How to Become a Data Scientist in 2023: Step by Step Guide!

If you are interested in pursuing one of the hottest fields in Tech- Data Science, read this article to learn how to become a data scientist and land a top gig.

How to Become a Data Scientist in 2023: Step by Step Guide!
 |  BY ProjectPro

Data Scientist is widely labeled as the sexiest job of the 21st century. And, if you have fallen for its charm and want to know how to become a data scientist in 2022, read this article that lays out a perfect guide for all the beginners out there.


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All of us, during a late-night walk, would have looked at the sky and glanced at the beauty of the Moon. In the year 2022, it is pretty easy to take a snapshot of the moon’s face and affirm that Moon has craters on its surface, but back in the days of Galileo(the 1600s), cameras weren’t available. So, how did Galileo collect data that led to the discovery of the moon's irregular surface? Simple, he drew what he saw on the telescope’s lens.

Comparing the role of a Data Scientist with Astronomers

 

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How to Become a Data Scientist?

Fortunately, in the 21st century, if you want to do Data Science, you don’t need to be an artist to become a successful data scientist. With modern tools and technology, it is easy for people from diverse backgrounds to pursue a career in Data Science. So, if you have decided to grab the sexiest job of this century, Data Scientist, and want to know how to become a data scientist, continue reading this article. All your questions related to becoming a data scientist and securing a data science job: data skills required, data scientist salary, steps to get hired for the data science role, etc., will be answered in detail. 

Is it Worth Becoming a Data Scientist? Benefits of Becoming a Data Scientist.

The simple answer to the question: Is it worth becoming a data scientist? is YES. And to know the detailed answer, read the advantages of becoming a data scientist below:

Skills Required for Becoming a Data Scientist

Data Science Job Salary: The average data scientist salary in the United States as per Indeed.com is  $140994 per year, and as per Glassdoor, it is $1,17,212 per year. These numbers for data scientist salary highlight that it is a well-paying job.

Data Science Career Growth: Every company in today's era is likely to evolve into a tech company. This suggests that such companies will rely on data to increase their profits. Thus, data scientists will likely be in demand in the future for a long time.

Anyone Can Do Data Science: As the requirements of becoming a data scientist are independent of an individual's educational background, it is not difficult for a beginner to pursue a career in data science. The opportunities are abundant for those willing to work hard to learn all the skills. Also, as the data science domain has only recently taken a formal shape, the year 2022 is not too late for any beginner to start learning about data science from scratch.

Data Science is Fun! : The best part about becoming a data scientist is that it is a fun job that integrates the beauty of mathematical science and the excitement of solving business problems. So, if you are interested in a field that involves working at the intersection of science and business, the data science job will be fun.

If the above pointers have motivated you to pursue a career in data science, continue reading to know what skills are required to become a data scientist.

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Skills Required for Becoming a Data Scientist

To become a data scientist, you should first have a fair idea of the technical skills needed. That will help you estimate the time it will take you to become a data scientist.

Benefits of Becoming a Data Scientist.

Linear Algebra and Multivariable Calculus

One of the most basic data science skills is understanding advanced mathematics, usually taught after high school. Linear Algebra and Multivariable calculus are the backbones of most algorithms in data science. Thus, understanding them in depth will help you better understand which algorithms are likely to suit a given problem.

Probability and Statistics

The first and foremost step that data scientists and even data engineers perform to understand data better is to look at the summary statistics. It gives them a fair idea of the data and helps them design an efficient solution for the problem. And, as the probability is fundamentally important to understand statistical analysis, we recommend you get hold of both the subjects thoroughly.

Programming Language

Many popular programming languages in Data Science include Python, R, Scala, etc. You can learn these data science programming languages, but we recommend you pick Python as it is on top of the popularity list per Statisticstimes. Python has amazing libraries to help data scientists implement solutions. You'll understand its significance once you get the hang of it by working on various Python machine learning projects.

Data Files Management

Handling a large number of data files or data management is one of the key responsibilities of a data scientist. And to leverage the files for solving business problems, it is crucial to know how to manage them. One of the popular tools of data file handling for Relational Database Management systems is SQL, while for NoSQL databases, there are options like MongoDB, Cassandra, ElasticSearch, Base, etc.

Data Analytics and Visualization

If Galileo hadn’t analyzed his data correctly, he would not have ended up revealing insightful facts about the universe. Similarly, for a data scientist visualizing data to understand it better is an integral part of the data science project pipeline. Tools like PowerBI, MS Excel, Tableau, Python’s matplotlib library, etc., are best for performing data analysis and visualization tasks.

Machine Learning, Deep Learning, and NLP

Artificial Intelligence projects are usually subdivided into three categories: Machine Learning, Deep Learning, and NLP. Machine Learning is the art of using mathematical models to train a machine to learn the hidden pattern in a given dataset. Deep Learning, like machine learning, also involves implementing mathematical models, with the difference that these models are based on the way neurons in the brain transmit information. And to leverage audio files and textual data, data scientists use special methods called Natural Language Processing techniques. All three categories are critical for becoming a data scientist, but you can initially choose to pursue a bias in your data science career.

MLOps

These days, developers stick to DevOps methods to enhance their performance efficiency. Similarly,  experienced data scientists have also developed MLOps methods for the same purpose. You don’t need to learn MLOps by heart, as many MLOps tools in the market make it easier for you to implement them. This particular skill will help you become a full-stack data scientist, so make sure you don’t skip it.

Apart from these, strong communication and presentation skills are by default added to the bucket of a good data scientist as they are likely to interact with the data engineering team, data analysts team, and other data scientists.

After going through the technical skills required to become a successful data scientist, it is time to find the answer to the question:what steps you must take to become a data scientist. Come, let us find out!

Steps to Becoming a Data Scientist

become a data scientist

Master Advanced Mathematics!

As mathematics is fundamental to all the algorithms and analytics in Data Science, the first step is understanding the underlying mathematics behind the scenes. Topics in advanced mathematics such as Linear Algebra, Multivariable Calculus, Probability, and Statistics are key for aspiring data scientists. Any graduation-level mathematics book can help you learn all these topics. So, pick your favorite mathematics textbook and get started right away!

Befriend them, Algorithms!

The next step will be to explore algorithms used in data science. Start with understanding the basics of machine learning algorithms and understand their advantages and disadvantages. Next, move towards deep learning algorithms to solve more complex problems in Data Science that deal with computer vision. Lastly, don’t skip learning NLP techniques, as they are useful when leveraging textual data.

Program Your Way Out!

The algorithms used by data scientists are slightly complicated if one tries to build them from scratch in a programming language. Fortunately, programming languages like R, Python, Scala, etc., are there that contain various libraries and packages that make the implementation of various algorithms in data science easy. So, start learning a programming language of your choice and explore which libraries offer what functions.

Opt for MLOps!

MLOps is likely to be the next big thing in Data Science as many data scientists are likely to head toward becoming full-stack data scientists. This is where MLOps comes into the picture, a set of rules that help define the life cycle of a project in data science. The best part about implementing MLOps projects is that there various MLOps tools in the market like MLflow, DVC, Pachyderm, etc., that assist in easy deployment and monitoring of data science projects.

Work Hard and Smart to Become a Data Scientist!

To excel in any subject, practicing what you learned by solving challenging problems is important. In Data Science, you can find solved problems on websites like GitHub and Kaggle, but these sites are likely to consume much of your time as they lack proper explanations. So, we suggest you try out Projects at the ProjectPro repository that will help you work hard and be smart as the repository contains guided solutions with codes.

You can find the common resources for each of the steps mentioned in our Data Scientist Learning Path, Career Track & Roadmap for 2022 blog. Do check it out if you are interested.

The steps discussed in this section are generic for aspiring data scientists who have just started exploring the domain. But, often, we receive specific requests from the aspiring data scientists, and in the following sections, we have addressed the most commonly asked questions around becoming a data scientist.

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Path to Becoming a Data Scientist: Is Age a Bar?

Senior professionals in the IT industry often find themselves attracted to the buzz around Data Science and are curious about knowing if they can transition to the Data Science field. Yes, such professionals can easily switch to a career in Data Science by learning all the required skills. They don't exactly need to sit for those data science programs or pursue a data science course. Becoming a Data Scientist at 35 or Becoming a Data Scientist at 45 is more about understanding what skills they have learned so far and realizing what the missing ones are. Once the list of skills to be learned is ready, they can quickly start their journey of learning data science.

How to Become a Data Scientist for FREE?

With so many free resources available online, it is not a difficult task to become a data scientist for free. There are many free courses available, but instead of enrolling in them, we recommend you invest your time in reading the following textbooks that are freely available:

  1. Learn Python the Right Way by Zed Shaw

  2. Deep Learning by  Ian Goodfellow and Yoshua Bengio, and Aaron Courville.

  3. Statistical Learning with Sparsity: The Lasso and Generalizations by Trevor Hastie, Robert Tibshirani, Martin Wainwright.

  4. An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, and Rob Tibshirani

  5. Data Mining and Analysis by Mohammed J. Zaki and Wagner Meira, Jr

How to Become a Data Scientist in 6 Months?

Becoming a data scientist in six months is possible if you have a strong background in mathematics and coding. If you are one such candidate, follow the steps below:

  1. Download simple datasets and perform Exploratory Data Analysis on them.

  2. Start exploring machine learning techniques, deep learning algorithms, and NLP methodologies.

  3. Implement data-specific algorithms on your dataset using a programming language.

  4. Enhance the performance of your algorithms using statistical methods.

  5. Practice more and more number of projects in data science across various industries.

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How to Become a Data Scientist With No Experience?

Becoming a data scientist with no experience is possible if you are determined and possess the tenacity to do the hard work. After learning all the skills required to become a data scientist, start looking for beginner-friendly opportunities on LinkedIn. You must focus on gaining experience in the initial stages of your data science career instead of salary. Once you have gained a few years of experience, prepare a data science project portfolio and apply for the data science positions at different companies.

How Hard it is to Become a Data Scientist?

It is not at all difficult to become a data scientist if you have the right resources with you.Here is a brief step-by-step guide on how to become a data scientist easily:

  1. Learn advanced mathematics

  2. Explore different algorithms in Data Science and understand their use cases.

  3. Implement these algorithms using a programming language like Python.

  4. Understand the application of as many algorithms as possible by working on multiple projects.

  5. Enhance the efficiency of a data science project using MLOps tools.

What Qualifications Do You Need to Become a Data Scientist?

While many believe that a bachelor’s degree in computer science is needed to become a data scientist, it is not entirely correct. You can become a data scientist without that by following the learning path discussed in this article. All you need to have before starting your journey in Data Science is high school-level education. However, having advanced degrees in technical subjects like mathematics, physics, chemistry, etc., can be beneficial for an aspiring data scientist.

How to Become a Data Scientist Without a Degree? How to Become a Data Scientist After 12th?

The answer to both of these questions is that start slow and gradually climb up the ladder of difficulty. Follow these simple steps below to know how to kick start your journey in data science after class 12th:

  1. Focus on learning advanced mathematics and spend at least a year practicing several problems to strengthen your basics.

  2. Start reading about the simple machine learning algorithms that data scientists use. Spend at least six months learning them.

  3. Next, learn a programming language and implement a few beginner-friendly machine learning algorithms with its libraries.

  4. After that, explore deep learning algorithms and applications in computer vision projects.

  5. Next, explore the NLP techniques and work on a few NLP Projects to enhance your expertise.

  6. Finally, aim for a data science internship and start your career in data science.

All these customized questions may or may not be the ones you wanted to answer. It might be possible that you are comfortable with a particular set of algorithms but want to know more about other algorithms. If similar is the case, don’t worry because ProjectPro’s customized learning path considers such requests and curates a specific learning path for each user. So, check out ProjectPro’s repository of solved data science and big data projects that will help you excel in the data science/big data industry by providing you with a requirements-based learning path.

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FAQs on How to Become a Data Scientist from Scratch? 

1) Is Becoming a Data Scientist Hard?

No, becoming a data scientist is not hard if you are learning it with the help of appropriate resources. Instead of enrolling in popular online courses, sticking to classic textbooks and working on a few practical data science problems is better.

2)  How Long Does it Take to Become a Data Scientist?

The time required to become a data scientist depends on an individual’s capabilities to grasp new concepts and their educational background. A strong background in technical subjects like Physics, Chemistry, Mathematics, Statistics, etc., will make it easier to become a data scientist.

3) How to become a Healthcare Data Scientist?

To become a Healthcare Data Scientist, we suggest you explore the following problems in Data Science:

  1. Cancer Cells Detection

  2. Polyps Detection

  3. Heart Failure Prediction

  4. Disease Monitoring

  5. Drug Discovery

After doing a few sample projects on similar topics, we suggest you read a few research articles and request a few authors to collaborate with you. You can also look for job opportunities for data scientists in the Healthcare industry.

4) How to become a Geospatial Data Scientist?

To become a geospatial data scientist, work on satellite data. Organizations like NASA, ISRO, JAXA, etc., host a few free datasets for the public. Identify the satellites you are interested in and visit the official websites to download the datasets. Use these datasets to build a few data science projects.

5) How to become a Genomic Data Scientist?

To become a Genomic Data Scientist, focus on datasets containing gene informations. There are a few datasets readily available on Kaggle that you can consider to get a fair idea of using data science for solving genomic problems. Next, approach institutions that work on genomic research projects and start collaborating with them.

 

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