Data Science (https://en.wikipedia.org/wiki/Data_science) is a multi-disciplinary field technology that uses scientific methods, algorithms, processes, and systems to extract knowledge and insights from structured and unstructured data. It is a union of statistics, data analysis, machine learning, and related processes. In order to analyze complex problems. And solve these problems to bring out the possible and desired results.
What is Data Science?
I am going to explain this topic using an example. Which will also make it easier to understand for the readers. For example, Uber uses data science to a greater extent. Well, Uber ( https://en.wikipedia.org/wiki/Uber) is a multi-national company that provides transportation services in the cities. Its services include peer-to-peer ridesharing, food delivery, a bicycle sharing system, etc. And the company is completely online. So, it needs to use some technologies to manage its system.
Hence, here it is. For these types of companies, there is always a challenge. The challenges include busy routes, which area include a larger number of passengers, etc. To cope up with these challenges, Uber uses data science technology. It uses data science to find the busiest route in the city. Which enables the system to provide an efficient facility to every area. To the busiest routes also. For this, sometimes it provides rides with inflated prices. But ensures better facility.
Uber also uses data science to find the region with largest passengers in need. And then provides the maximum number of drivers to that area.
Data Science – How it works
We implement data science in different steps. The steps include:
- Business Requirement: This step usually includes the identification of problems. The problem which we need to overcome. This step includes the gathering of information about the problem. And assigning and deciding the most reasonable solution. For example, providing the maximum number of drivers in a particular region.
- Data Collection: This step is also the most important step in technology. In this step, we collect different types of data. Which relate to the problem. For example, the traffic, weather, historical data, time, pickup and drop regions, the fare calculated, etc. Uber needs to keep this data or record to solve the particular problem.
- Data Cleaning: When we collect the data. There is certain possibility that unnecessary data gets collected. So, in order to clean out the unnecessary data. That we unknowingly collect during the data collection step. We perform data cleaning. As these unnecessary data increases the complexity. Eg, the data of restaurants, cafe, libraries, etc.
- Data Exploration and Analysis: It is like a brainstorming of data analysis. In this, we basically try to understand the patterns in the data.
- Data Modelling: This includes building a machine learning model. That predicts the uber search at a given time and location. In this, we train the model by feeding thousands of customer records. So that it can learn to predict precisely.
- Data Validation: When there is a new booking in Uber. The model compares it to historic data. To check if there is any anomalies or false prediction. If it finds, then it sends the problem to the data scientist. And data scientist fixes it.
- Deployment and Optimization: After testing and improving the model efficiencies. Then, we deploy it to the user. At this stage, we collect customer feedback. And then we optimize the model accordingly. And also fixing the issues if found.
Amazon and Flipkart use data science technology. To provide product recommendations to the users and customers. Netflix also uses this technology. To provide a commendation system to the users. A credit card detection system also uses this technology. Self-driving cars widely use this concept. This concept also applies to virtual assistants.