Introduction To Data Science
Data Science is one of the fastest-growing, challenging and high paying jobs of this decade. So, the question is what is data science? data science is an interdisciplinary field (it consists of more than one branch of study) that uses statistics, computer science and machine learning algorithms to gain insights from both structured and unstructured data. According to ‘Economic Times’ India has seen more than 400 percent rise in demand for data science professionals across varied industry sectors at a time when the supply of such talent witness slow growth.
Main Components of Data Science
The main components or process followed in the Introduction to Data Science is as follows:
1. Data Exploration
It is the most important step as this step consumes the most amount of time. Around 70 percent of the time is spent on data exploration. The main ingredient for data science is data so when we get data, it is seldom that data is in a correct structured form. There is a lot of noise present in the data. Noise here means a lot of unwanted data that is not required. So what we do in this step? This step involves sampling and transformation of data in which we check the observations (rows) and features (columns) and remove the noise by using statistical methods. This step is also used to check the relationship among various features(columns) in the data set, by the relationship we mean whether the features(columns) are dependent on each other or independent of each other, whether there are missing values in the data or not. So basically the data is transformed and readied for further use. Hence this is one of the most time-consuming steps.
So, by now our data is prepared and ready to go. This is the second step where we actually use Machine Learning algorithms. Here we actually fit the data into the model. The selection of a model depends on the type of data we have and the business requirement. For example, the model selection for recommending an article to a customer will be different than the model required for predicting the number of articles that will be sold on a particular day. Once the model is decided, we fit the data into the model.
3. Testing the Model
It is the next step and very important with respect to the performance of the model. The model is tested with test data to check the accuracy and other characteristics of the model and make the required changes in the model to get the desired result. In case we do not get the desired accuracy we can again go to step 2(modeling) select a different model and then repeat the same step 3 and choose the model which gives the best result as per the business requirement.
4. Deploying Models
Once we get the desired result by proper testing as per the business requirements, we finalize the model which gives us the best result as per testing results and deploy the model in the production environment.
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Characteristics of Data Science
The characteristics of a data scientist are as follows:
1. Business Understanding
It is the most important characteristic as unless you understand the business you cannot make a good model even if you have good knowledge of machine learning algorithms or statistical skills. A data Scientist needs to understand the business requirement and develop analytics according to it. So, domain knowledge of the business also becomes important or helpful.
Although the math involved is proven and foundational but a data scientist needs to pick the right model with the right accuracy. As all models will not give up exact same results. So a data scientist needs to feel when a model is ready for production deployment. They also need the intuition to know at what point the production model is stale and needs refactoring to respond to changing business environment.
Data Science is not a new field. It has been there before also but the progress being made in this field is very fast and new methods to solve familiar problems are being developed constantly so, as a data scientist curiosity to learn emerging technologies becomes very important.
Here in the introduction to data science, we have cleared about the applications of data science that it is huge. It’s required in every field. Here are examples of a few sectors where data science can be used or being used actively.
There is a huge scope in marketing, for example, Improved Pricing strategy Companies like Uber, e-commerce companies can use data science-driven pricing which allows them to increase their profits.
Using wearable data to prevent and monitor health problems. The data generated from the body can be used in healthcare to prevent future emergencies.
3. Banking and Finance
As we discussed the introduction to data science now we will go ahead with the application of data science uses in the banking sector for fraud detection which can be helpful in reducing the Non-Performing Assets of banks.
4. Government Policies
The Government can use data science to prepare better policies to cater better to the needs of the people and what they want using the data they can get by conducting surveys and others from other official sources.
Advantages and Disadvantages of Data Science
After going through all the components, characteristics and the wide Introduction to Data Science, we are going to explore the advantages and disadvantages of Data Science:
In this topic of Introduction To Data Science, we also show you the advantages of Data Science. Some of them are as follows:
- It helps us to get insights from the historical data with its powerful tools.
- It helps to optimize the business, hire the right persons and generate more revenue as using data science helps you to make better future decisions for the business.
- Companies can develop and market their products better as they can better select their target customers.
- Introduction to Data Science also helps consumers search for better goods, especially in e-commerce sites based on the data-driven recommendation system.
As we studied about the introduction to data science now we are going ahead with the disadvantages of data science:
The disadvantages are generally when data science is used for customer profiling and infringement of customer privacy, as their information, such as transactions, purchases, and subscriptions, is visible their parent companies. The information obtained using data science can be used against a certain group, individual, country or community.
This has been a guide to Introduction to Data Science. Here we have discussed the introduction to data Science with the main components and characteristics of introduction to data science. You may also look at the following articles: