Course Overview: The Post Graduate Diploma in Data Science (PGDDS) is a program offered by the National Institute of Professional Promotion Skills (NIPPS). This program is designed to equip students with the knowledge and skills required to excel in the rapidly evolving field of data science. Below, we will explore the key components of this program, including eligibility, fees, duration, mode of study, study materials, and the course structure.

Eligibility Criteria:

To be eligible for the PGDDS program at NIPPS, candidates must meet the following criteria:

• Graduation: Candidates should have successfully completed their graduation in any discipline. This educational background ensures that students have a foundational knowledge base before diving into the complexities of data science.

• Any Other Relevant Qualification: In addition to a bachelor's degree, NIPPS may consider other relevant qualifications or experience on a case-by-case basis. This flexibility allows individuals with diverse educational backgrounds to pursue the program.

Program Details:

• Total Fees: The total fees for the PGDDS program at NIPPS are Rs. 6,500/-. Additionally, there is an admission and form fee of Rs. 1,000/-. This fee structure is designed to make quality data science education accessible to a wide range of students.

• Duration: The PGDDS program has a duration of 12 months. This year-long program is structured to provide students with in-depth knowledge and hands-on experience in data science concepts and techniques.

• Mode of Study: The PGDDS program at NIPPS is offered in online mode of study. The online mode allows for flexibility.

• Study Material: Upon enrollment in the PGDDS program, students receive their study materials by post at their provided address. These study materials are carefully curated to cover the essential topics and provide a comprehensive foundation in data science.

Course Structure:

The PGDDS program at NIPPS is structured to cover a wide range of topics and skills relevant to data science. Below is an overview of the key subjects and modules that students can expect to encounter during their studies:

• Introduction to Data Science: This module provides a foundational understanding of data science, its applications, and its importance in various industries. Students learn about the data science workflow and its role in decision-making.

• Statistics for Data Science: A strong grasp of statistics is fundamental to data science. This module covers essential statistical concepts, data analysis techniques, and data visualization.

• Data Wrangling: In this module, students learn how to collect, clean, and preprocess data. Topics include data extraction, transformation, and loading (ETL) techniques.

• Machine Learning Fundamentals: This module introduces students to machine learning concepts and algorithms. Topics include supervised learning, unsupervised learning, and evaluation metrics.

• Big Data Technologies: As data volumes continue to grow, handling big data is crucial. This module explores technologies such as Hadoop and Spark for processing large datasets.

• Data Visualization: Effective data visualization is vital for conveying insights. This module covers visualization tools and techniques to create compelling data presentations.

• Python Programming: Python is a widely used programming language in data science. This module teaches Python programming from the basics to advanced data manipulation and analysis.

• Deep Learning: Deep learning is a subset of machine learning focused on neural networks. This module delves into deep learning architectures and applications.

• Project Work: Students typically undertake a data science project where they apply their knowledge and skills to real-world data problems. This provides hands-on experience and a portfolio piece for future job prospects.

• Industry Case Studies: To bridge the gap between theory and practice, this module explores real-world data science applications and case studies in various industries like finance, healthcare, and marketing.

• Data Ethics and Privacy: Data ethics and privacy are essential considerations in data science. This module discusses the ethical use of data and privacy regulations.

• Career Development: To prepare students for a successful career in data science, this module covers job market trends, interview preparation, and resume building.

• Final Examination: The program typically concludes with a final examination to assess students' understanding of the material covered.

The Post Graduate Diploma in Data Science (PGDDS) program at NIPPS offers a comprehensive curriculum that covers the essential aspects of data science, from foundational concepts to advanced techniques. With flexible study options, affordable fees, and study materials delivered to students' doorsteps, this program aims to make quality data science education accessible to aspiring professionals. Upon completion of the program, graduates are equipped with the knowledge and skills needed to excel in the field of data science and make a meaningful impact in various industries.