As of now, we are surrounded by modern technologies such as Data Science, Machine Learning, Big Data and many more. Most of you will often be confused with the relation of it to one another. It has been observed that people confuse Machine Learning with Data Science and wonder whether there is any significant relationship between the two or not. There is no doubt in stating that all the above modern technologies have a close relationship between them. But their functionality differs from one another as well as purpose.
In this blog, you will be discussed with Machine Learning and Data Science in detail and at the end of the day, you will even get to know whether they are related to each other or not. Read the blog to know how machine learning and data science related.
Also Read: What are the Differences Between AI and Machine Learning?
Being an aspirant you must not have any doubt regarding this subject and we will help you to clear this myth of Data Science and Machine Learning through this blog. You might have already known that this is an era of technology and we are surrounded by lots and lots of it. In other words, technology has taken over us.
But the introduction of modern technology like AI (Artificial Intelligence), Machine Learning, Data Science has been the new hot topic in the technology domain. Without discussing further let’s go straight to the topic and discuss Machine Learning and Data Science to figure out how they are interrelated to each other.
Machine Learning – What it is all about?
Machine Learning is one of the most appreciated technologies that has already got wide attention due to its robust application. The concept of Artificial Intelligence comes into play here, where machines educate themselves following the experience and predict what can be the aftermath of the situation.
As of now, Machine Learning is widely used across many fields, rather, it has become effective in all areas. To be very Frank Machine Learning can be categorized as a subset of AI.
1. Impact of Authenticity and Quality of Data in Preparing the Model
Preparing the model nature is the basic task of Machine Learning. For better preparation of this model, it is necessary to be assured with authenticity as well as data quality so that it could represent well in the model.
There are a lot of steps involved in Machine Learning not only the nature of the model is all about the quality of data. Rather, data preparation is also crucially important here.
2. Data Preparation
To prepare the data for training, it is necessary to assemble it properly. Now, there is a certain process involved in fulfilling these events like applying filters to it during the most relevant option. The scientist also had to deal with missing values, normalization, the secondary data type in this process.
3. Model Training
After the above tips and done then it comes training the model. Here it is emphasized on training the model and look up to the answers of the questions and see whether they predict correctly or not.
4. Model Evaluation
Model Evaluation is a very important step to determine the performance of your model. The best way to deal with model evaluation is by utilizing a metric combination.
Once this is done it is recommended to compare your model with the data that was previously for the sake of testing.
5. Parameter Training and Making Predictions
Here the final step arrived that is parameter training and making predictions of the model. To enhance performance, it is necessary to tune the parameters of the model.
Now you have properly gathered and analyzed all the data and tested it rigorously and your next step would be to predict the performance of the model and related it to the real world.
This accurate prediction feature of Machine Learning has got enough appreciations in the world and this is what makes many organizations opt for it.
Knowing about Data Science
The concept of Data Science is completely different from Machine Learning which involves some other chapters related to data scientists. Data science is surrounded by basic maths and statistics as well as hacking skills to accumulate big data from different sources.
Once the big data has been gathered the collaboration of Machine Learning is done here so that important information can be hunt down from the data gathered. If you are wondering how data is understood here, then it is to let you know that data here is following the business requirement.
It would be clear to you with an example like, if you are logged into Amazon you will take a considerable amount of time by going to the products. Now when you scroll down the products and look up to it this will create some data which is termed as data generation.
All this data will be monitored strongly by the data scientist to analyze the behaviour of the user. Your behaviour is judged by the data scientist and you will get ads accordingly which will tempt you to buy a product. I hope with this example you have understood the concept of Data Science.
Now that you have known what is Data Science in the below section we will be discussing about data science process.
What Process involved in Data Science
Basically, data science involves three important aspects which are discussed below
- Collection of data
- Modelling of data analysis
- Decision support
If you think that Data Science is all about surrounding these three aspects then you are wrong. Because a Data Scientist has to deal with a lot of questions and in the below, sections will get to know about it.
1. Asking question
Some of the most common questions are whether there is any business goal to fulfill? Which parameters could fulfill the ideal answer?
2. Why Designing of Data Collection is Crucial?
Once you have explained the problem well you are now required with the data for solving it properly. Your deep thought process is required here, like thinking what data you will require and also figuring out the ways to get it. Be it an internal data basis or external data sets you have to find ways in this situation.
3. Processing the Data for Analyzation
The processing of data is a very important step before you proceed with the analyzation of the problem. You might experience with messy data if it is not properly maintained. This will lead to a lot of errors.
4. Exploring the Data
Now you are done with Data Cleaning and the next step is exploring it. There are a few problems that you will face in this process.
However, one of the most common problems that everybody has to tackle is the lining of the questions according to the deadline you fixed for the data science project. You will find some interesting patterns that will speak for the fall of your sale.
Conclusion
Data Science is a very big concept and is applicable for various disciplines and Machine Learning is suitable for it. There are many techniques for which machine learning has been extensively used such as regression and supervised clustering. As mentioned earlier Data Science involves a wide area that cannot dig into complex algorithms.
However, the importance of Data Science cannot be ignored in the present scenario as it plays a predominant role in structuring Big Data. Besides that, it tips for completing patterns as well as gives proper advice to the decision-makers to bring revolution in the needs of the business. Hope this blog has helped you to know about how Machine Learning and Data Science Related.
