Steps You Can Follow To Learn Data Science4.8 out of 5 based on 7468 votes
Last updated on 20th Aug 2022 5824K Views
Data Science is a collection of tools, algorithms, and machine learning methods. You can use this technology to uncover hidden patterns in raw data.
As the world is stepping towards the era of big data, the requirement for data storage has immensely increased. This issue was at its peak until 2010. At that time, the primary focus was to develop a framework and storage solutions. Now that Hadoop and other frameworks have successfully handled the storage challenge, the attention has shifted toward data processing. This is where Data Science plays its crucial part.
Thus, due to the growing technology, the world has reached the point where the future is Data Science. Therefore, Data Science Training in Gurgaon enables aspiring candidates to enter this ever-growing technology and helps them to advance their career goals.
Introduction to Data Science
Data Science is a collection of tools, algorithms, and machine learning methods. You can use this technology to uncover hidden patterns in raw data. Moreover, it is the domain of vast amounts of data using current tools and methodologies to discover unseen patterns, extract valuable information, and make business choices. Thus, in order to create predictive models, data scientists employ complicated machine learning algorithms.
Steps to Learn Data Science From Scratch
If you wish to make a career in the data field, you can follow the stepwise process to learn Data Science from scratch. These steps are as follows:
● Create a Strong Foundation in Statistics and Math
Like any other science subject, mathematics is a stepping stone to work in the data science domain. This subject provides you with a solid theoretical base.
Also, Statistics and probability provide you with the basic concepts to understand while working in data science. However, the majority of the algorithms and models developed by data scientists are just programmed versions of statistical problem-solving methodologies.
● Familiar yourself with R & Python
Once you're comfortable with the mathematical principles, it's significant to master programming abilities. This way, you can transform your mathematics proficiency into scalable computer programs. As a result, Python and R are the most often used programming languages in data science, so both of these languages are a fantastic place to start.
However, Python and R are excellent starting points for several reasons. As they are open-source and free, anyone may learn to operate in these languages. Also, Linux, Windows, and macOS support both languages. Most importantly, these languages are user-friendly with simple syntax and libraries.
● Understand Databases
Data Science Professionals must be familiar with databases to obtain and store the data they are currently working on.
As a result, one of the most prominent database query languages is Structured Query Language (SQL). It allows you to add new data, alter existing entries, and build tables and views.
● Get familiar with Data Analysis Methods
It's easy to analyze a dataset using several ways. The technique you are using here depends on the problem you're trying to answer and the type of data you are utilizing. Thus, your responsibility as a professional is to have the vision to determine which strategy will work best for a specific situation.
However, several data analysis approaches you may come across in your business. Such techniques include Cluster analysis, regression, time series analysis, and cohort analysis.
● Learn, Love, Practice, Repeat
After learning the above data analysis methodologies, it's time to work on beginner projects. Keep in mind that having an excellent practical knowledge of what you've learned so far is more essential than having a theoretical mastery of a wide range of topics. Also, make sure to practice what you've learned to ensure that you understand it thoroughly.
● Get yourself familiar with Data Science Tools
Data science tools make the job easier. For instance, Apache Spark helps to perform batch processing operations, while D3.js provides data visualizations for browsers.
However, at this point of proficiency, you don't need to master any tool. You can do this after you start working and know what tools your organization demands. At this stage, it's enough to choose one tool that appeals to you more and starts experimenting with it. The objective here is to have a fair knowledge of the data science tools and what you can do with them.
If you have a specific firm in your mind, you may look at their job descriptions to get an understanding of the requirement of most of the companies. They'll generally cite Hadoop and Tensor Flow as tools. In such a way, you may start working on it if you wish to work for that particular company.
● Work on Data Science Projects
After getting efficient in the above steps, it's time to start working on your data science projects. This way, you can create your project portfolio and become efficient in this domain.
Hopefully, you find this article informative. We have compiled the stepwise process to learn Data Science technology from scratch. As a result, Data Science Training in Delhi provides you with efficient knowledge of this particular domain and helps you to kickstart your career with a high-paying job.
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