“A data scientist combines hacking, statistics, and machine learning to collect, scrub, examine, model, and understand data. Data scientists are not only skilled at working with data, but they also value data as a premium product.”
Introduction
Data is sometimes referred to as the “new oil,” to be mined and purified to be profitable. The data scientist’s role is to alter and convert complex data structures into a high-yielding output, which helps industries to flourish.
The data scientist job is one of the notable profit-making jobs in the 21st century. Later on, the concept and the scenario improved and advanced, which reflects the current status of the data scientist jobs in the market. The number of students pursuing a data science degree or any related field has steadily increased in recent years. Many students enrolled for Master’s in Data Science, and Computer Science, which shows a growth of 17% between 2011 and 2022. [ ed ]
Data scientists are in high demand in the current job market with wide employment opportunities projected to grow faster than other average occupations. Its increasing demand is due to its handsome scope for getting highly paid in the modern AI-influenced world, which excites people to search “How to become a data scientist”. The data science job positions continue to rise, estimating 36% of the growth rate until 2031, which significantly shows the importance of data scientists in business. [ 365datascience ]
What is a Data scientist?
A Data Scientist is a data expert who collects, analyzes, and interprets complex data sets to help organizations in decision-making and acquire skills. He/ she can work in fields like computer science, mathematics, statistics, predictive modeling, etc to unlock new opportunities and strategies.
Step By Step Guide To Become A Data Scientist
The following list outlines a pathway toward becoming an expert-level data scientist. However, there are many different degrees, skill-building tools, and advanced learning options available for those who are interested in this field. One way to answer the question of how to become a data scientist is:
📚 1. Begin with a Preparatory Course
It is best to start with an introductory course on a data science degree if you are unaware of this field. It helps you to gain an overall idea about the analytical data systems, mathematical and statistical skills, and other tools and technologies needed in this industry, and about the know-how of your day-to-day working life.
📊 2. Master Data Science Basics
The basic requisites for “How to become a data scientist” include:
- Know the basics of data science and its scope.
- Data science process and knowledge of common tools used for Data Science application development.
- Functions of Python libraries & packages.
- Data science education requirements include concepts and methods to find and apply solutions to real-world problems and methods to communicate these solutions effectively.
When considering becoming a data scientist, it’s better to evaluate yourself on what you know and what you don’t about the fundamentals of data scientist education and its requirements. Starting simple is never a waste of time!
Areas of focus should be:
- Mathematical and Statistical Analysis: This includes analyzing the collected complex data structures and presenting the result as a product of unbiased and accurate statistical methods like hypothesis testing and mathematical knowledge of probability, linear algebra, etc.
- Programming: Writing code and building software are key skills in many areas of data science. PYTHON and R are widely used for data analysis.
- Data Visualization: Presentation of data through visually appealing techniques using tables, graphs, charts, or dashboards using tools like Tableau for quick and easy comprehension for both technical and non-technical individuals.
- Machine Learning: Using algorithms and other effective skills to create software, that is acquired from the data it ingests and uses it to spontaneously adapt to its unique situations and nature.
🎓 3. Meet the Educational Requirements
A bachelor’s degree in Data Science skills, Mathematics, Statistics, or computer-related fields is needed for professionals.
According to the US Bureau of Labour Statistics BLS, some employers demand a Master’s or doctoral degree for some data science positions. This helps the candidate in a deep understanding of the core concepts and skills.
The reputed Universities can also provide internship and networking opportunities that help to boost resumes.
If the Bachelor’s Degree doesn’t match the aspirant’s needs, they should focus on developing a data science degree and skills through continued education like online short courses or boot camps. A certified course or enrolling in a data science boot camp under data science online learning platforms like Coursera, Udemy, etc. helps to get a thorough practical education in the fundamentals and the responsibilities of a data scientist.
🧭 4. Plan Your Career Specialization
Once you have the fundamentals under control, you can begin to plan a path for yourself based on data scientist education requirements and develop a niche or specialization that can give you an edge when you’re in the job market.
🛠️ 5. Advance your specialized and Essential Skills
Developing your data science projects to manifest the skills below is a great way to showcase your ability.
TECHNICAL SKILLS
- Programming Languages. A data scientist is expected to know Python and other languages like R or SQL.
- Data Mining and Data Analysis. Collecting relevant and trustworthy data from different sources.
- Querying and analyzing: the structured data can help to identify and translate it into actionable insights.
- Data Visualization. One of the key data scientist skills is the ability to use and apply Data visualization tools like Tableau, Microsoft Excel, and Google Charts to display data understandably and appealingly.
- Statistical Analysis: Unbiased insights based on numbers as the product of statistical analysis help organizations make proper business plans and decisions
- Machine Learning [ Deep Learning & Natural Language Processing]: NLP is widely used for virtual assistants like Apple’s Siri or Amazon’s Alexa.
- Algorithms: Algorithms perform various computations to solve business problems according to their purpose.
SOFT SKILLS
- Analytical Thinking. Building insightful questions requires an analytical outlook.
- Business Awareness. Understanding proper business needs to improve the quality and efficiency of work by making up profitable ideas.
- Critical Thinking. They are helping to sort out inconsistent, irregular, and odd data sets, before the data cleaning and wrangling process.
- Workability and Flexibility. Adapting to the required information by overcoming issues and turning them into advantages.
- Collaboration And Cooperation. Collaborating with all the teams and departments from the collection of data to sharing the findings
- Clear up Problems. Figuring out problems skillfully and logically will benefit any team.
- Communication. Conveying messages rapidly and clearly for the smooth, happy, and healthy working of the team and the project.
- Patience And Perseverance. Tolerating the problems and adapting to the time-consuming process, with continued steady belief and efforts to reach meaningful conclusions.
🧰 6. Acknowledge Yourself With the Essential Data Science Tools
If you want to know how to become a data scientist, get thorough knowledge of these data science tools.
- Apache Spark and SAS are designed for efficient computation of vast and complex data sets.
- Tableau is widely used to make visually appealing and interactive reports and dashboards.
- MATLAB is commonly used for creating algorithms and other data scientist skills.
- Python is used in the data science field globally because of its simplicity and readability.
- R is extensively used by statisticians.
💼 7. Gain Practical Experience by Working on Projects and Building Your Portfolio
Creating an impressive portfolio that demonstrates your ability, passion, experience, and efficiency in data handling and analyzing, in a creative way is an advantage.
- Projects: Engage in personal or open-source projects to practice your skills and create a portfolio. This could be any projects, accomplishments, hackathons, competitions, or coding challenges showcased through a personal website (or) by using GitHub/Kaggle.
- Internships: Acquire experience in a data-centered environment.
- Data Science Competitions: Kaggle is a platform that allows learners to solve real-world problems and helps them to learn and excel in data science fields.
🤝 8. Build a Strong Network
Building a wide network through meetups, or conferences is the best way to socialize and connect with professionals in the field to develop your data scientist educational requirements, work on your passion projects, and find a perfect data science job.
- Platforms: Linkedin, and Reddit are examples of such platforms.
- Conferences and Meet-Ups: Attending conferences, webinars, and meet-ups frequently, helps in getting acknowledged about the process and also helps in updating about the current status.
📜 9. Consider Certifications and Pursue a Data Science Internship
Though not required, certain certifications help you portray yourself as experienced and also can lead to more job opportunities.
Notable Data Science Certifications
- Certified Data Scientist (CDS): This certification is offered by the DSCNY(Data Science Council Of New York), and it certifies knowledge in several areas of data science.
- Certified Analytics Professional (CAP): Offered by the Institute for Advanced Analytic Professionals, this certification tells employers that applicants know how to use analytics and data science.
- Certified Machine Learning Scientist (CMLS), Cloudera Certified Data Scientist (CCDS), etc. are other examples.
Vendor-Specific Certifications
- Google Cloud Certified Professional Data Engineer: Enables to design and build data processing systems on the Google Cloud platform.
- Azure Data Scientist Associate: Validates expertise around Azure data services and how well the aspirant can build machine learning models using the proper tools.
- The AWS Certified Data Analytics — Specialty, CCA Data Analyst — the Cloudera Certified Data Analyst, etc., fall under this category.
Professional Associations:
- The Institute for Analytics Professionals (IAP) offers multiple certifications for certified IAP Data Scientists. The Data Science Council of New York (DSCNY) and the Data Science Council of India (DSCI) are the other associations providing Data Science Certification (DSC).
Online Platforms:
- Coursera provides specialization certifications in Data Science, Business, and Humanities.
- edX: Offers certificates in Computer Science, Engineering and Management.
- DataCamp: Offers online sessions in data science, analytics and programming. It also provides courses in Machine Learning including SQL, PYTHON and R.
To get first-hand practical experience, that complements your field of study, apply for internships in a junior data scientist or analyst job positions.
🗣️ 10. Prepare for and Ace the Data Science Interview
When it comes to getting data science interviews, then having a strong resume and cover letter are key factors. Customize your cover letter and resume for the position you are interested in, showing details of experience and achievements that highlight data science skills. Be sure to use keywords specific to the job description.
Research thoroughly about the company you have decided to work in, the professionals working there; both in technical and non-technical fields, the role and responsibilities of each data scientist resignations, and typical but relevant data science interview questions.
What Does A Data Scientist Do?
A data scientist analyzes the accessed complex data structures and interprets them by extracting workable insights to help organizations make informed and data-driven decisions.
Their role consisted of creating data models, engineering data pipelines, and supervising extract, transform, and load (ETL).
Data scientists build predictive models by designing machine learning models and evaluating their performance using metrics to improve their accuracy.
They provide suitable solutions after identifying and studying the problems and then hand over the results to the team members for further decision-making.
What Is The Role Of The Data Scientist?
A data scientist’s role is to accumulate raw and complex datasets to help businesses identify their problems and help them make better decisions at the right time. When asked how long it takes to become a data scientist, we conclude by following the process given below.
Gathering and Analyzing the Data
Data scientists collect and analyze structured and unstructured data from various sources like Databases, APIs, etc, using appropriate tools like machine learning languages, algorithms, and statistics. ‘How long does it take to become a data scientist’ starts from here, i.e., from gathering complex data structures followed by analyzing and interpreting.
Identifying Trends and Patterns
Data scientists recommend data-driven approaches to optimize the running processes through effective patterns and relevant trends.
Data Visualization
Data scientists construct useful charts, graphs, and diagrams to showcase the trends and predictions to improve model accuracy and business outcomes.
Communication
Data scientists convey the information, messages, findings, and insights to both technical members and non-technical stakeholders clearly and understandably.
Problem-solving
Data scientists apply statistical and mathematical analysis, machine learning, etc to extract insights to solve business problems and to make smart decisions.
Collaboration
Data scientists work with cross-functional teams, such as Engineering and IT professionals, and legal teams, to identify and resolve technical issues.
Professional Development
Data scientists collaborate and mentor the talented people within their team, to foster professional interpersonal growth as well as to enhance the proficiency of the team.
Current and Updated
Data scientists are well acknowledged with the present scenario with the latest and newest trends and developments in data science and technology.
Data Scientist Qualification And Requirements
According to the job industry, the query on how to become a data scientist is answered by several factors. These include the employer, the job position, and the data scientist qualifications for professionals; which may vary.
A Bachelor’s degree in Computer Science, Statistics, or any related field is essential for Data analysts and data engineers.
A Master’s degree in Data Science, or related disciplines like Machine Learning, Artificial Intelligence, etc., is required for a data scientist or Computer and Information Research scientist, as demanded by most employers in the business.
If someone completed a Bachelor’s unrelated to this field, they should go for an online course related to the data science field under a reputed platform like Coursera, edX, Udemy, etc. or a data science boot camp.
Technical skills proficiency is one of the data scientist skills needed to handle programming languages like SQL, Java, etc.
A deep knowledge of machine learning algorithms is mandatory.
Communication skills are very important in all sorts of data scientist jobs.
Certain certification courses like DASCA [Certified Data Scientist], Google Professional Data Engineer, etc. can also help to master and qualify for data scientist jobs.
Data Scientist Jobs And Salary Growth
Data scientist jobs are fundamentally important and inevitable in the business environment because they help identify and address business issues in a timely, methodical, rational, and efficient manner. In this high-tech, AI-influenced world, data scientist positions are the most demanding and fastest-increasing, with an average of 36% growth in the area between 2021 and 2031. A data scientist typically makes $125,426 a year, plus an additional $16,862 in benefits, according to (builtin).
The global data science job market is predicted to grow significantly in the coming years, reaching $103 Billion in 2027 compared to $84 Billion in 2024. (Global Data Revenue Forecast)
The shortage of skilled professionals and the high average income of data scientist jobs put them in high demand in the job market, and hence data scientists have a bright future. (365datascience)
| SL. Number | Job Title | Responsibilities | Average Annual Income |
|---|---|---|---|
| 1 | Data Scientist I | • Assistance in the execution of objectives. • Collection, synthesis, and Analysis of data. • Perform Routine procedures • Create Reports after scientific and synthetic analysis of data. | $125,446-$142,324 (builtin) |
| 2 | Entry-level Data Scientist | • Attend weekly meetings to discuss operations • Participate in sessions to improve leadership skills • Connect with executives and senior leaders. • Participate in team building and job shadowing events. • Create reports and dashboards. • Use SQL, R, or Python to organize data. | $31.76 – $42.34 per hour |
| 3 | Numerical Modeler | • Professional presentations • Marketing material • Website copy • Breaking down complicated texts • SEO titles and meta descriptions • Project briefs/directions | $60,221 |
| 4 | Statistician | • Ensuring the system’s effectiveness in company policies, procedures, and task assignments. • Contributing to the design and development of statistical analysis plans. • Analysing the programming environment. • Collaborating with data management to establish study data processing. • Performing intricate data checks. • Produce tables, figures, and data listings for verification study and clinical study records. • Communicating basic statistical principles to the project team. | $64,000 – $128,000 |
| 5 | Algorithm Engineer | • Take part in creating algorithms. • Carry out discovery analysis. • Participate in System V & V, Product Test and Evaluation, Requirements Analysis, Risk Analysis, etc. • Gather, arrange, examine, and record test results. | $95,500 – $190,900 |
Data scientist jobs will continue to be the fastest-growing jobs in 2024, according to the (US Bureau of Labor Statistics bls). It is estimated a projected increase in the field of data scientist jobs from 2022 to 2033, is about 35% to 36 percentage with an average of 20800 new data scientist job openings per year. To become a data scientist everybody should acquire some tech, math, and statistic skills and competencies along with relevant machine learning programming languages. Hence, nowadays all people are rushing towards acquiring these data scientist skills to become a data scientist as it hits the jackpot for a lucrative career.
Everyone would be rushing for this money-making job deal for tomorrow and for the days after when looking ahead to the job market status, heading in 2024.
FAQs
A bachelor’s degree in Computer science, Engineering, Mathematics, Data science, or a related field is usually required to become a data scientist. A master’s degree in data science, Machine Learning, Artificial Intelligence, or a related discipline is sometimes demanded by employers.
Yes, there are many online courses designed for aspiring data scientists, like the IBM Data Science Professional Certificate under Coursera, Data Science MicroMasters under edX, Machine Learning A-Z under Udemy, etc. These platforms offer both free and paid courses and most of them issue certificates too.
A fixed timeline is not decided to become an efficient data scientist. It varies according to your prior knowledge of data science basics, formal education, experience, and sector of choice. However, the answer to the question of how long does it take to become a data scientist is dependent on your availability and time. An average duration of 4-6 years is primarily needed to complete both undergraduate and graduate courses and an additional timeline is also essential to complete a self-learning course.
No, a formal bachelor’s or Masters is not needed to become a data scientist. Employers check the practical skills and experience in data handling, solving, and implementing knowledge of the aspirant.
