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Big Data Doctor of Philosophy or Ph.D., is a specialty-focused doctorate level degree program in big data that mainly focuses on the professional development of the scholar. The program usually takes 3-5 years to complete the requirements for a degree. It encompasses the completion of numerous courses in such areas of specialization as machine learning, data mining, and predictive modeling, data visualization, and statistics. The students also engage in their individual research and submit a dissertation that provides a new contribution to the knowledge in the area of big data. The goal of the Ph.D. Big Data degree program is to equip the students with sufficient knowledge and skills in data science, prepare them for research careers and academic positions, as well as to prepare them for positions in research, analytics, and leadership in business.
While applying for admission into the Ph.D. Big Data programs, there are some customary entry tests that are accepted. These include the Graduate Record Examination (GRE) which assesses the students in verbal reasoning, Quantitative reasoning and analytical writing. Some of the elite institutions also consider GMAT scores in place of GRE for big data programs that have more of a business orientation. On the same note, some colleges have their own entrance examinations as well as end of semester examinations. Most programs also demand that applicants had a strong academic preparation especially a master’s degree in a related quantitative field who have working experience, good letters of recommendations and statement of purpose. The cut off is rather stringent due to the complexity of the doctoral level analytics programs that the students are pursuing.
| Feature | Description |
|---|---|
| Full Form | Doctor of Philosophy (Big Data) |
| Duration | 3-5 years (full-time), Can be longer for part-time |
| Course Level | Doctoral Level |
| Eligibility | Master's degree in Computer Science, Information Technology, Statistics, or a related field with a strong foundation in data analysis, programming, and distributed systems, and minimum marks (usually 55%) |
| Top Colleges (India) | Indian Institutes of Technology (IITs) - Delhi, Bombay, Kanpur, Kharagpur, Madras, Indian Institute of Science (IISc) Bangalore, Indian Statistical Institute (ISI) Kolkata, Delhi |
| Entrance Exam | Varies by Institution (Common exams include UGC-NET (Computer Science/Mathematics/Statistics), institute-specific entrance tests) |
1. A master degree holder in computer science or information technology or any other discipline with 60% marks or more than 6.5 in equivalent CGPA.
2. Passing marks in national/state level examinations and tests such as GATE/NET or any test conducted by the university. In some cases, institutes conduct their own entrance test and one should prepare for it accordingly.
3. The candidate must have a good problem-solving aptitude and should be well versed with programming languages, data structures, algorithms, databases, machine learning algorithms, and statistics.
4. Letter stating reasons for interest in the institute and the specific area of focus germane to the institute’s specialties.
5. Once again I find that publications in reputed journals show research abilities but you may not necessarily need them.
6. Clear speaking and writing and previous working experience may be beneficial during enrollment. Recommendation letters also help.
| Subject | Important Topics |
|---|---|
| Big Data Fundamentals | Big Data Concepts & Technologies, Distributed Computing Systems (Hadoop Ecosystem), Big Data Storage & Management (HDFS), Data Warehousing & Business Intelligence for Big Data |
| Data Management & Analytics | Data Preprocessing & Feature Engineering for Big Data, Scalable Machine Learning Techniques for Big Data, Big Data Stream Processing, Real-time Analytics |
| Big Data Programming | Big Data Programming Languages (e.g., Python, R with Spark), MapReduce Programming Model, Apache Spark & Spark SQL, NoSQL Databases for Big Data |
| Big Data Architectures | Big Data Platforms (Hadoop, Spark), Cloud Platforms for Big Data (AWS, Azure, GCP), Data Lake Architecture, Data Security & Privacy in Big Data |
| Specialization | Job Titles | Average Salary (INR) |
|---|---|---|
| Big Data Engineering | Big Data Architect, Big Data Engineer, Cloud Data Engineer (Big Data Focus) | 14-20 Lakhs |
| Big Data Analytics | Big Data Analyst, Big Data Scientist, Business Intelligence Analyst (Big Data) | 12-18 Lakhs |
| Big Data Visualization | Big Data Visualization Specialist, Data Visualization Engineer, Business Intelligence Specialist (Big Data Visualization) | 10-15 Lakhs |
| Big Data Security | Big Data Security Architect, Data Security Analyst (Big Data), Cloud Security Engineer (Big Data) | 12-18 Lakhs |
| Big Data Applications (Specific Industry) | Big Data Scientist (Healthcare), Big Data Analyst (Finance), Big Data Engineer (Social Media) | 12-18 Lakhs |
