Quick Links

Big Data

Why study Big Data courses?

In today’s world, Big Data is everywhere and is being used to drive business decisions and operations in almost every industry. Organizations are increasingly relying on big data technologies to analyse vast amounts of customer data and make data-driven decisions. As a result, there is a growing demand for professionals who have the skills and knowledge to handle, manage and analyse large volumes of data. Taking a Big Data course is a great way to gain the knowledge and experience necessary to take advantage of the vast opportunities presented by Big Data. Big Data courses provide students with the technical skills and knowledge necessary to analyse complex data sets and make data-driven decisions. Students learn how to use analytics and visualization tools to interpret and present data in meaningful ways. Additionally, they gain an understanding of how to use machine learning algorithms and other tools to draw insights from data. Big Data courses also cover various programming languages, such as Python and R, which are commonly used to process and analyse data. Big Data courses also provide students with the opportunity to develop technical skills in areas such as database management, data mining, and data visualization. These skills are essential for working with large datasets and uncovering insights that are not immediately obvious. Additionally, students learn about data security and privacy, which are increasingly important in the age of big data. Finally, Big Data courses equip students with the skills to become data-savvy, which is a huge asset in the job market. With a Big Data course, students gain the ability to make informed decisions based on data and to present data effectively. This can make them highly sought-after in the job market, as organizations are looking for employees who are not only technically proficient but also have the ability to think analytically.

Best Countries to study Big Data

Big Data in United States

There are many reasons why the USA is considered the best place to pursue big data courses. Firstly, the USA has some of the most prestigious universities and colleges in the world, offering courses in data science and analytics. These universities provide a comprehensive education in the field, with a focus on practical applications. Furthermore, the USA has a large and vibrant tech industry, making it a great place to gain experience in big data. Additionally, the USA also has a large pool of highly-skilled professionals and innovative companies, such as Google, IBM, and Microsoft, which are actively using big data and analytics in their operations. Finally, the USA has a well-developed infrastructure for data processing and storage, making it a great place for data scientists and analysts to work.

The duration and fees of big data courses in the USA vary depending on the institution and program. For example, Johns Hopkins University and Northwestern University offer a Master’s in Data Science program that takes 2 years to complete and costs $112,400. Columbia University offers a Master’s in Data Science program that takes 18 months to complete and costs INR 46,14,131. Some Master’s in Big Data programs can be completed in 18 months with 30 credits, while others take 2 years with 32 credits. The cost of these programs varies as well. It is best to check with individual institutions for specific information on duration and fees.

  • Scholarships in USA

There are several scholarships available for Indian students in the USA who are interested in Big Data programs. ACM SIGHPC Computational and Data Science Fellowship. Acxiom Diversity Scholarship. Fulbright Foreign Students Scholarship Program USA. Robert S. Kaplan Life Sciences Fellowship. Tata Consultancy Services (TCS) Data Science Scholarship. Renewable Big Data Scholarships. Indian students can apply for these scholarships based on academic merit and other criteria. It is recommended to check the eligibility criteria and application process for each scholarship before applying.

Big Data In United Kingdom (UK)

The UK is considered one of the best places to pursue big data courses for a number of reasons. Firstly, the UK is home to some of the world’s top universities, such as Oxford, Cambridge, and Imperial College London, which offer a wide range of courses in the field of big data. Additionally, the UK is a hub for some of the biggest names in the field, such as Google, IBM, Microsoft, and Oracle, providing excellent opportunities for networking and internships. Finally, the UK has been at the forefront of research into big data and data science, making it the perfect place to gain experience in this rapidly growing field.

The duration and fees of big data courses in the UK vary depending on the institution and program. For example, the University of Stirling offers a one-year full-time MSc Big Data course. The cost of big data analytics courses in the UK ranges from £560 to £2695, and the average duration of the course is 2-150 hours, which can be taken over a period of 5 days or 10 weeks. The University of East London offers a Big Data Technologies program, but the duration and fees are not specified. Similarly, the duration and fees of big data courses at top UK colleges such as the University of Oxford, Imperial College London, and the University of Edinburgh are not specified. Online big data courses are also available, but the duration and fees vary depending on the course provider.

  • Scholarships in UK

There are several scholarships available for Indian students to study in the UK, but like Commonwealth Scholarship and Fellowship Plan, Women in Leadership Scholarship, IKOS Scholarship, Academic Excellence Senior Status Law Scholarship, Chevening Scholarships, etc. are various scholarships offered by universities in the UK.

Big Data in Canada

Canada is a great place to pursue big data courses due to its wide range of educational institutions, research and development opportunities, and access to world-class technology. Canada is home to some of the most prestigious universities and research institutes in the world, such as The University of Toronto, McGill University, and the University of British Columbia. These institutions offer courses and research facilities focused on big data, including data science, machine learning, artificial intelligence, and more. Additionally, Canada is home to a number of world-leading tech companies, like Google and Amazon, which offer access to cutting-edge tools and technologies for use in big data projects. Finally, Canada has a supportive government that provides funding and resources to help foster big data innovation.

The duration and fees of big data courses in Canada vary depending on the program and institution. For example, the one-year Diploma in Data Analytics Co-op offered by Toronto School of Management lasts for 52 weeks, with 24 weeks spent learning from tutors and 12 weeks on co-op education experience. The Master of Data Science and Analytics degree at the University of Calgary can be completed in 12 months through the direct entry pathway or 3-6 years through the part-time laddered certificate and diploma pathway. The fees for big data courses in Canada range from CAD 1,000 to CAD 4,600, depending on the program and institution. Other institutions, such as Simon Fraser University and Georgian College, also offer big data courses with varying durations and fees.

  • Scholarships in Canada

There are several scholarships available for Indian students in Canada who wish to pursue Big Data programs. Some of the scholarships available are Canada Graduate Scholarship-Master’s Program (CGS-M), Ontario Graduate Scholarship (OGS), Vanier Canada Graduate Scholarship, Fulbright Foreign Student Program, etc. Two scholarships valued at up to $10,000 each for incoming or current graduate students in Data Science, Numerous university-specific, privately funded, and government scholarships. Master of Data Science Domestic Scholarship, which is a $5,000 entrance scholarship offered annually to a domestic student in the Master of Data Science program at UBC.

Big Data in Australia

Australia has a well-developed IT infrastructure and is considered one of the most advanced countries in the world when it comes to technology. As a result, Australia is well-equipped to teach professionals the skills they need to work with big data. Many of the top universities in Australia offer specialized courses in big data, and the country’s businesses have already adopted big data solutions to increase their productivity. Additionally, the Australian government is actively investing in big data projects to improve the efficiency of public services. Finally, Australia has access to a large pool of talented experts in the field of big data, making it an ideal location for those seeking to pursue a career in this area.

The duration and fees of big data courses in Australia vary depending on the institution and program. For example, the Bachelor of Information Technology – Big Data and Analytics program at Federation University Australia has a duration of 3 years and fees that can be found by viewing the details of the program. Another option is the Data Science & AI Part-time or Full-time Program offered by the Institute of Data, which has a current fee of $15,000 and interest-free financing options available. The course entry requirements include completion of a bachelor’s degree in any field of study and completion of assigned online pre-work provided upon enrolment. To find more information about the fees and duration of big data courses in Australia, you can contact Edusky.

  • Scholarships in Australia

There are several scholarships available for Indian students in Australia, including those pursuing Big Data programs. The University of Sydney offers the Sydney Scholars India Scholarship Program, which provides $500,000 worth of scholarships for commencing Indian students. The Australian Embassy offers scholarships for Indian students pursuing undergraduate, Master’s, PhD, and MBA programs. Monash University offers the Monash University International Merit Scholarship for Master of Data Science students, which provides $10,000 per annum.

Big Data in Germany

Germany is considered one of the best places to pursue big data courses due to a number of factors. Firstly, Germany is home to a number of world-class universities, such as the Technical University of Munich, the University of Bonn and the University of Freiburg, which offer a variety of high-quality big data courses. Secondly, Germany has a strong economy and is home to many major companies that are actively exploring big data technologies, providing students with ample opportunities for internships and job placements. Thirdly, Germany has a vibrant academic and research community, which helps to drive the development of big data technology and ensure that students are exposed to the latest developments. Finally, Germany offers a great quality of life, with a low cost of living and a high quality of life.

The duration and fees of big data courses in Germany vary depending on the university and program. For example, the Master of Science in Data Science program offered by DAAD has no tuition fees. On the other hand, the Data Science program offered by IU Germany has a course fee that can be paid quarterly, and the duration of the course is 2 years. Saarland University offers a Master of Business Administration program in European Management (Part-Time) with a duration of 1-4 years and tuition fees ranging from €1813 – €7250 per semester. The fees of Big Data courses in Germany range from EUR 1,000 – 3,500.

  • Scholarships in Germany

Indian students looking for scholarships to study Big Data programs in Germany can apply for various scholarships. The German Academic Exchange Service (DAAD) offers scholarships to Indian students in Germany. Other scholarships available for Indian students in Germany include the Amiran scholarship, Heinrich Boll Scholarships, Bayer Foundations, and Konrad-Adenauer. Additionally, the Friedrich-Ebert-Stiftung scholarship is a great option for foreign students pursuing their studies or doctoral degree in Germany.

Big Data in France

France is considered one of the best places to pursue big data courses due to a number of factors. The French government has established a strong legal and regulatory framework that ensures the safe, secure and responsible use of data and analytics. This in turn has led to a very open, collaborative and innovative environment for big data initiatives. Additionally, France is home to a large number of big data specialists and has a number of big data-related initiatives taking place across the country. Furthermore, there are several universities and other educational institutions that offer a range of big data courses and certifications, making it easier for people to get the skills they need to succeed in the field.

The duration and fees of big data courses in France vary depending on the institution and program. For example, the Master in Big Data Analytics for Business at IÉSEG has a tuition fee of €18,900 and includes a personal laptop. The Master Year 2 Data Science at Institut Polytechnique de Paris covers big data infrastructures and other skills and is offered as part of a larger program. The M2 Big Data Management and Analytics program at Université Paris-Saclay covers machine learning and other topics and is part of the Erasmus Mundus BDMA Master’s program. Bachelor’s degrees in Data Science & Big Data are also available in France. It is recommended to check the websites of specific institutions or contact Edsusky for more

  • Scholarships in France

There are several scholarships available for Indian students in France for Big Data programs. The French government scholarship program in India is called Charpak and offers MSc programs in data science. The Eiffel scholarship is another popular option for Indian students pursuing Master’s and Ph.D. level programs in France. Other scholarships available for Indian students in France include the French Government Scholarship and scholarships offered by French universities. For more information on scholarships in France for Indian students, contact Edusky.

Big Data in Netherlands

The Netherlands is considered one of the best places to pursue big data courses due to its excellent academic institutions, skilled professionals, supportive government policies and initiatives, and a strong industry ecosystem for data analytics. Dutch universities offer world-class programs in data science and big data, and the country is home to some of the world’s leading data analytics companies. Dutch companies are also very open to hiring foreign professionals to fill their data analytics positions. Additionally, the Netherlands provides a supportive atmosphere for start-ups and other innovative companies working in the field of big data. The government has established a number of initiatives to support the development of the big data sector, and the country’s digital infrastructure is well-developed, providing a great environment for data-driven companies.

The duration and fees of big data courses in the Netherlands vary depending on the program. For example, the 2-year MBA Big Data & Business Analytics program at the University of Amsterdam has a tuition fee of €45,000, which can be paid in 6 equal instalments spread over the duration of the program. On the other hand, short courses in big data and data science in the Netherlands can range from €1,000 to €3,500 and can be completed in a short period of time. It is recommended to research specific programs to get more accurate information on their duration and fees.

  • Scholarships in Netherlands

There are several scholarships available for Indian students who want to pursue Big Data programs in the Netherlands. Holland Scholarship, which is meant for international students from outside the European Economic Area (EEA) who want to study in the Netherlands. Orange Knowledge Programme, which is a scholarship offered by the Netherlands government. NN Future Matters Scholarship, which offers grants of up to €5,000 to international students to study a master’s degree in risk management, economics, or data science. Indian students can also explore top universities, courses, fees, scholarships, and job prospects for MS in Data Science in the Netherlands

Job Opportunities

  1. Big Data Analyst
  2. Big Data Architect
  3. Big Data Engineer
  4. Big Data Scientist
  5. Big Data Developer
  6. Big Data Consultant
  7. Business Intelligence (BI) Analyst
  8. Data Warehouse Architect
  9. Data Warehouse Manage
  10. Data Mining Specialist
  11. Data Visualization Analyst
  12. Database Administrator
  13. Machine Learning Engineer
  14. Predictive Modeller
  15. Statistician
  16. Software Developer
  17. Systems Administrator
  18. Technical Account Manager

Specialisations

  • Data Warehousing

Data warehousing is a specialization that involves the collection, storage, and management of data from various sources to provide valuable business insights. A data warehouse is a system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence (AI), and machine learning. Data warehousing specialists are responsible for developing processes and procedures for data management across an organization or within the scope of a project, creating software applications or designing computer programs that accomplish tasks necessary for data storage and management. The Data Warehousing for Business Intelligence Specialization covers data architecture skills that are increasingly critical across a broad range of technology fields.

  • Data Mining

Data mining is a process of analyzing large amounts of data to discover patterns and relationships. It involves cleaning raw data, finding patterns, creating models, and testing those models. The Data Mining Specialization teaches data mining techniques for both structured and unstructured data, including pattern discovery, clustering, text retrieval, text mining and analytics, and data visualization. Data mining can be used by corporations for everything from learning about what customers are interested in or want to buy to fraud detection and spam filtering. The process of data mining usually consists of four main steps: setting objectives, data gathering and preparation, applying data mining algorithms, and evaluating results.

  • Data Visualization

Data Visualization Specialization is a program that teaches how to create visualizations to communicate data analysis results effectively. It involves using common graphics such as charts, plots, infographics, and animations to represent complex data relationships and insights in a way that is easy to understand. The program covers various types of data visualizations, including line plots, bar charts, pie charts, treemaps, scatter charts, and map charts. Students can learn data visualization through various tools such as Excel spreadsheets and Tableau, which offers powerful capabilities with a drag-and-drop interface and without coding. The Data Visualization Specialization is a part of the Data Analytics major and has a prerequisite structure within the core curriculum.

  • Machine Learning

The Machine Learning Specialization is an online program designed to teach the fundamentals of machine learning and its practical applications. It is offered by various institutions, including DeepLearning.AI and Stanford Online, and the University of Washington. The program is beginner-friendly and provides applied experience through practical case studies. The updated version of the Machine Learning Specialization by DeepLearning.AI is structured and designed to give anyone the opportunity to learn and understand machine learning, regardless of their background or technical knowledge. The program is suitable for students who want to find ML-related jobs, those who have never heard of machine learning, or those who are not working in IT or technical occupations.

  • Natural Language Processing

Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language. It is concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. NLP is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data. The Natural Language Processing Specialization is a course that teaches how to design NLP applications that perform question-answering and sentiment analysis, create tools to analyze large amounts of data, and more. The NLP track is intended for students who wish to gain expertise in NLP technologies and applications.

  • Predictive Analytics

Predictive analytics is a field of data analytics that involves using statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or trends. It is used in a variety of industries, including finance, healthcare, and marketing, to forecast future outcomes and make data-driven decisions. Popular predictive analytics models include classification, clustering, and time series models. There are various courses available online that teach practical predictive analytics models and methods. Predictive analytics is an ongoing journey, and companies can always find new sources of data and new applications for using that data to reduce costs, improve reliability, and add value.

  • Data Modelling

Data modeling is the process of creating a conceptual representation of data objects and their relationships to one another. It employs standardized schemas and formal techniques to provide a common, consistent, and predictable way of defining and managing data resources across an organization. Good data modeling and database design are essential to the development of functional, reliable, and secure application systems and databases that work well with data warehouses and analytical tools. The process of data modeling typically involves several steps, including requirements gathering, conceptual design, logical design, physical design, and implementation. Data modeling is a complex process that demands the ability to precisely understand and diagram business operations, data flows, and data management systems.

  • Data Stream Processing

Data stream processing is a real-time data technology that allows continuous streams of data to be processed as soon as they are generated. It involves ingesting a continuous data stream to quickly analyze, filter, transform or enhance the data in real-time. Stream processing services and architectures are growing in popularity because they allow enterprises to combine data feed from various sources. Stream processing is needed to develop adaptive and responsive applications, and to integrate data from multiple business applications or operational systems. Stream processing allows developers to think about ingesting data as a continuous data stream, and the data can be processed using stream processing techniques. Data streaming is a key capability for organizations that want to generate analytic results in real-time.

  • Big Data Infrastructure

Big data infrastructure specialization involves gaining an understanding of the insights that big data can provide through hands-on experience with the tools and systems used by big data scientists and engineers. Big data infrastructure refers to the hardware, software, and services required to manage and analyze large and complex data sets. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety of data types and sources. Companies that collect a large amount of data are provided with the opportunity to conduct deeper and richer analysis for the benefit of all stakeholders. Building a data architecture to drive innovation requires a new approach to defining, implementing, and integrating data stacks, leveraging both cloud and new concepts and components.

  • Big Data Security

Big data security is a specialized field that deals with the protection of large and complex data sets from unauthorized access, theft, or damage. It involves monitoring and protecting a company’s important business data to ensure safe and compliant ongoing operation. Data-centric security is a key aspect of big data security, which provides a risk-based analysis of regulated data and protects the data itself, allowing you to discover, classify, analyze, and protect your data at any given moment. Big data security is a constant concern because big data deployments are valuable targets to would-be intruders. There are various challenges and solutions in big data security, and it requires specialized skills and knowledge to manage and secure big data platforms.

  • Big Data Analytics

Big data analytics is the process of examining large and complex data sets to uncover hidden patterns, correlations, and trends that can help organizations make informed business decisions. It is an important tool for companies to leverage their data to identify opportunities for improvement and optimization, leading to more intelligent operations, higher profits, and satisfied customers. Pursuing a data analytics specialization can enhance one’s ability to make sound inferences and decisions using the science and art of learning from data.

  • Business Intelligence

Business intelligence (BI) is a field that combines data analytics, data visualization, and best practices to help organizations make data-driven decisions. BI analysts use their critical thinking skills and quantitative abilities to help businesses improve and thrive. BI reporting uses data visualizations such as interactive data dashboards, charts, graphs, and maps to make findings easier to understand and share. BI parses all the data generated by a business and presents easy-to-digest reports, performance measures, and trends that inform management decisions. BI solutions are enterprise-level software applications designed to optimize the information gleaned from data. The benefits of BI are diverse and include supporting functions such as hiring, compliance, production, and marketing.

  • Cloud Computing

Cloud computing is an emerging field in information technology that is changing the way computing is done. It is a multi-tenant environment where the cloud provider’s data center infrastructure is shared by all public cloud customers. Cloud computing has its roots in the 1960s, but it has only recently started to impact many enterprise computing systems in major ways. Cloud computing platforms automate the manual aspects of bringing a server online without a hypervisor on the server. The field of cloud computing is constantly evolving, and it is expected to change the face of computing in the years to come.

  • Hadoop

Hadoop is an open-source software framework designed to store and process large amounts of data in a distributed computing environment. It is based on the MapReduce programming model, which allows for the parallel processing of large datasets. Hadoop has several components, including MapReduce, the Hadoop Distributed File System (HDFS), and YARN, a Hadoop resource manager. Hive, a data warehouse software, provides an SQL-like interface to efficiently query and manipulate large data sets residing in various databases and file systems that integrate with Hadoop. Hadoop is commonly used in big data scenarios such as data warehousing, business intelligence, and machine learning.

  • Apache Spark

Apache Spark is an open-source, distributed processing system used for big data workloads. It is optimized for fast analytic queries against data of any size and utilizes in-memory caching to speed up processing. Spark provides development APIs in Java, Scala, Python, and R, and supports code reuse across multiple workloads. It is a general-purpose distributed data processing engine that can be used in a wide range of circumstances. Spark has libraries for SQL, machine learning, graph computation, and stream processing, which can be used together in an application. It is a popular tool for scalable machine learning on big data.

  • NoSQL Databases

NoSQL databases are a type of database management system that are designed to handle and store large volumes of unstructured and semi-structured data. Unlike traditional relational databases, NoSQL databases use flexible data models that can adapt to different types of data, including key-value, document, columnar, and graph formats. They are non-tabular and store data differently than relational tables. NoSQL databases are schema-free, which means they have a simple and flexible structure based on key-value pairs. They are designed to handle dynamic changes to the data model and can scale easily with large amounts of data and high user loads. NoSQL databases are better suited for cloud, mobile, social media, and big data requirements.

Top 15 Universities to pursue Big Data Courses
(Globally):

  • Massachusetts Institute of Technology- USA (#1 QS World Ranking 2023)
  • Carnegie Mellon University- USA (#2 QS World Ranking 2023)
  • University of California, Berkeley- USA (#3 QS World Ranking 2023)
  • University of Oxford- UK (#4 QS World Ranking 2023)
  • Harvard University- USA (#5 QS World Ranking 2023)
  • University of Toronto- Canada (#6 QS World Ranking 2023)
  • University of Washington- USA (#7 QS World Ranking 2023)
  • Princeton University- USA (#8 QS World Ranking 2023)
  • EPFL- Switzerland (#9 QS World Ranking 2023)
  • EPFL- Switzerland (#9 QS World Ranking 2023)
  • The University of Edinburgh- UK (#15 QS World Ranking 2023)
  • Imperial College London- UK(#16 QS World Ranking 2023)
  • UCL- UK (#17 QS World Ranking)
  • California Institute of Technology- USA (#18 QS World Ranking 2023)
  • University of British Columbia- USA (#19 QS World Ranking 2023)
  • University of Amsterdam- The Netherlands (#20 QS World Ranking 2023)

To know more, get in touch with an Edusky expert advisor today. Call

+910266405050or Email: [email protected]