In my experience, Data science is all about efficiently managing vast amounts of data using scientific methods. It combines various disciplines like mathematics, statistics, and computer science to analyze and derive insights from these large datasets. 

In this article, I’ll discuss the eligibility for Data Science Course eligibility in India. I’ll delve into the required qualifications and highlight the key topics covered in these courses. By the end, you’ll clearly understand what it takes to pursue a career in data science in India. 

Read more to know each in detail.

What is Data Science Course?

A Data Science program is an educational curriculum or set of courses designed to teach individuals the skills and knowledge required to analyze, interpret, and extract valuable insights from data. Data Science combines various fields such as statistics, computer science, mathematics, and domain expertise to handle large and complex datasets.

Data Science Course Eligibility

Who can do data science course? Lately, Data Science has been in great demand in the industry. To cope with the demand, students have started looking forward to studying the DS subjects. Industries started upgrading the skills of their staff to remain competitive. Several institutes and course providers picked the industry needs and designed suitable courses in Data Science. On that note, let’s take a look at some of the data science requirements in India.

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Data Science Demand

According to the report by the US Bureau of Labor Statistics, the rise of Data Science needs will create approximately 11.5 million job openings by 2026. The World Economic Forum predicts that by 2022, the profession of data scientist would be the most emerging in the world.

As the growth suggests, after the United States, India is looked at as the second most prominent hub for Data Science developments. As per the current industry job trends, Data Science is a highly employable and appealing profession. The demand, therefore, introduced a swift surge in the Data Science course providers.

Who is Eligible?

Anyone, whether a newcomer or a professional, willing to learn Data Science can opt for it. Engineers, Marketing Professionals, Software, and IT professionals can take up part-time or external programs in Data Science. For regular courses in Data Science, basic high school level subjects are the minimum requirement.

Data Science, loosely, is an amalgamation of concepts from Mathematics, Computer Science, and Statistics. Students should have a degree in one of the fields in science, technology, engineering, and mathematics (STEM background).

So a data scientist eligibility in India is anyone who is from a STEM background, as it is one of the minimum requirements for data scientist that any newcomer should possess. 

Having studied computer programming in high school is an additional benefit. Students study the fundamentals, as well as advanced concepts in Data Science. Based on the subject knowledge of statistics, machine learning, and programming, students become experts in implementing Data Science methodologies in the practical world. 

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Students from other streams, like business studies, are also eligible for relevant courses in Data Science. Similarly, business professionals having a basic degree in Business Administration, such as BBA or MBA, are also eligible for higher studies in the Data Science domain.

These professionals work in the capacity of Executives in the IT industry. They are mostly responsible for making CRM reports, MIS (Management Information System), and business-related DQA (Data Quality Assessment).

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The basic data scientist eligibility in India for a course in data science after 12th standard includes basic knowledge of maths, computer science, and statistics. Aspiring students also need to have at least a total aggregate of 50% on their high school score sheet. 

The data science course eligibility for students looking for opportunities for Diploma in Data Science includes a BE/BTech/MCA/MSc degree, with their core subjects being statistics, computer, or programming. Furthermore, the said students also need to have a total aggregate of 50% and above to qualify for a Diploma in Data Science. 

The Data Science course eligibility criteria for someone interested in MSSc/MTech/MCA Data Science is a BCA/BE/BTech degree or any other equivalent degree from a recognized university. Furthermore, the aspiring candidates should also have a minimum aggregate of 50% marks on their university score sheet, alongside a detailed knowledge of mathematics and statistics. 

Data science requirements for someone opting for a PhD in Data Science include a minimum of 55% marks in the postgraduation programme. Students having a GPA higher than the average are likely to be given more preference than others. 

Data Science Curriculum

The majority of courses designed are PG and certificate level courses for graduates. Recently, several technical institutes and engineering colleges in India launched degree level programs in Data Science and Analytics. 

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DS Subjects and Skills

In general, for admission to a DS course, the following are the data scientist qualifications in India–

• Degree – A graduation from the STEM stream.
• No coding experience is required.
• Mathematics – This subject is the heart of ML/DS and Data Analysis, where the model is created by processing mathematical algorithms’ data. In general, mathematics broadly covers topics in arithmetic, algebra, calculus, differentiation probability, statistics, geometry, and allied.
• Statistics – Statistical concepts will help you understand data, analyse, and derive a conclusion from the data.
• Data Visualisation – Access, retrieve data, and perform visualisation and presentation with R and Tableau.
• Exploratory Data Analysis – Explore Excel and databases to derive useful insights from the pool of data and learn from the data attributes and properties.
• Hypothesis Testing – Formulate and test hypotheses that are applied in case studies to solve real business problems.
• Programming Languages – Though coding is not the criteria for admission to DS courses, knowledge about programming languages, such as Java, Python, Scala, or equivalent, is highly recommended.
• Database – A good understanding of the databases is highly desirable.

If one is able to master all of the skills mentioned above then they have nothing else to worry about regarding data scientist eligibility in India.

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The Science Behind Data

The Data Science field highlights the processes involving methods, algorithms, and systems to derive knowledge and intelligence from a pool of structured and unstructured data. Data Science caters to several data-driven initiatives, such as Data Mining, Big Data, Machine Learning, and Artificial Intelligence.

In greater detail, Data Science could be looked at as a concept that unifies statistics, analysis of data, and methodologies to analyse and make sense of the real phenomena with the help of data.

Data Science Syllabus

Educationalists designed a Data Science course syllabus to help make the students industry-ready to implement DS knowledge in the industry. The curriculum is also tuned to match the needs of the industry.

The syllabus focuses on specific areas, such as open-source tools, libraries, databases, SQL, Python, R, data visualisation, data analysis, and machine learning. The centric concept in the course follows the methodologies in data handling. It uses models based on systematically designed algorithms. 

Major Tools and Programming Languages that are Data Science Requirements

• • Python or R: Python is an object-oriented, interpreted language. It has built-in data structures, dynamic typing (runtime type checks), and binding. Python syntax is easy to read and learn. Python and its libraries are free. Python improves programmers’ coding efficiency. It includes libraries like NumPy, Pandas, Scikit, Keras, Tensorflow, Matplotlib, etc. Jupyter Notebook, a live-code-sharing web app, makes data science explanations smooth.
    R is a statistical/computing/graphics programming language. R includes linear and nonlinear modelling, statistical testing, clustering, etc. R’s strength is how easily it can plot mathematical notations and formulae. Knowledge in Python or R is definitely a data scientist requirements that companies look for. 
  • Mathematics and Statistics: Machine Learning algorithms rely heavily on mathematics and statistics. Knowing the methods that underlie different Machine Learning algorithms can help you choose the right one for the job.
  • Algorithms: Machine learning and Deep Learning algorithms are the base of an Artificial Intelligence model. It is one of the key data scientist requirements. SVM, Random Forest, and K-means clustering are to name a few.
  • Data Visualizations: Users of data science and data analytics software may benefit from data visualization by gaining insight into the large volumes of data they have access to. Data visualization is one of the many data science requirements. The ability to discover new patterns and flaws in the data is beneficial to them. By understanding these patterns, users are better able to focus their attention on areas that signal potential problems or advancements.
    Tableau is one of the many data visualization software out there. Tableau is quite popular since it enables users to do data analysis in a relatively short amount of time.
    Additionally, dashboards and spreadsheets may be created from the visualizations. Tableau enables users to construct dashboards that provide insights that can be put into action and move a company’s operations forward.
    When properly configured with the appropriate underlying operating system and hardware, Tableau products will always run in virtualized environments. Data scientists investigate the data using Tableau, which provides them with endless visual analytics.
  • Spark, SQL, NoSQL: Spark is a platform for processing massive datasets using analytics, machine learning, graph processing, and more. Spark’s compatibility with popular database technologies like NoSQL and SQL makes it possible for data scientists to derive useful insights from massive datasets.
  • Hadoop: Apache Hadoop is a system that is developed using open source software that is used to effectively store and analyze huge datasets with sizes ranging from gigabytes to petabytes. Hadoop enables the clustering of several computers so that big datasets may be analyzed in parallel and much more rapidly than was previously possible. This is done in place of having a single gigantic system to store and analyze the data.

  Most Jobs Expect a DS Professional to Have the Following Skills, these are the basic requirements for data scientist –

• A good grade and understanding of Statistics, Mathematics, Computer fundamentals, and Machine Learning.
• Expertise in one or more of the programming languages, preferably R or Python.
• Thorough understanding of databases.
• Exposure to Big Data tools, like Hadoop, Spark, and MapReduce.
• Experience in data wrangling, data cleaning, mining, visualisation, and reporting tools.

Top Data Science Skills to Learn

Till now we have learned some of the basic data scientist qualifications in India. But what are the topics that are covered in Data Science and Data Analytics courses? In the following list lies the answer to this question. Almost every university has four major components in their data science courses offered to the students. These include 

• Big Data
• Machine Learning
• Artificial Intelligence
• Modelling

Big Data

This particular topic mainly focuses on engaging students in Big Data methods and strategies. Big Data in its first stage is made up of several unstructured data that are gathered in the form of videos, pictures, messages, posts, etc. This unstructured data is then transformed into organised data. 

Machine Learning

Students get to learn various mathematical models and algorithms, which are included under this segment of the Data science curriculum. The main objective is to help students adapt better to everyday developments, and face the challenges of an organization. 

Artificial Intelligence

Following the accumulation and gathering of data, enterprises need to be able to interpret and display the said data accordingly. That is where machine learning comes into play. It not only helps you to have a better understanding of the market side of the process, but you are also able to see the patterns and then make business progress accordingly. 

What Are the Important Areas of Data Science?

Data science encompasses a wide range of areas and skills, all aimed at extracting insights and knowledge from data. Some important areas within data science course include:

1. Statistics and Probability

A solid foundation in statistics and probability theory is crucial for understanding data distributions, making inferences, and building models.

2. Data Cleaning and Preprocessing

Before analysis, data often requires cleaning and preprocessing to handle missing values, outliers, and inconsistencies, ensuring accurate and reliable results.

3. Data Exploration and Visualization

Exploring data through visualizations helps in identifying patterns, trends, and relationships, aiding in the formulation of hypotheses and understanding the data’s underlying structure.

4. Machine Learning

This is a core area where algorithms are developed to enable computers to learn patterns from data and make predictions or decisions. Subfields include supervised learning, unsupervised learning, and reinforcement learning.

5. Feature Engineering

Selecting, transforming, and creating meaningful features from raw data to improve the performance of machine learning models is a critical step.

6. Model Selection and Evaluation

Choosing the right model for a given problem and evaluating its performance using metrics like accuracy, precision, recall, F1-score, etc., are essential tasks.

7. Deep Learning

A subset of machine learning that deals with neural networks and complex hierarchical representations, often used for tasks like image recognition, natural language processing, and more.

8. Natural Language Processing (NLP)

It helps machines to understand, explain, and create human language, with applications ranging from sentiment analysis to language translation.

9. Computer Vision

Involves the development of algorithms that allow computers to interpret and understand visual information from the world, often used in tasks like image and video analysis.

10. Time Series Analysis

This area deals with data collected over time, often used in forecasting and trend analysis. It involves techniques like autoregressive integrated moving average (ARIMA) and more.

11. Big Data Technologies

With the explosion of data, tools like Hadoop, Spark, and distributed computing frameworks are important for handling and processing large datasets efficiently.

12. Data Ethics and Privacy

As data science involves handling sensitive information, understanding ethical considerations and ensuring data privacy are essential aspects.

13. Domain Expertise

Having domain-specific knowledge is crucial for framing problems, interpreting results, and making informed decisions based on data insights.

14. A/B Testing

It is used to collate different versions of a website page or app against one another to find out which will deliver best results in terms of user engagement or other metrics.

15. Data Storytelling and Communication

The ability to convey complex insights in a clear and understandable manner to non-technical stakeholders is important for driving decision-making based on data.

16. Data Governance and Management

Involves establishing processes, policies, and standards for managing data throughout its lifecycle to ensure its quality, security, and compliance.

17. Feature Selection and Dimensionality Reduction

Techniques to identify the most relevant features and reduce the number of features in high-dimensional data, leading to more efficient models.

These areas often overlap and complement each other. Depending on the specific problem you’re working on and your role in the data science process, you may need to delve more deeply into certain areas than others.

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Conclusion

I should say this is the right time for aspirant students to decide on taking the right course in the Data Science stream. Assess your capabilities and decide on the courses that suit you best to achieve data scientist qualifications in India. And I would strongly recommend you check various courses offered by upGrad in Data Science that make eligible aspirants industry-ready professionals in Data Science. The courses range from Executive PG program in Data Science , PG Certification, to Masters. These courses will help you to make an informed decision based on career objectives and aspirations. This ensures that you will be well-prepared to excel in Data Science.