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Post Graduate Program in Data Science

Duration: 1 Yr / 40 Weeks 220 hrs

Industry Acceptance

The Post Graduate Programme in Data Science and Machine Learning from NIRA is highly regarded in the industry for its comprehensive curriculum that includes Python, ML, Tableau, and AI technologies. Graduates are equipped to tackle real-world data challenges, making them valuable assets in various sectors such as finance, healthcare, and technology. The program’s alignment with current industry demands ensures that participants are job-ready and can command competitive salaries. Additionally, NIRA’s accreditation and selective admission process underscore the program’s quality and relevance in today’s data-driven landscape.

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Registration Fees

₹ 1500

non-refundable

Course Fees

₹ 155,000

Flexible EMI options available.

Batch Start

Aug, 2025

Duration

1 Yr / 40 Weeks 220 hrs

Program Structure

Eligibility: Bachelor's degree from a recognized institution , with Mathematics as a subject with at least 50% aggregate marks and 1 year of work experience (Preferably)

Duration: 1 Yr / 40 Weeks 220 hrs

Batch Start Date: Aug, 2025

Batch Size: 25-30

Courses and Certification Conduct Policy:

What are the benefits to pursue Post Graduate Program in Data Science?

High Demand and Evolving Field:

Booming Job Market: Data Science is consistently ranked among the fastest-growing and most in-demand job fields. According to the Bureau of Labor Statistics, data scientist employment is projected to grow by 26.2% from 2020 to 2030, much faster than the average for all occupations.
Wide Range of Opportunities: Data science skills are applicable across diverse industries like finance, healthcare, technology, retail, and more. This adaptability gives you flexibility and options in your career path.
Competitive Salaries: Due to the high demand and specialized skillset, data scientists often command high salaries. The median annual wage for data scientists in the US was $126,830 in May 2022, according to the Bureau of Labor Statistics.

How this program would benefit in personal and professional growth?

Develop In-Demand Skills: You'll acquire valuable technical skills like programming, data analysis, and machine learning, along with soft skills like problem-solving, communication, and critical thinking.
Stay Ahead of the Curve: The data science landscape is constantly evolving. A PGPDS keeps you updated with the latest advancements and technologies, future-proofing your career.
Boost Your Employability: Graduates with a PGPDS in data science stand out in the job market with their specialized knowledge and hands-on experience.
Make an Impact: Data science offers the opportunity to solve real-world problems and contribute to meaningful developments in various fields.

What skills you would learn from PGP in Data Science from NIRA?

Programming Skills: You’ll become proficient in programming languages such as Python and R, which are essential for data analysis and machine learning tasks.
Machine Learning Techniques: You’ll master a variety of machine learning algorithms, enabling you to build predictive models and perform complex data analyses.
Data Analysis and Visualization: You’ll learn to analyze large datasets and visualize data using tools like Tableau and Power BI, which are crucial for interpreting data and making informed decisions.

Program Structure:

Module 1:

Python for Data Science +

Dive into Python’s data types, structures, and libraries like Pandas and NumPy, and learn to perform data manipulation, analysis, and visualization.

Business Analytics using Excel +

Master Excel’s formulas, functions, and data visualization tools, and explore data analysis techniques like PivotTables and advanced charting.

Power BI +

Learn data modeling, DAX, and visualization in Power BI, and understand how to connect to various data sources and publish reports.

Tableau +

Understand Tableau’s interface, data types, and visualization techniques, and learn to create interactive dashboards and stories.

Essential Statistics +

Cover descriptive and inferential statistics, probability distributions, hypothesis testing, and data interpretation.

Module 2:

R for Data Science +

Explore statistical computing and graphics, including data manipulation, analysis, and visualization using R’s extensive package ecosystem.

Data Analysis using SQL +

Learn to query and manipulate data within relational databases, understand data types, and optimize SQL queries for data analysis.

Predictive Analytics (PA) +

Delve into statistical techniques to forecast future events, utilizing historical data, data mining, and machine learning algorithms.

Use Case +

Understand the creation of use case diagrams to represent system functionalities and user interactions in system design.

Learning Analytics (LA) +

Study the measurement and analysis of educational data to optimize learning and educational environments.

Module 3:

Time Series +

Explore forecasting models like ARIMA, and understand concepts like seasonality and trend analysis for time-dependent data.

Deep Learning +

Delve into neural network architectures, including CNNs and RNNs, and learn about backpropagation and dropout techniques for model optimization.

Natural Language Processing (NLP) +

Study text pre-processing, sentiment analysis, and language modeling, along with the use of transformers like BERT for advanced NLP tasks.

Analytic Process Automation (APA) +

Learn about automating data preparation, analytics, and business processes, and understand the role of AI and machine learning in enhancing APA solutions.

Module 4:

Big Data +

Learn about data processing tools and techniques, and explore the ethical implications and security challenges of handling large-scale data.

Domain Training on HR Analytics +

Understand the application of analytics in HR for talent management, performance analysis, and recruitment strategies.

Computer Vision +

Delve into image processing, object detection, and advanced techniques like 3D reconstruction and machine learning in vision applications.

Essentials of General AI +

Cover the basics of machine learning, natural language processing, and the ethical considerations in AI deployment.

Use Case +

Examine how use cases describe user-system interactions to accomplish tasks, including actors, goals, and system responses.

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