PG Diploma in data science is a 10-month - 2 years duration course that provides specialised training in data science and data analytics, which is used in computational and biological sciences. A PG Diploma in data science provides a faster path to complete a qualification, less financial investment than a college degree, and enter the workforce than a bachelors’ or master’s degree in Data Science. The need for data scientists is growing, yet the supply is inadequate. It is the fastest growing career on Linkedin, with 11.5 million positions expected to be created by 2026. NMIMS Mumbai, Chandigarh University, IIIT Lucknow are the top colleges that offer PG Diploma in Data Science. The placements at NMIMS Mumbai record average salaries up to INR 15 lakhs. According to their research, the national average income for a Data Scientist in India is INR 6,50,000, while the national average income in the United States is $1,20,931.
Course | Advanced Diploma In Data Science |
Full Name | Advanced Diploma In Data Science |
Stream | Computer Applications |
Eligibility | 10+2 |
Duration | 1 Year |
Type | Diploma |
Mode | Year |
The admission process for an Advanced Diploma in Data Science can vary depending on the institution offering the program. However, here is a general overview of what the admission process might entail Research Programs: Begin by researching different institutions that offer Advanced Diploma programs in Data Science. Look for reputable institutions with relevant accreditation and a strong curriculum. Check Eligibility Criteria: Review the eligibility criteria for the program. This may include educational qualifications, such as a bachelor's degree in a related field like Computer Science, Mathematics, Statistics, or Engineering. Some programs may also require certain prerequisite courses or proficiency in programming languages like Python, R, or SQL. Application: Once you've identified the programs you're interested in, submit an application through the institution's website or admission portal. Make sure to fill out all required fields and provide any necessary documentation, such as transcripts, letters of recommendation, and a statement of purpose.
Review Exam Content: Obtain information about the content and format of the entrance exam. This may include topics such as mathematics (algebra, calculus, probability, etc.), statistics, programming languages (such as Python or R), data structures, and basic machine learning concepts. Study Material: Gather study materials such as textbooks, online courses, practice exams, and study guides covering the topics that will be tested on the entrance exam. Look for resources specifically tailored to data science entrance exams or covering relevant subjects. Practice Problems: Practice solving problems and answering questions related to the exam content. Work through example problems, exercises, and practice tests to reinforce your understanding of key concepts and improve your problem-solving skills. Programming Skills: If the entrance exam includes programming questions, make sure to practice coding in relevant programming languages such as Python or R. Familiarize yourself with common data manipulation and analysis techniques and algorithms used in data science.
The admission process for an Advanced Diploma in Data Science can vary depending on the institution offering the program. However, here is a general overview of what the admission process might entail Research Programs: Begin by researching different institutions that offer Advanced Diploma programs in Data Science. Look for reputable institutions with relevant accreditation and a strong curriculum. Check Eligibility Criteria: Review the eligibility criteria for the program. This may include educational qualifications, such as a bachelor's degree in a related field like Computer Science, Mathematics, Statistics, or Engineering. Some programs may also require certain prerequisite courses or proficiency in programming languages like Python, R, or SQL. Application: Once you've identified the programs you're interested in, submit an application through the institution's website or admission portal. Make sure to fill out all required fields and provide any necessary documentation, such as transcripts, letters of recommendation, and a statement of purpose.
The eligibility criteria for an Advanced Diploma in Data Science can vary depending on the institution offering the program. However, here are some common eligibility requirements you might encounter Educational Qualifications: Typically, candidates must have a bachelor's degree in a relevant field such as Computer Science, Mathematics, Statistics, Engineering, Economics, or a related discipline from a recognized university or institution. Minimum GPA: Some institutions may require applicants to have a minimum Grade Point Average (GPA) or equivalent academic performance in their undergraduate studies. This GPA threshold can vary depending on the institution and the competitiveness of the program. Prerequisite Courses: Certain programs may have prerequisite courses or foundational knowledge requirements in areas such as mathematics (including calculus, linear algebra, and probability), statistics, programming (Python, R, SQL, etc.), and computer science concepts.
To apply for an Advanced Diploma in Data Science, follow these general steps Research Programs: Start by researching different institutions that offer Advanced Diploma programs in Data Science. Look for reputable institutions with relevant accreditation and a strong curriculum. Check Eligibility Criteria: Review the eligibility criteria for the program. Make sure you meet all the requirements, such as educational qualifications, prerequisite courses, and proficiency in programming languages. Visit College Websites: Visit the websites of the institutions offering the Advanced Diploma in Data Science programs. Look for information about the application process, admission requirements, and deadlines. Prepare Application Materials: Gather all the necessary documents required for the application. These may include transcripts, letters of recommendation, a statement of purpose, a resume or CV, and proof of English proficiency (if applicable).
Semester I | Semester II |
Introduction to data science | Python |
Mathematics and Statistics | Data Analysis |
Database Management Systems | Data Wrangling |
Statistical Foundations for Data Science | Data Visualization |
Machine Learning Algorithms | Exploratory Data Analysis |
Business Communication | Data Mining and predictive analytics |
Data Analytics using SQL & Excel | Business Analytics using SAS |
Analytics with R & Tableau | Predictive Analytics and Segmentation using Clustering |
Data Structures and Algorithms | Business Acumen & Artificial Intelligence |
Scientific Computing | Information Technology |
Experimentation, Evaluation and Project Deployment Tools | Time Series Model |
Data Scientist
Statistician
Actuarial Scientist
Mathematician
Software Developer Analyst
Application Architect
Business Intelligence Developer
Machine Learning Engineer
Data Architect
Clinical Data Scientist
Machine Learning Scientists
Data Engineer