Common sense is a form of data science. In a very simple sense, data science is a computational form of common sense leading to predictive analytics and generative AI. In the technological world, data science is already making headway. Embracing this technological disruption is the need of the hour. Staying updated on fast-changing technology is important for personal and career success. It helps with innovation, staying competitive worldwide, and solving current problems. Learning about Artificial Intelligence (AI) and Machine Learning (ML) is especially important for students in STEM (Science, Technology, Engineering, and Mathematics). These fields are key in today’s technology. They offer many job opportunities and inspire new ideas for research, development, and creativity. AI is about computers mimicking human intelligence.
In ancient history, certain aspects of astrology were based on data science. Some proponents of Indian astrology attempted to use data science techniques to analyze astrological data and identify patterns that might correlate with certain life events or traits. People often do this to give more evidence for astrological claims. Vedic astrology is one method used to make predictions. It is a type of data science. In data science, large amounts of information (called data) are used to find patterns and make forecasts. Now, developed countries use data science to track individuals’ organizations so that they can predict via patterns and algorithms.
You can see the use case everywhere. Detective agencies use pattern recognition techniques for anomaly detection by analyzing large volumes of data from social media, communication, financial transactions, and travel records. People use natural language processing techniques to understand sentiment. Sentiment analysis means figuring out the emotions or opinions in text, like if someone is happy, angry, or neutral. You will find many uses of data science in social network analysis, predictive analytics, risk management, and behaviour analysis.
Data Science
The most important thing in data science is “data” But the question is, do we have the required data? How much data? Real time? What format? Creating relevant features from raw data is essential for building effective machine-learning models. Keeping the data in most cases, like text files and Excel files, will be a catastrophe if you are handling large volumes. You need to extract sufficient data from various sources properly. Data becomes garbage if the required data is not available. We need to keep this data in a proper format that is compactable. Data is not static. It changes over time, and once effective, models may become obsolete as the data distribution shifts. Data scientists often need to work closely with subject-matter experts to understand the data and its context.
Data science projects often require significant computational resources, both in terms of hardware and time.A data warehouse is a central storage for company data from various sources. It supports business intelligence (BI) by enabling data-driven decisions, report creation, and analysis. Popular data warehousing technologies include Amazon Redshift, Google BigQuery, Microsoft Azure SQL Data Warehouse, and Snowflake. These Modern data warehousing solutions often include both on- premises and cloud-based options, and they can also integrate with data lakes to handle more diverse and unstructured data types.
ETL Process
ETL is a critical component of data science and analytics; it stands for Extract, Transform, and Load. It describes a group of steps used to combine and organize data. These steps help move data from original sources to a final location. In that location, the data can be studied, searched easily, and used for reports. An ML algorithm is a computational method or procedure that enables computers to learn from data and improve their performance on a specific task over time.These algorithms help computers find patterns. They also let computers make predictions and decisions. The computers do this without being told exactly what to do for every situation. Ex: linear regression, logistic regression, decision trees, reinforcement learning, random forest, KNN, neural networks, etc. ML algorithms enable data scientists to create models that learn from data, generalize patterns, and make informed predictions or decisions.
These algorithms are essential in modern data science. They turn raw data into useful insights and help drive innovation in many fields. Data Analytics (DA) interns can use them for tasks like automating decision-making, recognizing patterns, predicting future events, analyzing images, detecting unusual data (called anomalies), diagnosing medical conditions, and improving processes (optimization). They also help AI work with larger and more complex data sets. This allows AI to find important information from unorganized data and automate repeated tasks. As a result, we can spend more time on bigger-picture analysis and creating strategies.
Gen – AI
I work at Altair, a global tech company focused on computational science—using computers to solve complex problems. The goal is to enable smart decisions for a safer, more sustainable future. With 30+ years of data science experience, Altair offers solutions across industries like banking, retail, manufacturing, and engineering. A key tool is Altair RapidMiner, available as both desktop and cloud-based versions (SaaS), designed for multi-user environments. It enhances Altair’s data analytics capabilities, helping customers understand, act on, and automate their data.
Generative AI refers to a category of artificial intelligence techniques that involve creating new content, such as images, text, music, or other forms of data, using algorithms and models. Generative AI is different from traditional AI. Traditional AI systems follow specific rules and instructions. They are predictable. Generative AI, on the other hand, tries to imitate human creativity. It creates new and original content.
There are various online platforms for learning different aspects of AI and ML. I found them by doing quick searches. Some examples are Code.org, aiworldschool.com, the Swift Playgrounds app by Apple, Teachable Machine by Google, CodeCombat, AI4K12, Mimo, and Scratch by MIT. Learning should happen step by step. It’s fine to begin with simple ideas and slowly move to harder ones. The most important thing is to make learning fun and useful. This way, students stay interested and focused as they explore AI (artificial intelligence) and ML (machine learning). Engage students with hands-on activities that illustrate AI and ML concepts. For example, you could guide them through simple programming exercises using tools like Scratch or educational platforms like Code.org.
The author is working as head of academic initiatives at Altair. He is instrumental in Altair India’s academic initiatives, viz., incubators, startups, universities, and industry.
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