How to Learn Data Science from Scratch?

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Data Science, as the name implies, is all about studying and analyzing datasets to draw meaningful conclusions. Today, data is generated in terabytes daily across the internet by users as well as companies, and this has proven to be a major resource for firms to capitalize on. The numbers don’t lie, and it is the job of Data Scientists to figure out their truth. It is the hottest job in the market and requires great skill and mindset to pull off. Learning Data Science basics is the start of a career path in this field. 

Any kind of data generated from an ecosystem can point to the trends, tendencies and the flow of whatever market or field the study is conducted on and can help form useful conclusions. From scientific research to the stock market, the field of Data Science encapsulates all areas that generate any kind of information. And in the age of information, data itself is a resource as well as a commodity.

Learning Data Science from scratch

To learn advanced methods of Data Science, it is important to understand how data and its analytics work. There needs to be a strong foundation and the perfect mindset for this to happen, as outlined by the following steps.

The Right Mindset

Working with anything requires passion to be successful. In the case of Data Science, it is important to learn and love the various nuances of how data works, from collection and cleaning to statistics, analytics, and drawing conclusions from numbers and graphs. Especially when working with large datasets regularly, not having the passion for it can prove to be tedious and mind-numbing. It is crucial to be motivated to learn and understand data, to foster the right mindset to be successful.

Technical Knowledge

Once you have established the proper headspace to learn Data Science, the next step is to master the tools required for the field. This includes an array of subjects, mainly coding and Mathematics. There are many tools that you can add to your repertoire, but Data Science basics consist of learning to code and learning to analyze data and statistics.

Learning to code

It is no coincidence that the largest and most successful data scientists in the field have an IT background. Coding is an integral part of being a Data Scientist and a useful skill in the field. The most popular language for Data Scientists is Python, and this is due to the many benefits that it offers in learning as well as coding.


Python is one of the easiest languages to learn. Many beginners opt for this language to add to their resume, and it is incredibly simple even for people without much experience in IT. Many online resources offer Python lessons and courses for free, and this also attracts aspirants to this language.

As a versatile tool, Python is popular in many fields. Any type of job that requires coding can be done using it, as it is versatile, yet has specialized tools for various fields and applications. Hence, it is important to master the basics of Python and coding in general first. As you learn, you will also be able to encounter many tools, libraries, and popular methods to apply Python for Data Analytics. This comes at the later stages of your journey.

Understanding Mathematics

In Data Science, data predominantly consists of Numbers and Statistics. Therefore, Mathematics being an essential skill does not come as a surprise. Any aspiring Data Scientist should have a basic understanding of Mathematics, especially data related fields like Statistics. At the bare minimum, one must be able to understand data distributions, algorithms and concepts like regression and classification. This will allow him/her to look at the resulting graphs and data to draw conclusions, analyze trends, spot outliers, and so on. An understanding of the Mathematical basis of how data works is vital to the success of any Data Scientist.

Machine Learning

Machine Learning (ML) is an essential tool for any Data Scientist for one major reason, and it is the volume of data that one has to work with. Datasets come in much larger chunks and are impossible to analyze manually. All the virtual tools and algorithms that one uses for Data Analytics must be understood by the user. A Data Scientist is required to build models based on algorithms and previous models and then fit them to the required application. For this purpose, it is important to understand the concepts of ML and complete the work accurately and efficiently.

Due to this, ML is a basic tool for Data Science despite being a high-level application of coding and Neural Networks. You can explore many free and paid courses online to improve your understanding of the concept and add this tool to your arsenal.

Practice makes Perfect

For any course, it is important to practice regularly to master your tools to the highest levels. For Data Science, you can tackle many projects online. These projects allow you to work on tasks of any difficulty and also with your peers. Learning by practice and learning from peers are two very important steps in any field. Sites such as GitHub offer thousands of collaborative projects that people at any level of expertise can try out. You can also go through previous projects for learning purposes, and you will have an infinite and regularly updated resource to learn from.

Practicing your skills by working on projects and slowly increasing difficulty levels can help you polish your skills and test them out in the field.

For data scientists, the end goal is to use the collected datasets to draw meaningful conclusions to help the business or the project that they are working on. For this, it is essential to not only be able to analyze data but convey these results to people who might not be well versed in your job. Communication skills play a huge role in this as well as the knowledge of Mathematics that will help you to read, interpret, and translate data into easier terms.

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