This article was published as part of the Data Science Blogathon.
Introduction
is created in one click. This data is valuable for any organization and company. In this digital age, we are always connected to the internet. And this leads to a lot of data generation. This data brings success to companies for their business problems and day-to-day solutions.
Do you know that data is the ultimate goal of every organization and, therefore, I think they are the ones who rule? No data, nothing can be achieved. From a business perspective to troubleshooting for end-to-end applications, we need data.
These data must be to derive some purpose from them. Because the forms of the data can be texts, images, videos, infographics, gifs, etc. Some data is structured while most is unstructured. The compilation, analysis and prediction are the necessary steps to take into account with this data.
However, What exactly is data science and machine learning?
I will define it in a simple way. All the context related to this can be similar if you look elsewhere. Therefore, data science is the science of gaining insights from data in order to obtain the most important and relevant source of information. And with a reliable source of information that makes predictions using machine learning.. So I guess you have understood this definition very well. Now, my point here is that with data science you can bring in valuable insights.
Why is data science and machine learning necessary??
The data has been there for a long time. In earlier times, data analysis was carried out by statisticians and analysts. The data analysis was carried out mainly to obtain the summary and what were the causes. Mathematics was also the central topic of interest when used for this work.
It was not a cumbersome process because there was a limited amount of data. Business problems were also mainly solved by using software tools like Microsoft Excel. This tool is also used for data analysis. Here, when I say business problems, are specifically in digital format. As companies began to digitize, The internet and cloud computing became the backbone of his establishment. There was a large amount of data generation in millions of bytes, what is generally known as big data. With the advent of social media, powerful search engines like Google and YouTube, it became mandatory for these companies to handle your data with care.
How data science and machine learning solutions?
Data science uses statistical methods, math and programming techniques to solve these problems. Programming techniques are widely used to analyze, visualize and make predictions. As you see, does all the work of a statistician, programmer and mathematician. Studying all of these important areas is the best way to deal with this kind of big data.. Machine learning is integrated by creating models from various algorithms.
This is done for model building in data science, which helps future predictions. These predictions depend on the new data that is given to the model without explicitly telling it what to do.. The model understands it and then gives us the result or the solution. For instance, banks use machine learning algorithms to detect if there is a fraudulent transaction or not. Or if this client does not pay their credit card fees.
Cancer screening in the healthcare industry uses data science and machine learning to detect whether or not patients are prone to cancer. So there are a lot of examples around us where companies are using this across the board.. Online food delivery companies like zomato or swiggy use to recommend food to us to order based on what we have ordered in the past. This type of machine learning algorithm is a recommendation system. They are also used by YouTube, Spotify, Amazon, etc.
The data science life cycle.
There are several steps involved in solving business problems with data science.
1. Data acquisition – this process involves the collection of data. It depends on what the objectives are or what is the problem to be solved. This way, we tend to collect the necessary data.
2. Data preprocessing – This stage involves the processing of data in a structured format to facilitate its use.. Unstructured data cannot be used for any analysis because it will give wrong business solutions and may have a negative impact on consumers.
3.Exploratory data analysis (EDA) – It is one of the most important stages where all the data summaries are found by statistics and mathematics. Identify the target variable (Exit) and the predictor variables (independent). Data visualization and then classification of all necessary data to be used for predictions. Programming plays a vital role in this. A data scientist spends almost 75% of your time to this to understand your data very well. What's more, in this stage, data is divided into training and test data.
4. Construction of the model – After EDA we select the most suitable methods to build our model. This is done with the use of machine learning algorithms.. Algorithm selection as regression, classification or grouping. As machine learning algorithms are of 3 types. Supervised learning, unsupervised learning and reinforced learning. There are different sets of algorithms for all these types. Selecting them mainly depends on the problem we are trying to solve.
5. Model evaluation – Model evaluation is done to see how efficient our model is performing on the test data. Minimization of errors and also fine-tuning of the model.
6. Mode displayl: the implementation of the model is carried out since it is now in a position to attend to all future data to make predictions.
Note: There are reevaluation techniques involved even after implementation to keep our model up to date.
How do you do all this?
Data science frameworks and tools are used specifically for this process. Some popular tools like jupyter, board, tensor flow. Programming languages like Python and R are important to accomplish these tasks. Knowing and learning any language is enough. Python and R are widely used for data science because there are additional libraries that facilitate any data science project. I prefer Python because it is open source, easy to learn and has great support from the community around the world. Statistics, mathematics and linear algebra are some basic subjects that you should understand before getting involved in any data science or machine learning project.
Conclution: Data science and machine learning rule the digital world because artificial intelligence is the next big thing. There have also been advances in this field. Deep learning is also part of artificial intelligence and a subset of machine learning is becoming more popular. Deep learning makes use of neural networks similar to the functioning of neurons in our brain. Has a deeper, layered approach to solving business problems. For instance, like autonomous Tesla cars, they also use deep learning and machine learning.
In the future, these data sources will continue to expand and it will be necessary to collect them all. An important part or information to obtain from this data will only lead to the need for data scientists and machine learning engineers.
