How to Become a Data Scientist in 2024: Ultimate Guide

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Yachana Sharma

04 January 2024

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How to Become a Data Scientist in 2024: Ultimate Guide

Unlock your data science potential. If you are planning to pursue a career in data science and want to know how to become a data scientist, here is a career guide.

Features

Table of Contents

  • Description

  • Who is a Data Scientist?

  • Who can become a Data Scientist?

  • 5 Most Important Technical Skills Required to Master Data Science

  • How to Learn Data Science from Scratch

  • Careers in Data Science

  • Alternate Career Roles For Data Scientists

  • Conclusion

Unlock your data science potential. If you are planning to pursue a career in data science and want to know how to become a data scientist, here is a career guide.

Description

The field of data science has accumulated enormous popularity in recent years and for a good reason. Data is the new oil. Companies across industries are now using data science to build successful data-driven businesses to stay ahead of their competitors. As a result, data science is a highly sought-after career option.

This career guide will help provide insights on how to become a Data Scientist, covering data scientist qualifications, skills to become a data scientist from scratch, how long does it take to become a data scientist, data science courses and much more.

Who is a Data Scientist?

A Data Scientist is a trained professional who leverages advanced analytical techniques, statistical methods, and machine learning algorithms to extract meaningful insights from vast and complex datasets. They possess a unique blend of domain knowledge, programming expertise, and statistical understanding to solve intricate business problems and make data-driven decisions.

What Do Data Scientists Do?

  • Conducting Data Analysis and Predictive Modeling to derive actionable insights.
  • Designing and implementing Machine Learning (ML) Algorithms for data-driven solutions.
  • Collecting, cleaning, and transforming raw data for analysis.
  • Collaborating with cross-functional teams to identify business challenges and develop data-driven strategies.
  • Building and maintaining data pipelines and databases for efficient data management.
  • Communicating complex findings and technical concepts to non-technical stakeholders.
  • Ensuring data privacy and adhering to ethical data handling practices

A Data Scientist is a trained professional who leverages advanced analytical techniques, statistical methods, and machine learning algorithms to extract meaningful insights from vast and complex datasets. They possess a unique blend of domain knowledge, programming expertise, and statistical understanding to solve intricate business problems and make data-driven decisions.

What Do Data Scientists Do?

  • Conducting Data Analysis and Predictive Modeling to derive actionable insights.
  • Designing and implementing Machine Learning (ML) Algorithms for data-driven solutions.
  • Collecting, cleaning, and transforming raw data for analysis.
  • Collaborating with cross-functional teams to identify business challenges and develop data-driven strategies.
  • Building and maintaining data pipelines and databases for efficient data management.
  • Communicating complex findings and technical concepts to non-technical stakeholders.
  • Ensuring data privacy and adhering to ethical data handling practices

Who can become a Data Scientist?

To become a Data Scientist, the first thing is to have at least a Bachelor's Degree in technical disciplines such as Mathematics, Engineering, Statistics, or IT (Information Technology). A degree in one of these serves as an education requirement for Data Scientists.

How to Become a Data Scientist: Step-by-Step Plan of Action

Given below are the common steps a professional can follow to break into the data science industry:

Gain proficiency in Data Wrangling, Data Visualization, and Reporting methods.

Data Wrangling involves cleaning and organizing raw data for analysis, while data visualization represents insights through charts and graphs. Reporting presents analyzed data in a structured format, aiding informed decision-making. Mastering these skills empowers you to draw valuable insights, communicate effectively, and contribute significantly to data-driven processes. 

Enroll in data science courses, gain hands-on experience with tools like Python, R, Tableau, or Excel, and undertake practical projects to sharpen your expertise. With these capabilities, you become a valuable asset, driving innovation and substantially impacting your field.

Elevate your understanding of Statistics, Math, and Machine Learning.

Mathematical concepts like probability, statistics, and algebra are some of the prerequisites to learn Data Science. Master mathematical concepts like Linear Algebra and Optimization for Powerful Algorithms. Develop proficiency in diverse Machine Learning (ML) techniques, enabling autonomous decision-making and predictive analytics. Enroll in courses, and engage in hands-on practice with real data and machine learning libraries. 

Apply your knowledge to practical projects and competitions for real-world impact. With these skills, you can become a sought-after professional, driving innovation and creating data-driven solutions that shape industries and society.

Develop Coding Expertise

Developing coding expertise is included in the qualification required for Data Scientist. Proficient coding enables efficient Data Manipulation, Analysis, and Visualization. With skills in Python, R, or SQL (Structured Query Language), you can clean and preprocess data, perform complex analyses, and apply machine learning algorithms. 

Coding proficiency facilitates impactful data visualizations and enhances project reproducibility. Take online courses, engage in hands-on projects, and collaborate with experienced Data Scientists to develop your coding skills. By mastering coding, you become a sought-after Data Science professional capable of making data-driven decisions and positively impacting businesses and society.

Develop an understanding of Database Systems.

Understanding database systems is vital for aspiring Data Science professionals. With databases at the core of data storage and retrieval, this knowledge is essential for efficient data analysis. Database understanding enables data cleaning, preprocessing, and integration, consolidating diverse datasets. Proficiency in SQL allows for complex querying and meaningful data extraction. 

Knowledge of database security ensures the protection of sensitive information. Optimizing queries and data structures ensures scalability and performance. Integration with Data Science tools streamlines workflows, improving productivity. 

Enroll in courses, practice SQL (Structured Query Language), queries, and collaborate to gain practical experience. Mastering database systems equips you with a crucial skillset, empowering effective data management and analysis as a successful Data Science professional.

Get hands-on experience with Big Data.

Hands-on experience with Big Data skills is vital for aspiring Data Science professionals. It offers real-world challenges, optimizing scalability and performance. Visualization and Machine Learning (ML) skills scale with big data. You learn various big data technologies and enhance your problem-solving abilities. 

Engage in real-world projects, internships, and competitions with large datasets. Collaborate with experienced Data Scientists to tackle big data challenges effectively. Experiment with public and open-source datasets. This experience hones your expertise and confidence in working with Big Data, setting you apart as a competent Data Science professional capable of delivering impactful insights and data-driven solutions.

Practice with real data, gain experience, and network with data scientists for professional growth.

Gain real-world data experience to enhance your Data Science expertise. Working with actual datasets provides a deeper understanding of data complexities and sharpens analysis, Machine Learning (ML), and visualization skills. Practical projects are an important aspect of the roadmap to becoming a Data Scientist, as they strengthen your portfolio and credibility when seeking opportunities. 

Networking with data scientists fosters collaboration, knowledge exchange, and diverse perspectives. By practicing with real data and engaging with peers, you become a well-rounded professional, ready to tackle complex challenges and contribute innovative data-driven solutions. 

Seek Internships or Job Placements

Internships and entry-level data science jobs are crucial for becoming a proficient professional. They offer hands-on experience, real-world problem-solving, and skill development. Working on diverse data projects enhances your technical abilities and fosters collaboration and communication skills. Networking is another aspect that will help you build valuable connections within the Data Science community. 

Successful internships and jobs strengthen your resume, creating a robust portfolio. They also clarify your career path and specific interests within Data Science. Taking on an internship or applying for employment reduces the gap between theory and practice, preparing you to contribute effectively in the dynamic world of Data Science.

To become a Data Scientist, the first thing is to have at least a Bachelor's Degree in technical disciplines such as Mathematics, Engineering, Statistics, or IT (Information Technology). A degree in one of these serves as an education requirement for Data Scientists.

How to Become a Data Scientist: Step-by-Step Plan of Action

Given below are the common steps a professional can follow to break into the data science industry:

Gain proficiency in Data Wrangling, Data Visualization, and Reporting methods.

Data Wrangling involves cleaning and organizing raw data for analysis, while data visualization represents insights through charts and graphs. Reporting presents analyzed data in a structured format, aiding informed decision-making. Mastering these skills empowers you to draw valuable insights, communicate effectively, and contribute significantly to data-driven processes. 

Enroll in data science courses, gain hands-on experience with tools like Python, R, Tableau, or Excel, and undertake practical projects to sharpen your expertise. With these capabilities, you become a valuable asset, driving innovation and substantially impacting your field.

Elevate your understanding of Statistics, Math, and Machine Learning.

Mathematical concepts like probability, statistics, and algebra are some of the prerequisites to learn Data Science. Master mathematical concepts like Linear Algebra and Optimization for Powerful Algorithms. Develop proficiency in diverse Machine Learning (ML) techniques, enabling autonomous decision-making and predictive analytics. Enroll in courses, and engage in hands-on practice with real data and machine learning libraries. 

Apply your knowledge to practical projects and competitions for real-world impact. With these skills, you can become a sought-after professional, driving innovation and creating data-driven solutions that shape industries and society.

Develop Coding Expertise

Developing coding expertise is included in the qualification required for Data Scientist. Proficient coding enables efficient Data Manipulation, Analysis, and Visualization. With skills in Python, R, or SQL (Structured Query Language), you can clean and preprocess data, perform complex analyses, and apply machine learning algorithms. 

Coding proficiency facilitates impactful data visualizations and enhances project reproducibility. Take online courses, engage in hands-on projects, and collaborate with experienced Data Scientists to develop your coding skills. By mastering coding, you become a sought-after Data Science professional capable of making data-driven decisions and positively impacting businesses and society.

Develop an understanding of Database Systems.

Understanding database systems is vital for aspiring Data Science professionals. With databases at the core of data storage and retrieval, this knowledge is essential for efficient data analysis. Database understanding enables data cleaning, preprocessing, and integration, consolidating diverse datasets. Proficiency in SQL allows for complex querying and meaningful data extraction. 

Knowledge of database security ensures the protection of sensitive information. Optimizing queries and data structures ensures scalability and performance. Integration with Data Science tools streamlines workflows, improving productivity. 

Enroll in courses, practice SQL (Structured Query Language), queries, and collaborate to gain practical experience. Mastering database systems equips you with a crucial skillset, empowering effective data management and analysis as a successful Data Science professional.

Get hands-on experience with Big Data.

Hands-on experience with Big Data skills is vital for aspiring Data Science professionals. It offers real-world challenges, optimizing scalability and performance. Visualization and Machine Learning (ML) skills scale with big data. You learn various big data technologies and enhance your problem-solving abilities. 

Engage in real-world projects, internships, and competitions with large datasets. Collaborate with experienced Data Scientists to tackle big data challenges effectively. Experiment with public and open-source datasets. This experience hones your expertise and confidence in working with Big Data, setting you apart as a competent Data Science professional capable of delivering impactful insights and data-driven solutions.

Practice with real data, gain experience, and network with data scientists for professional growth.

Gain real-world data experience to enhance your Data Science expertise. Working with actual datasets provides a deeper understanding of data complexities and sharpens analysis, Machine Learning (ML), and visualization skills. Practical projects are an important aspect of the roadmap to becoming a Data Scientist, as they strengthen your portfolio and credibility when seeking opportunities. 

Networking with data scientists fosters collaboration, knowledge exchange, and diverse perspectives. By practicing with real data and engaging with peers, you become a well-rounded professional, ready to tackle complex challenges and contribute innovative data-driven solutions. 

Seek Internships or Job Placements

Internships and entry-level data science jobs are crucial for becoming a proficient professional. They offer hands-on experience, real-world problem-solving, and skill development. Working on diverse data projects enhances your technical abilities and fosters collaboration and communication skills. Networking is another aspect that will help you build valuable connections within the Data Science community. 

Successful internships and jobs strengthen your resume, creating a robust portfolio. They also clarify your career path and specific interests within Data Science. Taking on an internship or applying for employment reduces the gap between theory and practice, preparing you to contribute effectively in the dynamic world of Data Science.

5 Most Important Technical Skills Required to Master Data Science

Being an interdisciplinary domain, Data Science demands not just one or two but various specialized skills and learning in Computer Science. Here are some of the essential Data Scientist skills one must have, irrespective of the knowledge gained.

  • Python Coding: Python is the most usable coding language in Data Science as it enables mining data and developing machine learning models or web scraping. Enduring various data formats, python can help you design and find datasets and import SQL tables into your code.
     
  • R Programming: R is a programming language that is typically created for data analysis purposes which includes the formulas and methods used for information processing and statistical analysis.
     
  • Machine Learning and Artificial Intelligence: Learning diverse techniques of Machine Learning, such as time series, computer vision, outlier detection, natural language processing, supervised machine learning, etc., is intrinsically significant to overcome challenges prevalent in Data Science.
     
  • Hadoop Platform: The volume of data can be massive enough to exceed the system's memory. The Hadoop Platform enables Data Scientists to Work with large data sets on multiple or distributed systems. This platform also helps in data filtration, data sampling and summarization, exploration, etc.
     
  • SQL: Structured Query Language, in short SQL, helps you communicate, access and handle the database by adding, subtracting or pulling the data. The Data Scientist must be skilled in SQL, specifically designed for time-saving and reducing the amount of programming for complex queries through its compact commands.

Due to its critical role in decision-making and problem-solving, data science skills remain high-demand even during an economic downturn. Skills like SQL, Python, Machine Learning, etc., are some of the top recession-proof data science skills you can master in 2024 to future-proof your career.

Being an interdisciplinary domain, Data Science demands not just one or two but various specialized skills and learning in Computer Science. Here are some of the essential Data Scientist skills one must have, irrespective of the knowledge gained.

  • Python Coding: Python is the most usable coding language in Data Science as it enables mining data and developing machine learning models or web scraping. Enduring various data formats, python can help you design and find datasets and import SQL tables into your code.
     
  • R Programming: R is a programming language that is typically created for data analysis purposes which includes the formulas and methods used for information processing and statistical analysis.
     
  • Machine Learning and Artificial Intelligence: Learning diverse techniques of Machine Learning, such as time series, computer vision, outlier detection, natural language processing, supervised machine learning, etc., is intrinsically significant to overcome challenges prevalent in Data Science.
     
  • Hadoop Platform: The volume of data can be massive enough to exceed the system's memory. The Hadoop Platform enables Data Scientists to Work with large data sets on multiple or distributed systems. This platform also helps in data filtration, data sampling and summarization, exploration, etc.
     
  • SQL: Structured Query Language, in short SQL, helps you communicate, access and handle the database by adding, subtracting or pulling the data. The Data Scientist must be skilled in SQL, specifically designed for time-saving and reducing the amount of programming for complex queries through its compact commands.

Due to its critical role in decision-making and problem-solving, data science skills remain high-demand even during an economic downturn. Skills like SQL, Python, Machine Learning, etc., are some of the top recession-proof data science skills you can master in 2024 to future-proof your career.

How to Learn Data Science from Scratch

To learn Data Science from scratch, resources like courses or enrolling in certifications are considered the best way to learn Data Science. Various courses can help you become a Data Scientist after your 12th. So, here are the top 5 popular data science courses which will help learners in learning and career advancement:

IBM Data Science Professional Certificate

IBM Data Science program will provide you with the latest job-ready skills, including Python, databases, SQL, open-source tools, libraries, data visualization, statistical analysis, and predictive modeling. You'll gain hands-on experience with the IBM Cloud and learn data science using real data science tools that work with real data sets.

Post Graduate Program in Data Science by Henry Harvin:

In this Post Graduate program in Data Science, you will become proficient in analytics tools like Python, Tableau, and SQL while also learning about machine learning techniques such as Regression, Clustering, Time Series Forecasting, and Classification. Additionally, you'll develop an analytics framework using statistics and data modeling to structure various business problems and gain expertise in data cleaning and transformation operations.

Data Science Specialization by Coursera

In this Data Science specialization, you will learn how to use R for data acquisition, cleaning analysis, and visualization. You will also explore GitHub for project management and gain practical experience in regression analysis, focusing on least squares and inference using regression models.

Data Science: R Basics

The Data Science: R Basics course teaches you basic R programming. R is more effective when it's applied to solving a problem. The importance of the Data Science course is that it will use real data about crime in the United States. Learn the R skills necessary to answer important questions about the differences in crime between the states.

Post Graduate Program in Data Analytics and Data Science

In this Post Graduate Program in Data Analytics and Data Science, learners are helped to gain a better understanding of the concepts of Data Science, Machine Learning, and Deep Learning. The course curriculum gives exposure to modern trends & the standard practices followed in the industry right now.

Data Science for Everyone

This Data Science for all intermediate-level certification program will help professionals learn about data scientist roles and foundational topics like A/B testing, time series analysis, and machine learning. It helps freshers and professionals to advance their career trajectories. Further, it helps gain Data Science, Data Visualisation and Data Analysis skills essential for building a successful career in the data science industry. 

Data Science Course Training in India 

Data scientists with 10+ years of deep domain expertise are imparted to this advanced-level data science course. The course spans over three months, including 80+ hours of instructor-led live interactive sessions that seek to shape Data science enthusiasts into fully competent data science experts. It is tailor-made for learners and professionals looking to become industry-ready data and science experts.

To learn Data Science from scratch, resources like courses or enrolling in certifications are considered the best way to learn Data Science. Various courses can help you become a Data Scientist after your 12th. So, here are the top 5 popular data science courses which will help learners in learning and career advancement:

IBM Data Science Professional Certificate

IBM Data Science program will provide you with the latest job-ready skills, including Python, databases, SQL, open-source tools, libraries, data visualization, statistical analysis, and predictive modeling. You'll gain hands-on experience with the IBM Cloud and learn data science using real data science tools that work with real data sets.

Post Graduate Program in Data Science by Henry Harvin:

In this Post Graduate program in Data Science, you will become proficient in analytics tools like Python, Tableau, and SQL while also learning about machine learning techniques such as Regression, Clustering, Time Series Forecasting, and Classification. Additionally, you'll develop an analytics framework using statistics and data modeling to structure various business problems and gain expertise in data cleaning and transformation operations.

Data Science Specialization by Coursera

In this Data Science specialization, you will learn how to use R for data acquisition, cleaning analysis, and visualization. You will also explore GitHub for project management and gain practical experience in regression analysis, focusing on least squares and inference using regression models.

Data Science: R Basics

The Data Science: R Basics course teaches you basic R programming. R is more effective when it's applied to solving a problem. The importance of the Data Science course is that it will use real data about crime in the United States. Learn the R skills necessary to answer important questions about the differences in crime between the states.

Post Graduate Program in Data Analytics and Data Science

In this Post Graduate Program in Data Analytics and Data Science, learners are helped to gain a better understanding of the concepts of Data Science, Machine Learning, and Deep Learning. The course curriculum gives exposure to modern trends & the standard practices followed in the industry right now.

Data Science for Everyone

This Data Science for all intermediate-level certification program will help professionals learn about data scientist roles and foundational topics like A/B testing, time series analysis, and machine learning. It helps freshers and professionals to advance their career trajectories. Further, it helps gain Data Science, Data Visualisation and Data Analysis skills essential for building a successful career in the data science industry. 

Data Science Course Training in India 

Data scientists with 10+ years of deep domain expertise are imparted to this advanced-level data science course. The course spans over three months, including 80+ hours of instructor-led live interactive sessions that seek to shape Data science enthusiasts into fully competent data science experts. It is tailor-made for learners and professionals looking to become industry-ready data and science experts.

Careers in Data Science

Data science career prospects are, nonetheless, extremely diverse. Opting to embark on a data science career will open up several career options and job titles. Here are the popular top 5 job titles and career opportunities.

Data Scientist

A data scientist is a specialist who specializes in exploring and interpreting data. The primary goal of Data Scientists is to help institutions make sounder judgments and enhance their operations. Additionally, data scientists may create new ways to gather and hold data. The average annual Data Scientist salary in India is ₹5,056,213, whereas in the US, it is $60,735. In most industries, you can observe that Senior Data Scientist salary is quite high compared to other such roles.

Data Engineer

Data engineers build large, complex data sets. They recognize, design, and implement internal process modifications and assemble the infrastructure for optimal data extraction, mutation, and loading. They also construct analytics tools that employ the data pipeline. The average Data Engineer salary in India is ₹6,642,684, and the salary in the US is $79,792.

Data Architect

Data architects analyze the structural needs for new software and applications and design database solutions. They establish and configure information systems and migrate data from legacy to new ones. The Data Architect salary in India is ₹8,759,149, and in the US, it is $105,215.

Data Analyst

Data analysts obtain data from primary or secondary sources and uphold databases. They decode that data, analyze results using statistical methods, and construct data collection methods and other solutions that allow management to prioritize business and information needs. The Data Analyst's salary in India is ₹4,642,353, and in the US, it is $55,764.

Business Analyst

Business analysts aid a company with planning and monitoring by producing and organizing needs. They validate resource necessities and develop cost-estimate models by producing informative, actionable and repeatable reporting. The Business Analyst's salary in India ranges from ₹2.3 Lakhs to ₹10 Lakhs, and in the US is $80,744.

Data science career prospects are, nonetheless, extremely diverse. Opting to embark on a data science career will open up several career options and job titles. Here are the popular top 5 job titles and career opportunities.

Data Scientist

A data scientist is a specialist who specializes in exploring and interpreting data. The primary goal of Data Scientists is to help institutions make sounder judgments and enhance their operations. Additionally, data scientists may create new ways to gather and hold data. The average annual Data Scientist salary in India is ₹5,056,213, whereas in the US, it is $60,735. In most industries, you can observe that Senior Data Scientist salary is quite high compared to other such roles.

Data Engineer

Data engineers build large, complex data sets. They recognize, design, and implement internal process modifications and assemble the infrastructure for optimal data extraction, mutation, and loading. They also construct analytics tools that employ the data pipeline. The average Data Engineer salary in India is ₹6,642,684, and the salary in the US is $79,792.

Data Architect

Data architects analyze the structural needs for new software and applications and design database solutions. They establish and configure information systems and migrate data from legacy to new ones. The Data Architect salary in India is ₹8,759,149, and in the US, it is $105,215.

Data Analyst

Data analysts obtain data from primary or secondary sources and uphold databases. They decode that data, analyze results using statistical methods, and construct data collection methods and other solutions that allow management to prioritize business and information needs. The Data Analyst's salary in India is ₹4,642,353, and in the US, it is $55,764.

Business Analyst

Business analysts aid a company with planning and monitoring by producing and organizing needs. They validate resource necessities and develop cost-estimate models by producing informative, actionable and repeatable reporting. The Business Analyst's salary in India ranges from ₹2.3 Lakhs to ₹10 Lakhs, and in the US is $80,744.

Alternate Career Roles For Data Scientists

Apart from these five main and most demanded career outlooks in Data Science, here are some other common job profiles you can consider:

Apart from these five main and most demanded career outlooks in Data Science, here are some other common job profiles you can consider:

Conclusion

The goal of Data Scientists is to extract, pre-process and interpret data. Through this, companies can make better decisions, and that’s why Data Science is the future. We hope this ultimate learning guide has answered all your questions on data science as a career and how long it takes to become a data scientist in 2023. 

Published with permission from our partner Aegis School of Business.

The goal of Data Scientists is to extract, pre-process and interpret data. Through this, companies can make better decisions, and that’s why Data Science is the future. We hope this ultimate learning guide has answered all your questions on data science as a career and how long it takes to become a data scientist in 2023. 

Published with permission from our partner Aegis School of Business.

Features

Table of Contents

  • Description

  • Who is a Data Scientist?

  • Who can become a Data Scientist?

  • 5 Most Important Technical Skills Required to Master Data Science

  • How to Learn Data Science from Scratch

  • Careers in Data Science

  • Alternate Career Roles For Data Scientists

  • Conclusion