A Quick Guide to Data science and Machine Learning
This article was published as a part of the Data Science Blogathon.
In today’s time, an enormous amount of data is created in one click. This data is valuable for any organization and company. In this digital era, we are always connected to the internet. And this leads to a massive amount 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 for every organization, and hence actually I believe that it is the ruler? Without data, nothing can be achieved. From a business perspective to solving problems for end-to-end applications we require data.
This data needs to be in order to derive some purpose from it. Because forms of data can be texts, images, videos, infographics, gifs, etc. Some data are structured while most of them are unstructured. Collection, analysis, and prediction are the necessary steps that are to take into consideration with this data.
Now, what exactly are Data Science and Machine learning?
I’ll just define it for you in a simple way. All the context related to this can be similar if you search somewhere else. So data science is the science of deriving insights from data for the purpose of gaining the most important and relevant source of information. And with a reliable source of information making predictions by the use of machine learning. So I guess you might have very well understood this definition. Now my point here is that with data science you can bring meaningful insights.
Why there is a need for data science and machine learning?
Data has been there for a very long time. During earlier times analysis of data was done by statisticians and analysts. Analysis of data was done primarily to get the summary and what were the causes. Mathematics was also the core subject of interest when used for this work.
It was not a cumbersome process because there was a limited amount of data. Business problems were primarily solved also by the use of software tools like Microsoft excel. This tool is also used for the analysis of data. Here when I say business problems those are specifically in digital format. As companies started becoming digital, the internet and cloud computing became the backbone of their establishment. There was a huge amount of data generation in millions of bytes Which is usually referred to as big data. With the advent of social media, powerful search engines like Google and YouTube, it became mandatory for these companies to handle their data carefully.
How data science and machine learning solutions?
Data science uses statistical methods, maths, and programming techniques to solve these problems. The programming techniques are extensively used for analysis, visualizing, and making predictions. So you see it does all the work of a statistician, programmer, and maths. The study of all these major areas makes the best way of dealing with such big data. Machine learning is integrated by making models from various algorithms.
This is done for model building in data science which helps for future predictions. These predictions depend upon the new data which is given to the model without explicitly telling it what to do. The model understands it and then gives us the output or solution. For example, banks use machine learning algorithms to detect if there is a fraud transaction or not. Or if this customer will default in paying his credit card dues.
Cancer detection in the health care industry uses data science and machine learning to detect if patients are prone to cancer or not. So there are a lot of examples around us where companies are widely using this. Online food delivery companies like zomato or swiggy use for recommending us food to order based on what have we 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 various steps involved in solving business problems with data science.
1. Data acquisition – this process involves the collection of data. Depends on are objectives or what is the problem that needs to be solved. By this means, we tend to gather the required data.
2. Data pre-processing – this stage involves processing data in a structured format for ease of use. Unstructured data cannot be used for any analysis because it will give wrong business solutions and can have a bad impact on consumers.
3.Exploratory data analysis (EDA) – it is one of the most important stages where all the summarizations of data by statistics and math’s. Identifying the target(output) variable and predictor(independent) variables. Visualization of data and then sorting all the necessary data that will be used for predictions. Programming plays a vital role in this. A data scientist spends almost 75% of their time on this to understand their data very well. Further in this stage data is divided into training and test data.
4. Model building – After EDA we select the most appropriate methods to build our model. This is done with the use of machine learning algorithms. Selection of algorithms like regression, classification, or clustering. As machine learning algorithms are of 3 types. Supervised learning, unsupervised learning, and reinforcement learning. There are different sets of algorithms for all these types. Selecting them depends mainly on what is a problem are we trying to solve.
5. Evaluation of model – model evaluation is done to see how efficient our model is doing on the test data. Minimizing errors and also tuning of the model.
6. Deployment of model – model deployment is done as now it is fit to cater to all the future data for making predictions.
Note: There are re-evaluation techniques involved even after deployment to keep our model up-to-date.
How all this is done?
Data science tools and frameworks are specifically used for this process. Some popular tools like jupyter, tableau, tensor flow. Programming languages such as Python and R are important to do these tasks. To know and learn any one language is sufficient. Python and R are widely used for data science because there are additional libraries that make it easy for any data science project. I prefer Python as it is open-source, easy to learn, and has huge community support across the world. Statistics, math, and linear algebra are some core subjects you need to understand before getting involved in any data science or machine learning project.
Conclusion: Data science and machine learning are ruling the digital world because artificial intelligence is the next big thing. There has been advancement in this field as well. Deep learning also a 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. It has a more deep, layered approach to solving business problems. For example like Tesla’s self-driving cars use extensively deep learning and machine learning as well.
In the future, these sources of data will keep on expanding and there will be a need to harvest all of these. An important part or information to get from this data will only derive the need for data scientists and machine learning engineers.