"Sentiment" refers to feelings or emotions, and "analysis" means examining something in detail. Sentiment analysis, or opinion mining, is a subfield of natural language processing (NLP) that focuses on identifying and extracting subjective information from text. It aims to determine a piece of text's emotional tone or polarity, such as whether it expresses positive, negative, or neutral sentiment. Moreover, sentiment analysis is also used to detect different emotions like happiness, anger, or sadness to gauge public opinion on a topic. It's a part of natural language processing (NLP), that deals with computers understanding human language. This foundational skill is critical for understanding human emotions, opinions, and attitudes, which are increasingly valuable in applications like customer feedback analysis, social media monitoring, and chatbot interactions. This module is divided into two goals, to ensure structured learning. The focus is on building foundations in the first goal and applying sentiment analysis in real-world scenarios in the second goal.
MS Excel provides powerful tools for organizing, cleaning, and analyzing data, enabling students to interpret findings effectively. It helps to create charts and graphs to visually represent data, making it easier to present research insights. From calculating basic statistics to applying advanced formulas, Excel is equipped with essential analytical capabilities. Excel also automates repetitive tasks, saving time for other academic pursuits. A solid grasp of Excel creates a smoother transition to advanced tools like SPSS, Power BI, or R. Incorporating MS Excel early builds a strong foundation for academic success and opens doors to diverse career opportunities.
The automobile sector plays a crucial role in global economies, particularly in India, which ranks as the third-largest market worldwide. In recent years it has faced increasing scrutiny over its environmental impact, including greenhouse gas emissions, energy consumption, and labour practices. With growing consumer and investor awareness regarding sustainability, automobile companies must adopt practices that align with broader societal goals, including the United Nations Sustainable Development Goals (SDGs). However, despite the rising awareness of sustainability issues, many automobile companies struggle to implement effective strategies to achieve their sustainability targets, and complexities in selecting relevant sustainability indicators selection is identified in literature as one of the primary impediments. It is impossible for companies to integrate the measurement, monitoring, and reporting mechanisms needed to accommodate all the environmental, economic, and social sustainability indicators. As a result there is a high risk of greenwashing, i.e., reporting misleading or irrelevant indicators which can create the illusion of sustainability without real impact. Other implications include regulatory non-compliance and poor stakeholder confidence in the company. Therefore the success of their sustainability strategy rests on their approach to selecting the right indicators.
Discourse on indicator selection in case of the automobile sector primarily takes into account the Western markets where the business and political landscapes are markedly distinct. Considering this gap, this study evaluates 17 BSE (Bombay Stock Exchange)-listed automobile companies using their Business Sustainability and Responsibility (BSBR) and annual reports. Key sustainability indicators, such as emissions, energy consumption, and workforce diversity were assessed for the following Sustainable Development Goals:
The sector's environmental and social sustainability performance is assessed using Principal Component Analysis (PCA) on Python with the aim to identify the most relevant environmental and social sustainability indicators for India's automobile sector. The findings of this study will allow automobile companies identify suitable indicators for each sustainability area, so that they can create strategies focusing on the most relevant indicators to improve their environmental and social sustainability performance.
Python is a widely used programming language that is especially popular in data analysis, machine learning, and data science. Python's user-friendly syntax and powerful libraries allow users to perform various data manipulation and analysis tasks. This course invites students from all walks of life—science enthusiasts, commerce professionals, and even those from creative arts—to embrace a language that speaks to everyone.
This module provides a foundational understanding of Python, along with essential statistical techniques for analyzing data. It is designed to provide a comprehensive understanding of Python programming, starting with the very basics and progressing to essential programming concepts. With its easy-to-read syntax and growing importance in fields like artificial intelligence, web development, and data science, Python ensures you’re not just keeping pace but staying ahead in today’s digital world.