What is Data Science?
Data science is an integrative field that uses technical methods, procedures, algorithms, and systems to extricate knowledge and better understand complex data and the expertise application and executable perception from data over a vast range of application domains. Data science is directly connected with data mining, machine learning, and big data. Data science is a vast field and focuses on many fields, one of which is Machine learning.
What is machine learning?
In the modern world, the word Machine learning is the talk of the town. We were unaware that machine learning was with us for a long time without being aware of its existence. It is an exciting finding that we get suggestions on various online e-commerce websites. Surprisingly, the suggested items concern the items we searched for on that site. So, here the website tracks which item we search, the manufacturing company, its competitors, alternatives to that product, and items related to that product. This makes us understand that the website is learning from our searches, which is the basis for our suggestions. This is exactly how machine learning has been working for many years.
Scope of data science
There are many sectors where the influence of data science is very significant, and one amongst them is business intelligence. Data scientists receive terabytes of data each day to study and observe the patterns and trends, and these observations are essential in drawing out conclusions. Here the roles of the Business intelligence expert and the data scientist are pre-defined. The chief duty of the business intelligence expert is to recognize the data trends concerning a particular business sector and introduce business predictions and the way of execution based on these speculations.
With ever-increasing Industrialization and globalization, the need for data science courses has increased a lot in the world today. The demand for data scientists, business intelligence experts, and business analysts is rising. There are a lot of acclaimed institutes offering data science and machine learning for working professionals that would be very important in grooming the professional path for an individual. Institutes offering business analytics courses are easy to find these days in whichever part of the world it may be.
In today’s modern age, the terms “data science “and “machine learning “are two of the most searched terms in the world of technology. From software engineering jargon to working professionals in firms like Netflix, Amazon, etc., are crazy behind these two techniques. In this competitive world of data space, the epoch of Big data appeared when organizations were dealing with petabytes and exabytes of data. It was getting tough for the companies to manage and store data until 2010. With famous frameworks like Hadoop and others that have solved the problem of storage, the focus is the processing of data, where data science and machine learning play a huge role.
Five main steps in Machine Learning in the lifecycle of Data science
1. Data Collection:
The foundation step for data learning is Machine learning. It is the base for collecting data that is reliable and relevant. They are considered data is highly crucial as the quality and extent are directly impacted by the outcome of the Machine Learning Model. This is further used for your Data Model training.
2. Data Preparation:
Cleaning the data is the initial step for the overall Data Preparation process. It ensures that Data Preparation involves cleaning and standardizing the dataset to ensure it is error-free and splitting it into two sections for Training and testing the trained model.
3. Training the Model:
In this step, the learning starts with the dataset training for the output value prediction. In the first iteration, the output will differ from the desired value. However, the “Machine” is made perfect by practice. It is required to repeat the steps repeatedly so that some changes or adjustments can be made in the initialization. Your Model is incrementally improved through the use of Training data.
4. Model Evaluation:
After the Training, your Model now is the time for performance evaluation. This process uses the database you have set aside in the Data Preparation Process. The data you have set aside has never been used in Training the Model. Thus, this database will help you design the Model and brief you about its performance in real-life applications.
Even after your Model is Trained and evaluated, it is not perfect or ready to get deployed. In this process, it is required to improve the Model further by tuning the parameters. Thus, prediction is considered as the final step in Machine Learning, and the Data Model is ready to deploy.
Now let us understand the Machine Learning Algorithms with the help of three keys in Data Science Master program.
The first key, “Regression,” is used if the output variable is in continuous space. In mathematics, you might have come across the curve-fitting techniques as “y=mx c.” It is the same technique in regression.
Classification is used then the output variables are of discrete values. It is considered a classification problem if you wish to determine which category belongs to your data. It is used to predict the class or type of fresh or new data.
To group, the data points with similar characteristics without having the labels are considered a clustering problem. A Clustering Algorithm finds patterns in a dataset without acquainting labels with the data points. The Clustering Algorithms group similar points together based on a different definition of similarity.
So, here we can conclude that data science is the initial source for machine learning as machine learning is based on the information and observations fed on the systems. The machines are used to deliver an output or provide a search result and can be explained in detail in various MTech Machine Learning courses. You can choose such online courses from Great Learning to understand better and move ahead.