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How To Learn Data Science From Scratch? A Complete Guide

Data science is one thing that is dominating the world right now. Day by day, companies around the globe are exploring data science as the ultimate solution to almost all kinds of problems. This situation has placed data scientists on favorable grounds in terms of the job market and remunerations. Of course, a growing number of people would like to know how they can become Data Scientists. In this article, we will present all you want to know about how to learn data science from scratch and become a successful data scientist.

How to learn data science from scratch

What is Data Science?

Data is a tool of significant importance in different sectors of the professional life. In this day and age, data is all but valued in nature as organizations from various fields apply data science to gather information in performing business decisions.

Being crucial to many organizations, it is now more needed than ever due to the reliance of companies on information.

Data science is therefore defined as the process through which one is capable of finding useful patterns in large sets of collected data through the application and utilization of tools, algorithms, and scientific procedures.

Data scientists are valuable to businesses since they identify organizational data, process it, and make it logically presentable and easily digestible so that organizations can make the best use of it.

They assist the organizations to be ready to compete in the existing universe which is characterized by rapid technology and data.

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Importance of Data Science

Before we move ahead to understand how to learn data science from scratch, we should know the importance of it in today’s times. As the world becomes more of technology and analytics, almost every industry utilizes data in one way or the other.

By becoming a data scientist, you will be able to assist organizations in grasping a broad spectrum of data from various sources and acquire useful insights to facilitate proper business decisions within their companies.

Some of the industries that use data science include:

  • Marketing
  • Healthcare
  • Defense and Security
  • Natural Sciences
  • Engineering
  • Finance
  • Insurance
  • Political Policy

These are just some of the examples of industries that leverage data, however, data science proves its worth in a variety of other fields and companies. Data science has a number of benefits for many industries these include:

  • Data science can help businesses to know their customers so that they can create a positive experience in marketing or by producing relevant products.
  • It assists organizations in making out patterns in operation that are critical in informing areas of strength and potential improvement.
  • Through data usage, organizations will be in a better position to meet their customers’ needs and wants hence surpassing other organizations that they are competing against.
  • Analytics provide accurate and factual information that can be used to design new programs and also evaluate their outcomes.
  • The role of a data scientist is rather versatile, and there are numerous purposes that businesses need them for. By studying data science from scratch, you learn how to assist organizations with the process of collecting, segregating, and organizing data. Data scientists will also be expected to present information in simple and easily understandable formats to others at the company. This will assist in making the analytical data available and understandable to the various stakeholders in an organization, make them aware of the existing trends, and collect the necessary information for the organization to compile and devise sound business strategies that will enable the organization to satisfy the needs of its customers.
  • As a data scientist, you are also expected to contribute to the data security of the company that you are working for.

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Steps to Learn Data Science from Scratch

To become a data scientist, you will have to study several concepts in data science, programming languages, and tools based on machine learning from a beginner to an advanced level. The steps to learn data science from scratch are listed below.

  1. Strengthen your foundations in Statistics and Math
  2. Learn Python and R Programming Languages
  3. Understand Databases
  4. Learn Analysis Methods
  5. Keep practicing
  6. Learn to Use the Data Science Tools
  7. Work on Data Science Projects
  8. Become a Data Storyteller
  9. Network
  10. Continue Learning and Stay Updated

Let us look into the different steps to learn data science from scratch in more detail.

Strengthen Your Foundations in Statistics and Math

Math as a discipline is a prerequisite to working in data science, similar to many other science fields; it will provide you with a solid theoretical background in it. In data science, two other broad disciplines that are of paramount importance include statistics and probability.

In fact, most of the algorithms and models that data scientists use in their tasks are simply statistical problem-solving strategies put into programs.

If you are new to statistics and probability, you can take a basic course to familiarize yourself with the concepts. One should use this to be introduced to concepts such as variance, correlation, conditional probabilities, as well as Bayes’ theorem.

This will place you at an advantage in grasping how those concepts translate to the work that you will be doing as a data scientist.

The journey to becoming a data scientist entails familiarizing yourself with data wrangling, getting comfortable with organizing data, acquiring foundational techniques such as predictive modeling, learning this popular programming language, recognizing which tools and datasets you will encounter, and gaining an overview of information extraction where insight is derived from data, and finally, applying yourself toward final projects in data analytics.

It is also crucially important to have good communication skills along with a strong technical background in the field. The potential employers are more likely to give value to the necessary skills than everything else. This is the first step in learning data science from scratch.

Learn Python and R Programming Languages

Once the mathematical knowledge required for a data scientist is completed, the next steps involve learning the programming languages and skills that can be used to translate the math knowledge into scalable computer programs. Python and R are two of the most popular languages used for Data Science today, so it is a good place to start for anyone who wants to become a Data Scientist.

Python and R scripts are ideal as a starting point for several reasons. Both of them are open-source languages and free, thus enabling anyone to learn how to program in a specific language. As a data scientist, you are allowed to program in both languages across Linux, Windows, and macOS. More importantly, most of these languages are easy to learn since they have simple syntactical structures and libraries.

Together, Python and R can be used for almost any data science task out there; however, each is preferred in certain contexts. Python is better when you are dealing with a large amount of data.

It has been stated that it is better than R in deep learning, web scraping, and doing jobs in work streamlined. Some of these skills you’ll have to learn when making the transition to data scientist.

R is the language that should be used for translating statistical measures into computer models. This feature includes a variety of statistical packages you can use in the analysis of datasets with ease. As such, data compilation is slightly easier in R than building statistical models as seen in Python.

Finally, it can be concluded that the choice between Python and R depends on the intended career path. Thus, it is Python that turns out to be better to start with if you want to work in such subdivisions of data science as deep learning and artificial intelligence.

Start with R if your focus or interest is more toward statistical computation and model construction. You may also want to use your knowledge to develop your own first data science project – it will be a bonus if you are planning to become a data scientist.

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Understand Databases

The next prerequisite to learning data science from scratch is the knowledge of databases. This helps data scientists to access the data they are going to use and store it once they are done with it.  A common database query language is called Structural Query Language abbreviated as SQL. It enables the insertion of new data, alteration of records, and creation of new tables and views.

Advanced tools such as Hadoop also have other interfaces and methods of query that support SQL, which is an added advantage. Many people think that they must understand database technologies when becoming a data scientist.

That should be left to the database administrators. The good news for an aspiring data scientist is that you just have to know how relational databases function and what particular commands to use in order to get and store information.

Learn Analysis Methods

Depending on the kind of data collected or available, there are several approaches that data scientists can employ for data analysis. The specific method that you use is determined by the kind of problem you want to solve as well as the type of data that you are analyzing. If you’re a data scientist, your task is precisely to have the hindsight to understand which approach is going to help you address a particular problem.

It is important to understand some of the usual analysis techniques employed in the industry. Thus, some of the techniques that can be used include cluster analysis, regression analysis, time series analysis, and cohort analysis.  A data scientist does not need to be familiar with all methods of data analysis available but should be aware of the common ones.

It does not matter which type of approach that you are using, it is essential that you comprehend the applications of any specific approach. As in any profession, the best data analysts are the ones who are able to match the problem with the correct data analysis technique.

Keep Practicing

Once you’ve gone through the process and learned about how to perform data analysis and all the various approaches, you can then start doing the basic projects.  But do not forget, as a data scientist, it is more important to have a deeper understanding of everything that has been explained so far than to have a shallow understanding of a great many things.

Do not merely read what you have learned but try to practice it in order to master it. Thus the key step to learning data science from scratch is to keep practicing.

For instance, this could be concepts such as the weighted mean that one is acquiring knowledge in. Do not simply memorize the definition of the concept. It will be helpful to attempt to create a program in Python that estimates the weighted average of a given data set. This way of learning assists you in improving your grasp of the concepts you learn to enable you to apply what you learn.

Learn to Use the Data Science Tools

What used to be time-consuming and tedious is now made easier by the application of data tools. For instance, Apache Spark organizes the processing of batch computations and jobs and D3. JS helps generate plots of data for use in browsers. There are a number of other tools that are also widely employed in the field of data science.

At this stage of learning data science from scratch, you don’t need to become an expert in all the tools and try using all of them at once. You can do that once you are employed and understand what that particular company needs from their employees in terms of tools. Here, it suffices to choose one that you find fascinating and start experimenting with it.

It is therefore not so important to get lots of details about them but rather to get a general picture of what’s available and what can be done with it. If you have a special company that you would like to work for, then you can be able to look at the current job openings. They will often refer to tools such as Hadoop and Tensor Flow. It would be very useful for you if you could orientate with those tools in case you want to work at that certain organization.

Work on Data Science Projects

From this point in learning data science from scratch, it remains to combine everything and start constructing personal projects. Before jumping into how these projects may look, let’s examine two examples of what they might entail.

Sentiment Analysis- The analysis of sentiment in a particular text is the process whereby the sentiments in the text are determined. It might be helpful to work with a binary approach, which means to decide whether a text has a positive or negative sentiment or to name texts according to their degree of happiness, excitement curiosity, and so on. One resource that is likely to contain such data for analysis is the social media feeds, from where you could undertake a hashtag analysis for your sentiment analysis project.

Recommendation System- Suppose, for example, you were developing a movie recommender system. The datasets that MovieLens offers are in this place. You can then base the recommendation of movies on some factors such as genre, actors involved, and length of the movie among others.

These are just two examples of a data science project that can be considered. But the aim is to apply all the knowledge you have learnt into practical terms to gain a deeper and more enriched understanding of data science.

Become a Data Storyteller

The next step to learning data science from scratch is to become a data storyteller. It is important to package knowledge in such a manner that end users can easily comprehend. This is where storytelling steps into taking over this task. There are three main aspects of the data storytelling:

Data- The information that you are going to gather in the course of your analytical process is the object that you will be building your story around.

Narrative- A narrative is an intended story and background that a person wishes to convey to the audience.

Visualizations- These are graphical representations of data. It is also advisable to illustrate the narrative with the help of graphs, charts, videos, diagrams, etc.

Network

If you’re ready to start the job search, it is also helpful to connect with data scientists and other professionals in the field, in addition to working on personal projects and honing your resume. Networking is a powerful tool when you are just starting your way in the field of data science and in need of help in some way.

If you have a chance to have a conversation with several data scientists they might give you an insight into what the current situation in the field is. It may be helpful to talk to recruiters and find out some of the things they look for in candidates as well as how they conduct the interviews with the intention of gaining employment.

You can also learn a lot by discussing matters with individuals who are close to different areas and who will tell you how they are using data to arrive at certain conclusions. For all the reasons described above, networking remains critical when starting a career as a data scientist.

Continue Learning and Stay Updated

It is important to remember that the process continues even after you have established yourself with your initial few projects or a job. You have to remember that data science is a field that is constantly changing and developing, and so should you.

It is especially important for you to stay updated on the advancements in the field. This is a problem because if you don’t know what’s changing, then you also won’t know what you need to make yourself aware of. Some of the strategies include following other experts within the specific area and reading newsletters. There are a number of certifications that can help one upgrade themselves and become an even more proficient data scientist.

Frequently Asked Questions

Q.1) What is the Time Limit to Learn Data Science From Scratch?

It depends on how you set your study routine but it is advisable that it should take you at least six months to consider yourself as a beginner data scientist. This will provide you with a chance to acquire the skills and practice them in the form of projects.

Q.2) Who is Eligible to Work in Data Science?

There are no restrictions for anyone who wants to practice data science. One can get a job in this field without having a college education at all. In other words, as long as you have the right theoretical background and projects to present to the recruiters, anyone can find a job in the sector.

Q.3) Can Data Science Be Learned Easily?

Learning data science from scratch is not difficult provided that one follows the correct study methods and sources. Consider the way you study and where you look for materials and try to find something that suits you. For instance, some students may decide to learn independently from videos, while others have the best experience with mentorship from boot camps.

Q.4) What Is Big Data?

Big data is a broad term that refers to the vast amount of data being produced and processed through current trends and innovations. These are large datasets that cannot be processed by conventional data processing platforms or software. However, these large amounts of big data can be employed to solve business issues that you would not have been able to deal with prior.

Conclusion

Data Science is one of the most diversified fields in the present-day world. It offers the best solutions for managing the issues associated with rising demand and a sustainable future. Thus, the need for a data scientist is growing also, as well as the role of data science in the business world. This means that a data scientist has to be in a position to bring the best solutions that can solve the problems existing in all sectors. Thus, learning data science from scratch would benefit professionals aspiring for higher positions in their chosen career paths.

I’m Bhavni. I have completed my Masters in Clinical Psychology and am an enthusiast of everything related to the human brain. I hope to disseminate information about mental health, and well-being and help destigmatize it through my writings. I find my solace amongst the pages of a book and the lines of my writing. Books help me expand my creative thoughts and writing helps me articulate those thoughts. My ambition is to write stories and craft narratives that become a source of inspiration for others.

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