When you are thinking of going for a new career then the name of data science must have popped up. There is an inclination towards this field for those who are technically adept. But the question here is does data science require coding? Well, if you are looking for an answer to this question then you have come to the right page here, we are going to understand whether is coding really needed if you are thinking to have a career in data science. Also, not only we are going to ponder over that question, but we are also going to learn various skills that are needed to have a good career in this field. So, by the end of this page, you are going to have clarity of what are the requirements of this career and how you can hone those skills to have a good career in data science. Also with Data Science Certification, you will be able to understand all the processes in data science.
Before going into do data scientists code, the main question is what is data science and what is needed to become a data scientist? Data science is the domain of study in which there is a large volume of data. That data is being studied with the help of modern tools and technologies that help in unearthing various unseen trends, meaning derivation, and also use them to make important business decisions. There are various complex machine learning algorithms that are being used when these trends are being found in that volume of data. The data analysis is done in various forms and there are various formats that are used to derive results from that dataset. So, we can understand here that coding is going to play a major role in coming up with those trends using artificial intelligence and machine learning.
Coding and data science are closely related but the extent of the use of coding depends on the tasks which are assigned to the data scientist. Sometimes, they do not have to use complex algorithms to come up with the trends but help the business to make certain decisions. But overall, a career in coding in data science seems unfair.
Coding plays a significant role in data science and data science requires coding. But where coding is needed when working as a data scientist, let us find out below:
This is the phase where the data scientists are going to understand what kind of data is given to them and what are the objectives for that particular set. This is the planning phase in which they are going to understand what kind of tools and software they are going to implement throughout the process. Although no real coding is required for this but having a knowledge of what is going to work best for what kinds of datasets will come from experience in coding. They are needed to be focused and they should not let the white noise, or the unrelated data distract them.
We are familiar with the fact that there is so much that is being produced nowadays and to handle that data some skills are needed and one of them is coding. Not only collecting that data needed in the field of data science but also keeping the quality of that data set is needed. There are various possibilities like having missing data, uninterpreted data, inconsistent data, and much more. It will become difficult for the data scientist to cope with trends if they are not able to find datasets with good quality. So, they will have to use various querying languages which will include the use of SQL and NoSQL.
As mentioned above obtaining the dataset that is needed to find the insights is not enough. The data scientists will need to come up with solutions that will help to clean that data. There could be various discrepancies with the data but with the use of the right tools and software, they will be able to come up with a quality data set. There could be labelling errors, minor spelling mistakes along with miniature errors that could result in the tempering of the data set. So, with the use of coding languages like python and R, the team will be able to clean the data. With other software, the team is also able to change the format and use it as per their requirements.
Now that data has been collected and cleaned, it is time for the major task- analyzing that data. The main job here is to use the data set to come up with the solution to the problem at hand. Using various languages like python, R and MATLAB, the data analytics are able to come up with the trends. There are many types of coding languages that are coming to the surface, but python is ubiquitous. With various tools, the team is able to streamline the data and analyze the results. Thus, data science needs coding.
Using Machine learning libraries
There is one more reason why coding is needed in data science. When you are going to work in python or R, you will need to use machine learning libraries that will help you create powerful algorithms to come up with various solutions. But with the knowledge of coding, this task would become impossible. Without at least some knowledge of these programming languages, it would be hard for the data scientist to find a replacement for such a powerful library in machine learning. Therefore, to use these libraries, the use of coding is pivotal.
The job of a data scientist is to come up with a solution that is then implemented by the organization. These insights can be very useful for the business. Until the data scientist is not able to visualize the results to the team and come up with an understanding of what they have found, analysis is not useful. There is software like Excel and Tableau are needed to create graphics and get an understanding of the results. Of course, Python is always there to help the data scientist to make visuals.
When you are going to have a career in data science there are many coding languages that you need to get hold of. So below are some of the top programming languages that will help you have a better career in this field.
There is no doubt that when you are talking about skills in data science, an understanding of python is going to be of the top skills required. And it is not going anywhere for at least the next five to ten years. If you have an upper hand in python which is combined with a strong aptitude for quantitative understanding and experimental analysis, you are going to strike gold in this field. Python provides flexibility as your toolset and not only you can come up with the solution with this language, but it is also a great tool to visualize your results. Some services that come in handy with python are:
If there is any other language that is most important after python in data science, it is SQL. It is the language that is used to interact with relational databases. When the data is needed, the data scientist will query using this language. This will help them to gain access to those datasets as well as perform data mining. Moreover, when the structure dataset is needed in the team, the use of this language comes in handy. There are various needs to learn this language like aggregating data, calculating averages, and determining the maximum and minimum in the given data set. Also, it is a very useful tool for extracting data.
There are many programming languages that were used in data science, but R has surpassed everything and has become one of the most prominent languages in data science. If you are looking for design models that will help in building the statistical models, then you should have a good hold of R. The package archive which is public has contributed packages from almost 8000 networks. This can be used by the statistical for performing regression. Not only this, but with the use of R, they are able to come up with various charts that can support their analysis. R is known as the suitable langue that helps in creating research papers and reports.
For at least three decades, Java is one of the favourite languages that is used for desktop, web, and mobile development. This is one of the highly sophisticated languages and runs on the back of JVM which is short for Java Virtual Machine. As this language provides a great deal of scalability in this domain, the enterprise has always preferred this language over many other modern languages that have come recently. If you are developing a project in java, then there is a possibility of scaling that problem and creating a large-scale machine learning system that can use in the field of data science. With the use of this language, the developers and data scientists are able to perform data mining, come up with machine learning algorithms and create as well as train neural networks.
Data Science is one of the aspiring fields nowadays and but without the knowledge of coding, it would be hard on you to set foot here. Here we learned is coding required for data science. Coding will form the base and with the help of various programming languages only, you will be able to come up with hidden insights and have a good career in this field. There are various job titles associated with this field and you should them out and understand what profiles fit you the best.
Furthermore, if you are looking to learn more about data science, then it is best that you choose a good platform where you can get an understanding of all the concepts of data science along with hands-on experience with various programming languages in the Data Science Course. With StarAgile, you can get the best of the trainers, who are experts in these languages and in the field of data science. With StarAgile, you can make sure that you are well-equipped to enter this industry. So do not waste your time anymore and start learning.
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