4.1 Data Science

Data science is a branch of knowledge, which joins together mathematics and statistics skills with the programming domain of computer science. This combination is useful in various fields of life e.g. if we want to predict the result of a football match, data science will help us. Currently, in the age of artificial intelligence, organization of available data into meaningful form is necessary, Data Science helps us to achieve the objective of getting insight from the data. Consequently, we can find optimized solutions along with predictive modeling. In the previous grade we learned some basic concepts about data science. Now, we will learn some advanced topics based on our previous knowledge.

Consequently, we can find optimized solutions along with predictive modeling. In the previous grade we learned some basic concepts about data science. Now, we will learn some advanced topics based on our previous knowledge.

4.1 Data Science

Data Science refers to an interdisciplinary field of mathematics, statistics, and computer science. Main objective of data science techniques is to get meaningful insight from the available data. The available data can be structured as well as unstructured. This objective is achieved through the steps of data collection, data cleaning, data preprocessing and data visualization. Various programming languages like Python, R studio and MATLAB can be used for data visualization.

4.1.1 Data Science and its scope

Data Science has wide scope in many disciplines of life and it is continuously expanding. We can briefly learn about its scope in some common fields of life as follows:

Healthcare: To predict the possibilities of some disease, based on X- Rays, Ultrasounds or other analytics of patient.

Sports: If we want to predict who is going to win a cricket match, based on the previous performance of teams, data science will help us.

Finance: In the banking and finance sector, data science is helpful in fraud detection. It is also helpful in developing trading and investment strategies.

Goods transportation: It helps in route optimization and minimize delivery times. Data driven approach helps to reduce cost. It also helps in real time tracking of transportation of goods.

Airline: It helps in efficient route planning to reduce fuel consumption. It helps to improve services based on customer experience and feedback.

Energy and utilities: It helps to optimize energy production and distribution. Realtime monitoring is useful for load distribution and facilitates immediate response for demand fluctuations.

Education: It is useful to enhance personalized learning based on the performance of learners. It also helps to predict students' performance based on previous data.

Communication Media: It is used for content creation and distribution. Various approaches like sentiment analysis, social media monitoring and recommender systems are a few examples of scope of data science in media and communication sector.

Entertainment: Data science plays important role when you get suggestions about your favorite songs while playing online computer games.

Business: Data science helps in intelligent decision making about the business to get optimal profits. Customer insight and sentiments are also analyzed to improve the business.

4.1.2 Artificial Intelligence and its scope

The term artificial intelligence is not new. In 1950 a British mathematician Alan Turing proposed a Turing Test, which measured the ability of machine to exhibit intelligent behavior. Now a days the modern robots are supposed to be smarter if they can pass the Turing Test. The term Artificial Intelligence is referred to the ability of a machine to exhibit human behavior like problem solving, understanding natural language and interacting with the environment intelligently.

Like data science, the scope of artificial intelligence is wide. Here, we can discuss a few of them as follows:

Decision making: Artificial intelligence is very useful in optimal decision making, based on a data driven approach.

Personalized recommendations: It helps to provide personalized feed to customers, based on their past usage of machines.

Automation industry: It has played a vital role in automating the tedious jobs like car manufacturing and image & video analysis. The most important utilization of artificial intelligence is integration of internet in the devices of daily use. The entire industry of IoT and smart devices is based on automation.

Natural Language Processing (NLP): AI enabled the machines to understand and respond to the natural human languages. ChatGPT and chatbots are examples of it. Now you can switch ON your air conditioner with voice command rather than picking up the remote controller.

Robotics: Whether you watch some latest robot, talking and cooking like a human being or you get a small robot to clean your house, all this technology is due to significant advancement of artificial intelligence.

Healthcare: Like data science, artificial intelligence has played equally well in the field of healthcare. The examples are personalized treatment recommendations based on medical imaging.

Computer vision: It is a specialized branch of AI that teaches computers to draw meaningful results from digital images, videos and other visuals.

Smart cities: Artificial Intelligence enables to develop efficient infrastructure and services, which can be used to manage energy consumption, traffic management and other necessary tasks for smart cities.

AI Agents: Artificial Intelligence provides bases to develop modern AI agents like Siri, Alexa, Google assistant, ChatGPT and Cortana.

4.1.3 Machine Learning and its scope

Machine learning is used in many fields of life, like healthcare, automation industry, to develop recommender systems, finance and banking, pattern recognition, NLP, computer vision, research and innovation. An example of machine learning is Automated fraud detection. It helps to identify fraudulent activity by finding anomalies such as sudden large transaction or unusual network traffic. In short, the entire scope of Data Science and Artificial Intelligence is based on the algorithms developed in the field of Machine learning.

4.1.4 Data Science, Artificial Intelligence and Machine Learning Skills

Artificial Intelligence and Machine Learning skills refer to develop systems that can learn and perform decision making. Data Science skill refers to extract insights from data and make informed decisions. There are many skills to achieve excellence in artificial intelligence. These skills include programming language, machine learning, and acquiring domain specific knowledge. In programming languages, Python and R are the most used languages in Al. In machine learning algorithms, the knowledge of TensorFlow framework, deep learning, neural networks and NLP are very important.

4.1.5 Machine Learning Models

Primarily, there are three models of machine learning.

Supervised Machine Learning Model Unsupervised Machine Learning Model Reinforcement Machine Learning Model