As we are moving towards a digital world, we are already seeing the need for storing big data. Similarly, processing such big data needs the use of scientific methods, algorithms, and tools. Data science is the thing that can help us out through this.
Data science is gaining much importance in the present time. So, it is necessary to get an in-depth knowledge of data science. If you are a newbie in this field, this article is for you. This article is a beginner’s guide to data science.
A Beginner’s Guide to Data Science
Data science is mainly associated with scientific methods, algorithms, and systems and is used to extract knowledge from big data set. Earlier analyzing of big data was mainly carried out by the math or statistic specialist but time has changed now.
Life Cycle of Data Science Projects
There are some phases in the life cycle of science. These are as follows:
1. Understanding topic
Before starting the project, it is necessary to understand the problem and analyze the problem to get a solution. Here, you need to go through the central objective of the project. It will help you to understand the priorities, various specifications, and the required budget for the project.
2. Acquisition of Data
Once you have set the objective of your project, the second step is the acquisition of data. You may not find the complete set of data in one place. So, it is often necessary to retrieve data from various sources like web servers, online repositories, and databases.
Also Read: 8 Best Online Courses for Data Science
3. Preparation of Data
After the acquisition of the data, the next step is the processing of data. This phase involves the cleaning of data. It is necessary to clean data before starting the analysis. It is the most time-consuming part of all the phases.
4. Exploring data
Once you get clean data, you are ready to analyze the data. Data exploration is also known as data mining. This phase is really helpful for understanding the patterns in the data as well as getting important insights from the data.
5. Modeling and Evaluation
Here you try a different set of combinations with your data. This process is mainly associated with the use of statistics and machine learning.
Once you have done with the modeling, it is necessary to evaluate the success of the model. Evaluation means getting insights into the achievement of the project.
It is the final phase. Once, you are done with modeling and evaluating, you are ready to deploy the project. It is mainly associated with the deployment of the models into production.
Skills Needed to Become a Data Scientist
If you want to become a data scientist, you should go for a Bachelor’s degree in computer science, or mathematics, and statistics. These degree courses will give you adequate knowledge for processing and analyzing big data.
Most data scientists have a master’s degree or Ph.D. So you can either enroll in the courses or can undertake online training courses. Data science is associated with processes like R, Python, Apache Spark, Hadoop Platform, SQL/ database, machine learning, AI, and data visualization.
Application of Data science
It is quite tough to list down each use of data science. Everything we are using mostly thrives on a set of data like in the case of our mobile phone. Data science is necessary for every industry.
1. Banking Sector
Since banks deal with a large array of data, data science is an important tool that is playing a significant role in this case. Data science is widely used especially for risk modeling, fraud detection, customer segmentation, and real-time predictive analysis.
2. Health sector
The health sector is another field that deals with a large set of data. So, data science is proving to be a useful tool in this field. It helps to keep an electrical health record of the patients as well as helps the doctors to make data-driven decisions for better treatment.
This is especially useful when the patient is suffering from complex medical histories.
Data science is making a mark in the transport world. It is useful for optimizing vehicle performance and making a safe driving environment for drivers. Various transport companies like Uber is using data science for better user experience.
4. Game industry
The gaming industry is rising at a high speed with millions of players all over the world. Thus, data science is playing a significant role in game development.
The design, functionality of the game is very important in keeping the players engaged. So, the insights obtained from the analysis of gaming data is very important.
Data science is playing a significant role in the e-commerce and retail industry. It is useful to predict losses, purchases, profits. It also helps to understand the interest and liking of customers by capturing the web behavior of the customers.
This ultimately helps the e-commerce sector to personalize product recommendations and push the customers towards purchasing. Thus, it helps in improving the customer experience.
So, it is clear that data science is the future. As the amount of data is increasing day by day, the need for data scientists is also increasing. It is such an emerging field that every company more or less related to data science. So, the scope of a data scientist is high. Here we have presented a beginner’s guide to data science. Hope you have liked the article and found it useful.