising data pipelines and data infrastructure. They ensure the data is properly collected, pre-processed, and stored to transfer to data analysts and data scientists for querying or analysis. 

Years of experience required: 4+ years

Job responsibilities: 

• They are responsible for designing and building data pipelines to extract, transform, and load data from multiple sources into data lakes
• They are required to manage and optimize data infrastructure components, including data lakes, and data warehouse 
• You must implement various data quality checks and ensure data integrity throughout the data workflow 
• They also be required to identify and address different problems in data pipelines and storage systems that affect performance 
• Quite often these professionals collaborate with data scientists and analysts to understand what data is needed and provide the required data infrastructure and tools

Key skills required:

• You need to learn a programming language which is mostly Python or R, and knowledge of SQL is needed
• These professionals are required to work with multiple data engineering tools including Apache Spark, and Hadoop, and use cloud computing platforms like AWS and Azure 
• It is essential to learn relational databases including MySQL and PostgreSQL, and NoSQL databases like MongoDB and Cassandra 
• You must be efficient in designing and building ETL pipelines (ETL stands for Extract, Transform, and Load)
• Some soft skills are also required like identifying and solving complex data-related challenges and communicating efficiently with data scientists and analysts 

Average annual salary:₹10.7L in India, $1,31,665 in the US

5. Data Manager 

A data manager is a senior-level professional who supervises the development and use of the data system of a company and ensures the data is properly organised, stored, and secured. They are also responsible for creating techniques for quality data collection and implementing secure and efficient processes for data handling and analysis.

Years of experience required: 7+ years

Job responsibilities: 

• They need to develop and implement data governance policies and procedures to ensure data quality, security, and compliance with regulations.
• Data managers establish and maintain data quality standards and metrics to identify and address data issues.
• They also protect sensitive data from unauthorized access, breaches, and loss, and manage the integration of data from various sources into a unified data repository.
• These professionals are required to design and implement data warehouses and data marts to support analytical and reporting needs.
• You need to maintain accurate and up-to-date metadata to document data attributes, sources, and usage.
• They are often required to lead and manage a team of data analysts, data engineers, and other data professionals.
• Data managers also take part in selecting and managing vendors who provide data management services or tools.
• They require effective communication with businesses about the data management initiatives and progress across the organization.

Key skills required:

• You must have  knowledge of relational databases like MySQL, or PostgreSQL, and NoSQL databases like MongoDB, and Cassandra
• Before becoming a data manager you must understand the data warehousing concepts, ETL (Extract, Transform, Load) processes, and data mart design.
• Data managers must have the ability to create and maintain data models, including entity-relationship diagrams and dimensional models.
• Learn about Data Querying and Analysis which means you need to know SQL and data analysis tools like Tableau, Power BI, or Excel.
• You must know how to use data quality assessment and improvement tools and be familiar with data security best practices, encryption techniques, and access controls
• You need to understand metadata concepts and tools for managing metadata.
• Experience with cloud-based data platforms like AWS, GCP, or Azure is essential for data managers.
• One of the must-have skills for data managers is having knowledge of programming languages like Python or R for data automation and analysis.
• Additionally, you must know the data governance frameworks and tools.

Average annual salary: ₹13.1L in India

Is Data Science a good career path in 2024?

Yes, data science is a good career path in 2024, and many professionals are willing to start their careers in this domain. This is mainly because companies generate huge amounts of data, which they need to analyse and look for opportunities, trends, and areas to improve. I have shared a few valuable insights on the data science industry for you to understand its demand in the market, job availability, pay scale, etc. 

1. Huge industry demand

According to the US Bureau of Labor Statistics, data scientist jobs are increasing by 35% from 2022 to 2032. With the increased volume of data in the market, data scientists are now needed in almost every business across several sectors.

They have the power to shape business decisions, solve real-world challenges, and assist in finding opportunities and risks ahead of time.

In the past 5 years, the search trend for “Data Science” has shown an upward rise which means this domain is still in demand.

2. Multiple job opportunities 

Almost every company today prefers using data to understand trends, opportunities, and areas to improve their business. Data-backed decisions provide better outcomes in any business. This has opened huge opportunities for data science experts.

We searched on LinkedIn for data science job roles and found 29,000+ vacancies in India consisting of junior to senior-level job roles. It means if you have data science knowledge and expertise you have a higher chance of getting an entry-level job.

3. Competitive salary 

Data Science career paths offer attractive salary packages to experts from their early-stage job roles and the salary gets exceptionally high when you gain adequate experience. Listed below are the top-paying data science career paths in India.

Career Path	Average salary in India
Data Scientist	₹14.6 LPA
Senior Data Scientist	₹27.2 LPA
Senior Data Analysts	₹11.3 LPA
BI Analysts	₹8.8 LPA
Data Engineer	₹10.7 LPA
Lead Data Scientist	₹30.8 LPA
Big Data Engineer	₹10.8 LPA
Data Architect	₹27.7 LPA
Chief Information Officer	₹74.9 LPA

Not only in India, but we have checked reports to find that data science is a high-paying job in multiple other countries.

4. Multiple career opportunities 

Data science career paths diversify into multiple streams which means more opportunities to grow. You can find maximum full-time data science job opportunities in multiple industries.

5. Easy to get started with Data Science 

Most of the Data Science courses require only a bachelor’s degree which means you can land a job at an early stage. Upon completing graduation you can enroll for data science courses and start applying for internships or entry-level jobs. 

Data Science career opportunities in India

We have listed a few top Data Science career opportunities in India along with their experience, role type, salary, and primary responsibilities.

Data Science Career	Experience Required	Role type	Average annual salary range in India	Primary responsibility
Data Scientist	1-3 years	Early-stage	₹3.9L – ₹28.0L	A primary responsibility of data scientists is to extract meaningful insights from complex data sets using statistical analysis, machine learning, and programming to inform business decisions and solve problems.
Manager Data Scientist	4-8 years	Senior	₹19.0L – ₹93.0L	A primary responsibility for a manager data scientist is to lead and oversee data science projects, ensuring teams effectively analyze data to generate actionable insights that drive business decision-making processes and strategies.
Data Modeller	3-5 years	Mid senior	₹4.6L – ₹37.0L	Design and develop data models to organize, structure, and integrate data from various sources, ensuring efficient data storage and retrieval for analysis and decision-making processes.
Big Data Engineer	2-3+	Mid senior	₹3.6L – ₹21.1L	Design, implement, and maintain scalable big data infrastructure and pipelines to process, store, and analyze large volumes of data efficiently, supporting data-driven decision-making.
Senior Business Analyst	4-5 years	Mid senior	₹5.0L – ₹25.0L	Analyze complex business processes, identify improvement opportunities, and develop strategic solutions to enhance organizational efficiency, profitability, and decision-making across multiple departments or projects.
Chief Operations Officer	10+ years	Senior	₹7.8L – ₹102.0L	Their primary responsibility is to oversee the day-to-day operations of an organization, ensuring efficiency, effectiveness, and alignment with strategic goals. They often manage teams, improve processes, and oversee financial performance.
Data Architect	5+ years	Senior	₹14.5L – ₹50.0L	The primary responsibility is to design and develop data architecture strategies. This involves ensuring data quality, consistency, and accessibility for business intelligence and analytics. You’ll often work with various data technologies and tools, and collaborate with data engineers and analysts.

What Do Data Scientists Do?

Data scientists are the professionals who extract, analyze and interpret large volumes of data from multiple sources using algorithms, artificial intelligence, data mining, machine learning, and multiple other tools and technologies. 

Once they analyze and interpret the data in an easy-to-understand format, they present that result to businesses and help them in data-driven decision-making. They are experts in solving real-time problems within a company and predicting the future by using past data.

Some of the common roles and responsibilities of a data scientist are:

• They are responsible for  gathering relevant data from various sources and making sure that these data are accurate and complete
• Data scientist focuses on data exploration and analysis by applying statistical techniques and data mining algorithms to uncover hidden patterns, trends, and anomalies.
• They create new features or transform existing ones to improve model performance.
• These professionals need to build and train machine learning models to predict possible outcomes or make classifications.
• They often need to deploy models into production environments and continuously monitor their performance.
• Finally, their tasks cover presenting findings or data insights and making recommendations to stakeholders in a clear and concise manner, using data visualizations and storytelling skills.

Skills and prerequisites to become a Data Scientist 

1. Python Programming 

Knowledge of a programming language is essential for data scientists which helps them sort, analyse, and manage large sets of data. Data scientists must learn the syntax and concepts of Python programming and what’s more important is learning the Python libraries like NumPy, Pandas, and matplotlib. 

These built-in libraries can help you perform multiple tasks from Data manipulation and data preprocessing to statistical analysis and data visualisation. 

Python also is useful in machine learning and deep learning domains. Its popular packages and frameworks like Tensorflow, Scikit-learn, and Keras can build and train machine learning algorithms.

2. R programming 

Besides Python, data scientists can also learn R programming. The Comprehensive R Archive Network (CRAN) comprises packages essential for data science, such as Tidyverse. R programming can be used in data manipulation and visualization and for statistical computing and machine learning domains.

Most of the data science courses cover Python programming, so if you want to learn R you can take up an individual course. 

3. Knowledge of SQL and NoSQL

Often people compare Python, R and SQL and learn either of them. However, I believe learning SQL alongside a programming language is necessary as it helps in editing and extracting data from relational databases. Most companies use relational databases to store their data so SQL is a necessary skill for data scientists. 

Besides these, I also recommend data scientists learn NoSQL databases as you may also need to deal with unstructured data. 

For example, you are extracting data from audio, video, satellite images, or web server logs. These are all unstructured data and using a traditional relational model will make it difficult to store and process them. 

This requires you to know the use of MongoDB, Cassandra and other NoSQL databases to handle large amounts of unstructured and complex data.

4. Use of statistical analysis and probability 

You might not require a mathematical degree to enroll for data science courses but certain mathematical skills are necessary. You mainly know these four mathematical areas: calculus, algebra, statistics, and probability. While Machine learning depends on mathematical algorithms, you must know linear algebra, calculus, and probability to make those algorithms accurate and efficient. 

Knowledge of statistics helps you choose and apply different data techniques, build data models, understand what data you are dealing with and identify hidden patterns and trends within that data. 

5. Understanding of data wrangling 

Data scientist requires the knowledge of data wrangling to clean and organize complex data sets to make them easier to understand and analyze. Data wrangling methods can transform raw data into easily understandable formats so that you can answer any question or generate valuable insight from that data for better decision-making. Some of the data-wrangling tools include Altair, Talend, Alteryx, and Tamr, which you can learn using. 

6. Gain knowledge of machine learning skills

Machine learning skills help data scientists create predictive models and algorithms using Python frameworks like Scikit-Learn, TensorFlow, and PyTorch. You can uncover data patterns using these skills, predict outcomes, and improve data-driven strategies. If you are enrolling for a data science course, you will learn the basics of machine learning including its types (supervised, unsupervised, reinforcement learning), setting up the development environment, ML workflow, and knowledge of common data preprocessing techniques.

7. Learn deep learning skills 

It is a subset of machine learning and is essential for data scientists to work on neural networks. It allows you to tackle complex problems like image and speech recognition, virtual assistants, and robots. 

8. Acquire data visualisation skills

Apart from growing skills in analyzing, organizing, and categorizing data, you must also learn data visualization which focuses on creating charts and graphs to present your findings to others. Data scientists can summarize thousands of data on a table in a format that is easy to understand and accessible. You can learn using different tools for that purpose like Tableau, Microsoft Excel, and PowerBI. 

9. Develop Natural Language Processing skills

Knowledge of NLP techniques can be used by data scientists to improve the accuracy of predictive models by understanding text data better, distracting relevant features, and creating accurate models. You can learn concepts like tokenization, stemming, lemmatization, sentiment analysis, language modelling, parsing, etc. 

10. Having Cloud computing skills is a plus

Most data scientists need to work with data stored on cloud platforms and use proper cloud computing skills to analyze and visualize those data. Focus on specific cloud services like AWS, Microsoft Azure, and Google Cloud. They provide ready-to-go solutions according to the client’s circumstances and of course, necessary data tools that help you perform the entire workflow without leaving the cloud.

11. Acquire the necessary soft skills

Besides having relevant technical skills, data scientists need strong communication, problem-solving, collaboration, and analytical skills. This ensures they can analyse data better, work in a team whenever needed, and communicate insights to businesses efficiently. 

How Much Do Data Scientists Make?

Data Scientists make around ₹3.9L – ₹28.0L in India, and their average annual salary is ₹14.6L which is really mouthwatering for tech professionals.

Listed below the average annual salary of Data Scientists in the top five cities of India:

Top 5 cities in India	Average annual salary of Data Scientists
Bangalore	₹15.2 Lakhs
Hyderabad	₹14.7 lakhs
Pune	₹13.2 lakhs
Noida	₹13.5 lakhs
Mumbai	₹13.5 lakhs

How Difficult is Data Science?

Data Science isn’t very difficult if you know what skills you need and enroll on a good data science course. This comprises multiple disciplines like computer programming, mathematics and statistics, Artificial Intelligence, Machine Learning, Natural Language Processing and a lot more. You can find multiple resources available in the data science communities that can help you deal with all these challenges.

Most of the problems lie in dealing with larger volumes of datasets, complex algorithms, and the use of constantly updating tools and technologies.

To become an expert data scientist you need to get proper hands-on practice regularly on real-world projects. This helps you understand future challenges and identify solutions for them.

Data Science course certification to start your career

If you want to step into the world of data science and get a high-paying job, you need to enrol on a data science course from a reputed institution. But choosing the right one can be difficult as the options are huge.

I recommend you enrol for Codegnan’s Data Science course which offers a practical approach to learning. You will not only receive an industry-accredited certificate on course completion but also experience hands-on training on real-world projects under the supervision of industry experts. We also prepare you for advanced data science courses by clearing your foundational knowledge and advising you on how to get a job consistently during class.

We offer both online and offline classes (Hyderabad and Vijayawada) for our global students such that geographical restrictions don’t stop you from becoming a data science expert. 

Our course starts with giving you an introduction to Python and slowly proceeds with database management, data visualization, data analysis, and other essential topics required for data scientists. 

By the end of the course, you will get to work on five real-world projects so that your basics get cleared and you gain proper practical knowledge. Our aim is to prepare you for the market so that the skill gap doesn’t stop you from getting your dream job. 

Codegnan has been rated 4.8 out of 5 on Google by 2,391 people, which means you can trust us for your dream data science job. We assign top experts for your learning who have the proper industry experience to help you learn about the trends and prepare you according to current market requirements. 

FAQs

Can a fresher get a job in data science?

Yes, a fresher can get a job in data science if they have the necessary skills and hands-on practice on multiple projects during their training period. You can also do internships to get adequate work experience and apply for jobs.

What is the qualification for data science?

The qualification for data science is a bachelor’s degree in computer science, mathematics, statistics, or engineering. However, multiple beginner-friendly data science courses allow anyone to enrol for learning the domain. If you have knowledge in at least a programming language (like Python and R), database management, statistics, and probability, learning data science can become a little easier.

How can I become a data scientist after 12th?

You can become a data scientist after 12th by completing your graduation in any field. This includes arts, commerce, core science subjects, or an engineering course.

The ideal course not only covers the essential knowledge but also provides students with adequate hands-on training. This training should involve real-time projects under the supervision of industry experts.

Is data science still in demand in 2024?

Yes, data science is still in demand in 2024. There are more than 29,000 data science jobs in India only on LinkedIn. You can find several other opportunities on other traditional job boards and through other sources.

The use of data has increased in the past few years and is going to stay longer, creating better job opportunities for data science experts.