Core subjects in a data science postgraduate program

A postgraduate degree in Data Science is a specialized course that offers students a thorough understanding of the theories, methodologies, and tools utilized in data science. The purpose of such a program is to provide students with the knowledge and abilities required to become skilled data scientists. Then, the students apply their knowledge and skills to solve real-world challenges.

Data science postgraduate subjects include mathematics and statistics, data mining, programming, machine learning, data visualization, big data, and business intelligence. Students will learn how to acquire, clean, and analyze massive datasets, as well as how to develop prediction models and explain their findings to non-technical stakeholders.

The present data science scenario has grown increasingly crucial as businesses across all industries acquire and generate massive volumes of data. This data/information gives significant insights into customers, markets, and operations, allowing businesses to achieve a competitive advantage.

Data science postgraduate program structure

Online Manipal offers two data science postgraduate program structures. We provide an M. Sc. in Data Science and a PG certificate program.

The following are the duration, format, and outcome of the M. Sc. and PG certificate programs in Data Science:

• M. Sc. in Data Science

Duration	Format	Outcome
The M. Sc. program will last 24 months and consist of four semesters. A candidate must devote approximately 15-20 hours per week.	The classes will be held online, and once the course is completed, the candidate will receive a completion certificate.	You will learn how to work effectively in groups, generate strategic and tactical recommendations, and use a perfect blend of machine learning, big data analytics, and statistics.

• PGCP

Duration	Format	Outcome
The PGCP in Data Science & Machine Learning will last 9 months and consist of four courses.	The PGCP in Data Science and Machine Learning is a self-paced online instructor-led program that may be attended from anywhere. This Data Science postgraduate coursework will include 7 micro-projects, mentorship sessions, and two capstone projects.	A candidate should be able to understand cutting-edge Data Science & Machine Learning techniques and tools such as database management and SQL, Python programming, data science statistical methodology, and so on.

Data science postgraduate curriculum

Data science is a new field growing in prominence by the day. The need for enterprises to translate data into insights is driving an increase in demand for Data Scientists. That is why a comprehensive curriculum is required for students.

The candidate will benefit from the curriculum in the following ways:

• Opportunities for networking

Our data science postgraduate curriculum lets you communicate with people from other cities, states, or nations. Meeting new people from different places with different growth and professional mindsets extends your thinking capacity and offers insight into how your peers in the same field think. The extensive networking opportunity will benefit your academic growth and lead to career-changing prospects.

• Exposure to recent resources

An advanced Data Science Certificate Program exposes you to recent data science resources, tools, and technology. The course makes such resources available online, allowing the learner to access them anytime and from any location. As a result, online courses are preferred by students.

Core subjects in the Data Science Postgraduate Program

1. Machine Learning

Machine learning is an important and time-consuming language to master while studying data science because it covers many topics. It is a discipline of data science that uses vectors and arrays to make interacting with datasets easier and is also useful for researching neural networks.

2. Data Mining

Data mining is locating hidden insights (information) in a database and extracting patterns from it. Data mining is an automated technique exploring vast databases for patterns humans may miss.

3. Statistics

Statistics is a set of mathematical ideas and tools that enable us to answer critical data questions. It teaches fundamental statistics topics, such as probability, distribution, hypotheses, and the CLT (Central Limit Theorem) theorem. These topics are necessary for understanding statistics for data science.

4. Data Visualization

Data science is a diverse subject with numerous applications. Although many believe that machine learning and its algorithms are the most important aspects of data science, this is not the reality. Data visualization is just as vital as any other aspect of data science, especially regarding practical application. It is the representation of data or information in graphical or pictorial form to communicate insights and findings effectively.

5. Deep Learning

Deep learning is an important component of data science, covering statistics and predictive modeling. It is especially useful for data scientists who must collect, analyze, and interpret vast amounts of data, as deep learning accelerates and simplifies these processes.

6. Natural Language Processing

Natural language processing is significant because it helps resolve linguistic ambiguity and gives helpful quantitative structure to data for various downstream applications such as speech recognition and text analytics.

7. Business Intelligence

Data and business analytics are incorporated into business intelligence. BI assists users in concluding data analysis. It is a multi-disciplinary approach to analyzing, understanding, and interpreting vast and complex data sets, utilizing various tools, technologies, and methodologies. These tools may include data warehousing, data mining, reporting, and dashboards to gain insights.

8. Big Data

Big data analytics helps businesses leverage their data and uncover new opportunities. As a result, companies make better judgments, processes are more efficient, profitability is higher, and customers are happier.

Data science postgraduate program requirements

• M.Sc. in Data Science

Indian applicants must have a graduation degree with a prerequisite in Mathematics/Statistics/Engineering/Computer Science/Technology or any other subject. They must also have a minimum of two years of learning Mathematics or Statistics.

International students applying for M. Sc. in Data Science must have a graduate degree in addition to the criteria specified above for Indian candidates.

• PGCP

An applicant applying for PGCP must have a minimum of 50% marks in Bachelor’s degree from a recognized institute and have math, statistics, or programming as a major subject.

Conclusion

In conclusion, the core subjects in a data science postgraduate program play a crucial role in providing students with a comprehensive understanding of the field. By studying these subjects, students have the skills to collect, process, analyze, and interpret large data sets to extract meaningful insights. Moreover, a strong grasp of the core subjects is essential for success in various data science specializations such as predictive modeling, visualization, and big data.

Postgraduates in data science will be well-prepared to explore several positions in the area, encompassing roles in industry, government, and academia, where they can utilize their skills to promote innovation.

Check out MAHE’s online M.Sc. in Data Science program, which will teach you relevant data science tools, hands-on applications, and techniques through industry case studies.

Key takeaways:

• Data is a valuable asset to any firm. It assists businesses in understanding and improving their processes, saving time and money.
• Data science is important to any organization in any industry. From statistics and insights throughout workflows to hiring new applicants, data assists senior employees in making better-informed decisions.
• If you want to become a Data Science specialist, we offer the right courses.