In today’s world, data has become an integral component of our daily lives. We receive an immense amount of data, ranging from minute details of physiological signals like heartbeats to intricate stock market price fluctuations which is also possible due to data science and machine learning. Data has become the new currency of the modern world, a notion that has already turned into a reality.

Online and offline platforms serve various purposes, such as marketing, growth, and behavior modification, to gather data. Using technologies like data science and machine learning, platforms are designed to collect and analyze data on a large scale, providing insights into consumer behavior and preferences that businesses can utilize for strategic decision-making.

Let’s dive deep into the world of data and understand what Data Science and Machine Learning are all about.

Introduction to Data Science and Machine Learning

Data Science 

Data science is a discipline that revolves around data with a scientific and statistical approach that includes standard procedures and algorithms. It comprises all three elements: mathematical, statistical, and computer science procedures and operations. Data science is used for extracting and analyzing data to find and predict patterns and trends. There are multiple steps involved in data science. Some of the major processes are : 

• Collection of data
  • Gathering data from various sources and compilation of those data into a single directory or location. The data may be raw, unstructured, or structured.

• Cleaning of collected data
  • Removing the outliers and unreliable data.
  • Additionally, Converting all the types of data to the same standard form 
  • Similarly, managing the missing data and inconsistencies in data

• Exploration of data
  • It is the initial method that consists of more statistical operations like calculating mean, median, variance, standard deviation, etc to get an insight into the data and its patterns. 
  • It generally involves the creation of a hypothesis and identifying areas of exploration.

• Analysis of the data
  • Analysis of the data is a more extensive process than the exploratory analysis
  • In addition to the exploration step, the data scientist is more focused on deriving some in-depth and fruitful knowledge from the data.
  • Some useful observations within the trends and patterns are represented with proper evaluation, illustration, and understanding.

In a standard manner, the data scientist executes the aforementioned process and employs various machine learning models to extract concealed distinct patterns and trends within the data.

Machine Learning

It is a sub-part of data science. Machine Learning is simply a concept where machines learn by themselves from all the activities they do and can perform any new or similar task on their own. It is based on the principle of predicting the outcome based on the previous data that was fed to it. It should be done without the involvement of humans. Machine learning is based on the training and testing of machines. Additionally, various types of algorithms on different sets of data.

Machine Learning and Artificial Intelligence

Machine learning is a  sub-element of Artificial Intelligence (AI) that focuses on the machine’s ability to do the things that humans do with human intelligence. It mainly focuses on the cognitive power of the human and tries to act like a human and make it natural as much as possible. It tries to simulate human behaviors like thinking, decision-making, planning, reasoning, learning, and solving problems. There are different machine learning models or Methods such as

• Supervised Learning
  • The process of machine learning includes using labeled training data, where we pair the input with the correct output. We train the machine on this labeled data, and then we apply various algorithms to predict the output for new or similar kinds of input.

• Example: Spam Email Detection

• Unsupervised Learning
  • It consists of the training data where the input is not labeled with any outputs. The machine should be able to identify and search over the data to find hidden patterns or trends necessary to match or generate the corresponding correct output using different algorithms.

• Example: Recommendation Systems

• Reinforcement Learning
  • It consists of two main characters: Agent and Environment. The agent can be a machine, software, or character that does some action in the environment and receive some feedback as a reward for its actions. The environment, which provides the agent with feedback, is the external factor for the agent. The feedback is based on the reward system. 

• Example: ChatGPT (mixture of both reinforcement and supervised learning)

Data Science and Machine Learning In the Context of Nepal

In response to the rapidly growing demand for expertise in data science and machine learning. Skill Shikshya, under the management of Vrit Technologies, has proudly launched a comprehensive diploma course in data science and machine learning with Python. We have designed our cutting-edge curriculum with the latest industry standards in mind. Our top-tier instructors with extensive experience in the field teach it.

Ultimately, our goal is to empower students across Nepal to succeed in the highly competitive arena of AI and Machine learning. By equipping them with the knowledge and skills necessary to excel in this rapidly evolving field. We believe that our program will help to enable Nepalese professionals to compete on the global stage.

Whether you are a recent graduate seeking to kickstart your career or a seasoned professional looking to expand your skill set. Our Data Science with Python diploma program is the perfect choice for you. With a focus on hands-on learning and real-world applications. Our program will provide you with the practical experience and theoretical knowledge necessary to succeed in today’s data-driven world.

Therefore, we invite you to join us on this exciting journey. Take your career to the next level with Skillshikshya and