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How to Become Data Scientist in 2024 [Step-by-Step]

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22nd Dec, 2023
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    How to Become Data Scientist in 2024 [Step-by-Step]

    Every business now incorporates data science into their operations, especially those that recognize the value of data and the potential applications of that knowledge. A data scientist's main responsibility is to draw practical conclusions from complicated data so that you may make informed business decisions. This career guide can assist you in taking the first steps toward a rewarding career in data science if you're wondering how to become a data scientist.  

    This blog offers a comprehensive explanation of the data skills you must acquire, the top data science online courses, career paths in data science, and how to create a portfolio to become a data scientist. 

    What is Data Science?

    Data science is a discipline of study that combines subject-matter knowledge, programming abilities, and competence in math and statistics to draw forth important value from data. The information used for analysis can be given in various formats and come from various sources. 

    What Does a Data Scientist Do?

    Data scientists are specialists who gather, examine and analyze vast amounts of data. A number of traditional technical roles, such as those of a scientist, statistician, computer specialist, and mathematician, have evolved into the position of a data scientist. Keep reading through this article, and you will understand how do you become a data scientist and how long does it take to become a data scientist.  

    How to Become a Data Scientist? (A Step-By-Step Guide)

    Returning to the question of how to become a good data scientist, the discussion above indicates that there are a few fundamental requirements that must be met. You ought to be hungry for information.

    How To Become A Data Scientist

    Here are the seven key actions you should take to become a data scientist. These stages will serve as a learning path and help you become a data scientist step by step. 

    Step 1: Learn Data Science Fundamentals

    Most individuals mistakenly believe that domain knowledge is not crucial to data science. The primary objective of data science is to derive useful insights from the data that can be applied to the business's operations. 

    The fundamentals of data science are introduced in this course, along with effective methods for exploring complex data through data visualization and summary statistics. The course covers fundamental computational statistical inference using the three frequentist methodologies: maximum likelihood, bootstrap, and Bayesian.  

    Step 2: Master Skills  

    Top Skills Needed to Become a Data Scientist 

    To excel as a data scientist, you need to develop a combination of soft skills, hard skills, and other competencies. Here are some essential skills to focus on: 

    Soft Skills 

    1. Analytical Thinking: Develop the ability to break down complex problems and think critically about data-related challenges. 
    2. Communication: Enhance your skills in presenting technical information to both technical and non-technical audiences. 
    3. Curiosity: Foster a sense of curiosity and a willingness to explore data, ask questions, and uncover insights. 
    4. Collaboration: Learn to work effectively in teams, as data science often involves collaborative projects with diverse stakeholders. 

    Hard Skills 

    1. Mathematics and Statistics: Gain a strong foundation in mathematics, including linear algebra, calculus, and probability theory. Statistics are important for analyzing and interpreting the data. 
    2. Programming: There are many programming languages out there that were created for different purposes. Some offer great productivity and performance to process significant amounts of data, making them better suitable for data science. Hence, below are the key programming languages needed for Data Science. 
      • Python 
      • SQL 
      • Java 
      • Julia 
      • Scala 
      • C/C++ 
      • JavaScript 
      • Swift 
      • Go 
      • MATLAB 
      • SAS 
    3. Data Manipulation and Analysis: Develop skills in data wrangling, data cleaning, and data preprocessing. Learn techniques for exploratory data analysis (EDA) and feature engineering.  
    4. Machine Learning: Understand and implement various machine learning algorithms, including supervised and unsupervised learning techniques. Study concepts like regression, classification, clustering, and dimensionality reduction.  
    5. Data Visualization: Learn how to effectively present data visually using tools like Matplotlib, Seaborn, or Tableau. Develop the ability to create insightful visualizations and communicate data-driven stories. 

    Other Competencies 

    1. Domain Knowledge: Acquire domain-specific knowledge related to the industry you plan to work in as a data scientist. This could be finance, healthcare, marketing, or any other field. 
    2. Big Data Technologies: Familiarize yourself with distributed computing frameworks like Apache Hadoop and Apache Spark. Learn how to work with big data technologies to process and analyze large datasets. 
    3. Data Management: Understand databases, SQL, and data querying languages. Learn about data storage, retrieval, and management techniques. 

    Top Data Scientist Certifications / Degree 

    While certifications and degrees are not mandatory to become a data scientist, they can boost your credibility and demonstrate your proficiency. Some popular certifications and degrees include: 

    1. Certified Analytics Professional (CAP) 
    2. IBM Data Science Professional Certificate 
    3. Microsoft Certified: Azure Data Scientist Associate 
    4. Cloudera Certified Data Scientist 
    5. HarvardX Data Science Professional Certificate 
    6. Master's degree or Ph.D. in Data Science, Computer Science, or a related field. 

    Remember, practical experience is highly valuable in the field of data science. Participate in data science projects, work on real-world datasets, and build a portfolio to showcase your skills to potential employers.  

    Step 3: Determine Your Ideal Timeline  

    The time it takes to become a data scientist can vary depending on factors such as your prior knowledge, learning format, time dedication, and learning resources. 

    • Start Learning in a Way That Suits You 

    Assess your existing knowledge and choose a learning format that suits your preferences and circumstances.  

    Consider options like self-paced online courses, boot camps, or university degrees. Determine how much time you can dedicate each week and set realistic goals. Identify high-quality learning resources that align with your learning style and engage in project-based learning for practical experience. 

    • How Long Will It Take to Become a Data Scientist? 

    The duration to become a data scientist varies, typically ranging from several months to a couple of years. Starting from scratch and dedicating around 10 to 20 hours per week, it can take several months to gain a solid foundation in data science, including mathematics, programming, and basic machine learning concepts.  

    To reach an intermediate or advanced level and become proficient in applying data science techniques to real-world problems, it may take an additional 6 months to 2 years. However, individual timelines may vary based on the learning pace and complexity of topics.  Continuous learning and practical experience are vital for growth in this dynamic domain. 

    Step 4: Begin Building Your Data Scientist Portfolio / Resume 

    As you progress in your data science journey, it's crucial to start building your portfolio and resume. This allows you to showcase your skills, projects, and expertise to potential employers or clients. Include relevant data science projects, highlighting your role, the problem you tackled, the methodologies used, and the outcomes achieved. Demonstrate your ability to work with real-world data and present your findings effectively. Additionally, emphasize any certifications, degrees, or specialized training you have obtained. A strong portfolio/resume will help you stand out in the competitive field of data science and increase your chances of landing opportunities. 

    Portfolio Example or Resume Template for Download 

    Sample 1: Juan Jose Carin, Data Scientist, University of California, Berkley 

     https://www.ischool.berkeley.edu/system/files/resume/resume_juanjosecarin.pdf 

    Sample 2: Jonathon Whitmore, Ph.D., Data Scientist 

    https://jonathanwhitmore.com/wp-content/uploads/2016/04/Whitmore-resume.pdf 

    Sample 3: Arindam Pal, Data Scientist, IIT Delhi 

    https://www.cse.iitd.ac.in/~arindamp/resume.pdf 

    Step 5: Where and how to find a job 

    Finding a data scientist job involves exploring various avenues and utilizing different strategies. Here are some tips on where and how to find a data scientist job: 

    1. Online Job Boards and Platforms: Utilize popular job search websites and platforms like LinkedIn, Indeed, Glassdoor, and Kaggle. These platforms often have dedicated sections for data science and offer a wide range of job opportunities. 
    2. Company Websites: Visit the career pages of companies known for hiring data scientists. Many organizations, particularly larger ones, post their job openings directly on their websites. 
    3. Professional Networking: Leverage your professional network, attend industry conferences, and join data science communities to expand your connections. Networking can lead to valuable job leads and referrals. 
    • Top Companies Hiring Data Scientists 

    Prominent companies across various industries often have a demand for data scientists. Some notable companies known for hiring data scientists include: 

    1. Google 
    2. Facebook 
    3. Amazon 
    4. Microsoft 
    5. Apple 
    6. IBM 
    7. Netflix 
    8. Airbnb 
    9. Uber 
    10. Tesla 
    • Job Outlook for Data Scientist

    The job outlook for data scientists is promising due to the growing demand for data-driven decision-making. Big data, machine learning advancements, and the need for insights across industries contribute to strong job prospects. Data scientists are sought after in the technology, finance, healthcare, e-commerce, and marketing sectors. While the job market is competitive, a strong skill set, portfolio, and relevant experience increase the chances of securing a data scientist position. Staying updated with emerging technologies and industry trends enhances job prospects in this rapidly evolving field. 

    Step 6: Ace the Interview 

    • A brief overview of the interview process + Tips 

    The interview process for data scientist roles typically involves multiple stages: initial screening, technical assessment, on-site/panel interviews, and behavioral/cultural fit interviews. 

    Tips to Ace the Interview: 

    1. Prepare Technical Knowledge: Review data science concepts, algorithms, and programming skills. Explain your approach to problem-solving and showcase project experience. 
    2. Practice Problem-Solving: Solve coding challenges and work on data analysis tasks. Emphasize analytical thinking and clear communication of your thought process. 
    3. Research the Company: Understand the company's data science initiatives, industry, and recent projects. Show enthusiasm and interest during the interview. 
    4. Communicate Clearly: Articulate ideas and methodologies effectively. Explain complex concepts in a concise and understandable manner. 
    5. Ask Questions: Demonstrate curiosity by asking insightful questions about the company, team, and projects. Show genuine interest and engagement. 

    Step 7: Engage in data science projects to hone your real-world data skills

    It has become essential to stand out from the competition and get hired by a top organization by showcasing the necessary Data Science skills. The projects are helpful in this situation. The majority of the topics covered in the Data Science Bootcamp review are useful for developing abilities, and End-to-end projects expose you to a real-world working environment as well. 

    • Kaggle 

    Data scientists and machine learning enthusiasts can connect online at Kaggle. Users of Kaggle can work together, access and share datasets, use notebooks with GPU integration, and compete with other data scientists to solve data science problems

    • Tableau Or Power BI  

    Power BI can only manage a small amount of data, but Tableau can handle a large volume with greater performance. Power BI is used by novice and experienced users, whereas analysts and experienced users primarily prefer Tableau for their analytical needs. 

    • Other Resources  

    Select a programming language in which you feel most at ease. However, the language of choice ought to be one of the popular ones, like Python, R, or Scala. Additionally, confirm that the dataset you are utilizing is error-free. 

    Who can Become Data Scientist?

    Data science is a field that welcomes individuals from diverse backgrounds. While certain skills and knowledge are necessary, there is no rigid set of criteria for who can become a data scientist. Here are some key requirements for aspiring data scientists: 

    Data Scientist Requirements:

    1. Strong Analytical and Problem-Solving Skills 
    2. Mathematics and Statistics Proficiency 
    3. Programming Skills 
    4. Domain Knowledge 
    5. Curiosity and Continuous Learning 
    6. Effective Communication Skills 

    Data Analyst vs Data Scientist: What's the Difference?

    A data analyst examines already-existing data, whereas a data scientist develops novel techniques for gathering and analyzing data that analysts can use. If you are interested in numbers, statistics, and computer programming, this career path can be a good fit. 

    Data scientists are accountable for these duties as well as a wide range of others. It is assumed that some people who begin with uncertainty about how to become data scientists from scratch and learn the necessary skills go on to become experts who are able to use cutting-edge analytics tools to examine big data and have the training required to develop novel algorithms to address specific problems. 

    They could also be asked to explore data without trying to solve any particular issue. In that case, they must comprehend the information and the company well enough to come up with queries and provide information to business executives to enhance corporate operations, goods or services, or client relationships. 

    Data Scientist Salary and Job Growth

    The average yearly wage for a data scientist is Rs. 698,412. Data scientists with one to four years of experience may expect to make an average income of Rs. 610,811. Data scientists' employment is expected to increase by 36% between 2021 and 2031, which is substantially faster than the average for all occupations. Over the next ten years, there are expected to be, on average, 13,500 job openings for data scientists. 

    CountryAverage Salary
    United States$1,04,115
    United Kingdom£55,697
    CanadaCA$91,713
    AustraliaA$1,11,750
    Germany€64,000
    IndiaINR 12,00000

    Is It Possible to Work as a Data Scientist Without a Degree?

    Data Science Specializations  

    Thus, the response is YES! Data scientists are now employable without a formal education. You only need to follow these five simple steps from DataFlair to start your career as a data scientist without a master's degree. 

    Data professionals are in great demand. According to the US Bureau of Labor Statistics (BLS), from 2020 to 2030, the employment of statisticians is predicted to increase by 33% and that of data scientists and mathematical scientists by 31%. That is a lot faster than the 8 percent average growth rate for all jobs. 

    Earn a Data Science Degree to Stand Out in the Data Scientist Crowd

    One of the most crucial things that interviewers look for is practical experience, and almost all applicants will have at least some real-time knowledge. Therefore, you must get domain-focused practical experience to stand out in a crowd. 

    One of the talents you'll need to master to improve your chances of being chosen is effective communication. Even if you are an expert with data, you will not be able to operate effectively if you are unable to explain to the company's managers what the data means or how it works. 

    Closing

    With more applications in more sectors, data science has a bright future with countless prospects. In addition to artificial intelligence and machine learning, data science will aid businesses in making higher-level judgments that are more informed. Furthermore, KnowledgeHut's Data Science Course subjects can assist you with developing the appropriate skill set if you're considering how to become a certified data scientist.

    How to Become a Data Scientist FAQs

    1Can I become a data scientist with no experience?

    Yes. Anyone can become a data scientist with no experience, but one needs to possess the right skills.

    2Is it easy to become a data scientist?

    Being a data scientist can place you on the cutting edge of new technological advancements while also being academically challenging and analytically satisfying.Being a data scientist can place you on the cutting edge of new technological advancements while also being academically challenging and analytically satisfying. 

    3What is the length of training to become a data scientist?

    A person who has never coded before and/or has no mathematics training often needs to put in 7 to 12 months of intense study to become a good data scientist. 

    4I don't have a degree. Can I become a data scientist?

    Despite the widespread misconception that you need a degree in computer science or a doctorate to work as a data scientist, analysis of job postings has shown that this is simply inaccurate, with the vast majority of employment not requiring such credentials. 

    Profile

    Amit Pathak

    Author

    Amit is an experienced Software Engineer, specialising in Data Science and Operations Research. In the past five years, he has worked in different domains including full stack development, GUI programming, and machine learning. In addition to his work, Amit has a keen interest in learning about the latest technologies and trends in the field of Artificial Intelligence and Machine Learning.

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