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How to Become a Data Scientist

By Andrea Delgado

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In the age of big data, the role of a data scientist has emerged as one of the most coveted and lucrative careers in the tech industry. If you’ve ever wondered how to embark on this exciting journey, join us as we uncover the essential requirements, explore the enticing salary prospects, and shed light on the promising career outlook for aspiring data scientists.

Career Summary

Data Scientist Salary

The salary range for data scientists can vary significantly depending on different factors. Here is a general overview of a data scientist’s salary range from entry-level to executive positions according to data from glassdoor.com:

  • Entry Salary (US$114k)
  • Median Salary (US$127k)
  • Executive Salary (US$163k)

The average income of data scientists in the United States is generally higher than the national average income for all occupations, which was around US$61,900 in May 2022, according to the U.S. Bureau of Labor Statistics

Data Scientist Job Description

Data scientists use their expertise in mathematics, statistics, and programming to extract valuable insights from vast amounts of data. They employ advanced analytical techniques to identify patterns, trends, and correlations, which help businesses make data-driven decisions. Their role involves data cleaning, analysis, modeling, and developing machine learning algorithms to solve complex problems and optimize processes.

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Starting a Career in Data Science

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Data Scientist Career Path

Even though it can depend on the organization and industry, the career progression of a data scientist typically looks like this:

  • Entry-Level Position: As a fresh graduate or someone new to the field, you may start as an entry-level data scientist. You’ll gain hands-on experience in data analysis and programming.
  • Junior Data Scientist: Here, you will be assigned more complex projects and you’ll develop your skills in data manipulation, statistical analysis, and machine learning techniques.
  • Senior Data Scientist or Lead Data Scientist: As a senior data scientist, you will lead teams and provide strategic insights to drive business decisions.
  • Data Science Manager or Director: In this role, you’ll oversee data science strategies and contribute to high-level decision-making within organizations.
Data Scientist Career Progression

Pros:

  • Cutting-Edge Technologies
  • High-level of Autonomy
  • Excellent Career Prospects
  • Competitive Salaries

Cons:

  • Working with large and complex datasets.
  • Communication challenges when explaining insights to non-technical stakeholders.
  • High expectations and pressure.

Useful Skills to Have as a Data Scientist

  • Mathematics and Statistics
  • Programming
  • Machine Learning
  • Data Visualization
  • Problem-Solving and Critical-Thinking
  • Effective Communication

Popular Data Scientist Specialties

How to become a Data Scientist

Data Scientist 7 Steps to Career

Let’s explore the common steps to becoming a data scientist. By following these steps and continually expanding your knowledge and experience, you can become a data scientist in a particular domain or skill set and unlock exciting career opportunities in the field of data science.

Obtain a Solid Educational Foundation

Start by earning a bachelor’s degree in a relevant field such as computer science, mathematics, statistics, or data science. This provides a strong foundation in quantitative methods and programming, which are crucial for a data scientist.

Average Time and Costs of Obtaining a Degree in Data Science

The average time and costs of obtaining a degree in data science can vary depending on several factors, including the type of degree program, the institution, and whether you pursue the degree on a full-time or part-time basis. Here is a general overview:

Bachelor’s Degree
  • Time: A typical bachelor’s degree in data science takes around 4 years of full-time study to complete.
  • Costs: The costs of a bachelor’s degree can vary significantly depending on factors such as the country, institution, and whether it’s a public or private university. In the United States, for example, the average cost of a bachelor’s degree ranges from $10,000 to $64,380 per year for tuition fees, excluding additional expenses like housing, books, and supplies.
Master’s Degree
  • Time: A master’s degree in data science usually takes around 1 to 2 years of full-time study to complete. Some programs offer accelerated or part-time options, which can extend the duration.
  • Costs: The costs of a master’s degree in data science can vary widely. In the United States, for instance, tuition fees for a master’s program in data science can range from $20,000 to $60,000 per year. It’s important to consider additional expenses such as living costs, and any required software or equipment.
Ph.D. Degree
  • Time: A Ph.D. in data science typically takes around 4 to 5 years of full-time study beyond the bachelor’s degree. This includes coursework, research, and the completion of a doctoral dissertation.
  • Costs: Ph.D. programs are often fully funded through research or teaching assistantships, scholarships, or grants. This means that students typically receive a stipend and have their tuition fees waived. However, it’s important to consider other expenses related to living costs and research-related activities.

It’s worth noting that these estimates are general averages, and the actual time and costs can vary significantly based on the specific program and institution you choose. Additionally, there are alternative options to formal degree programs, such as online courses and boot camps, which can offer more flexibility in terms of time and costs.

Develop Programming and Data Manipulation Skills

Gain proficiency in programming languages commonly used in data science such as Python or R. Learn how to manipulate and analyze data using libraries and frameworks specific to data science, such as pandas, NumPy, and scikit-learn.

Useful Skills to Have as a Data Scientist

To excel as a data scientist, several key skills prove invaluable in the field:

  • Strong Foundation in Mathematics and Statistics: Strong foundation in mathematics and statistics is crucial. Proficiency in areas such as linear algebra, calculus, probability, and statistical analysis enables data scientists to effectively understand and apply advanced analytical techniques.
  • Programming Skills: Data scientists commonly work with programming languages such as Python or R to manipulate, clean, and analyze data. Fluency in SQL is also important for working with databases and querying large datasets.
  • Machine Learning: A fundamental skill for data scientists. Understanding different machine learning algorithms, such as decision trees, random forests, and neural networks, enables them to build predictive models and extract insights from data.
  • Data Visualization Skills: These are also valuable. The ability to present complex data in a visually appealing and understandable manner allows data scientists to communicate insights to stakeholders and make data-driven recommendations effectively.
  • Strong Problem-Solving and Critical-Thinking Abilities: are necessary to tackle complex data-related challenges. Data scientists must be able to formulate the right questions, identify patterns, and develop innovative approaches to solve problems.
  • Effective Communication Skills: effective communication skills are vital. Data scientists need to communicate their findings and insights to both technical and non-technical audiences. Being able to convey complex concepts clearly and concisely facilitates collaboration and ensures stakeholders understand the value of data-driven insights.

Gain Practical Experience With Real-World Data

Work on projects that involve real-world datasets and problem-solving. This could include internships, personal projects, or participating in Kaggle competitions. Practical experience helps develop skills in data cleaning, feature engineering, and the application of machine learning techniques.

Internship Opportunities for Data Scientists

Internship opportunities for data scientists are abundant and can provide valuable hands-on experience in the field. Many companies, ranging from tech giants to startups, offer internships specifically tailored to data science roles.

Here are some internship opportunities for data scientists:

  • Technology Companies: Large technology companies such as Google, Microsoft, Amazon, and IBM often have internship programs that include data science positions. These internships offer exposure to cutting-edge technologies, mentorship from industry experts, and the opportunity to work on real-world data science projects.
  • Data-Driven Companies: Many companies across various industries, such as finance, healthcare, e-commerce, and telecommunications, rely heavily on data-driven decision-making. Interning at these organizations can provide practical experience in applying data science techniques to solve industry-specific problems. Examples include banks, insurance companies, healthcare providers, and retail companies.
  • Research Institutions: Universities, research labs, and government organizations often have internships available for aspiring data scientists. These internships may focus on research projects, data analysis, or machine learning algorithm development. Interning at research institutions can offer exposure to cutting-edge research and collaboration with leading experts in the field.
  • Consulting Firms: Consulting firms with a data science practice also offer internship opportunities. These firms work with clients from various industries, providing data-driven solutions and insights. Interning at a consulting firm can provide exposure to diverse projects and industries, as well as the chance to work in a team-oriented and client-facing environment.
  • Startups: Startups, particularly those in the tech industry, often offer internships that provide a fast-paced and entrepreneurial experience. Working at a startup allows interns to contribute to multiple aspects of data science, gain exposure to a wide range of technologies, and make a direct impact on the company’s growth.

Stay Updated with Industry Trends and Technologies

Data science is a rapidly evolving field, so it’s important to stay updated with the latest tools, algorithms, and advancements. Follow industry blogs, research papers, attend conferences, and engage with the data science community to keep your knowledge current.

Build a Strong Portfolio

Develop a portfolio showcasing your data science projects and achievements. Highlight your problem-solving abilities, technical skills, and the impact of your work. A portfolio demonstrates your expertise and helps when applying for data science positions.

Networking and Professional Development

Engage with the data science community by attending meetups, conferences, and networking events. Connect with professionals in the field, participate in discussions, and seek mentorship opportunities. Continuous learning and professional development are essential to stay competitive and enhance your skills.

Pursue Advanced Degrees or Certifications (optional)

While not always necessary, pursuing a master’s degree or obtaining specialized certifications in data science can provide a deeper understanding of the field and make you stand out among candidates.

Data Scientist Certifications You Should Consider

There are several certifications available for those wondering how to become a data scientist that can demonstrate your skills and knowledge in the field.

Here are a few notable certifications:

  • IBM Data Science Professional Certificate: Offered by IBM through Coursera, this certification program provides a comprehensive understanding of data science tools and methodologies. It includes courses on Python, data analysis, data visualization, machine learning, and applied data science projects.
  • Microsoft Certified: Azure Data Scientist Associate: This certification from Microsoft validates skills in designing and implementing solutions using Azure technologies. It covers topics like machine learning, natural language processing, computer vision, and data engineering.
  • SAS Certified Data Scientist: This certification by SAS validates proficiency in SAS programming, data manipulation, predictive modeling, and machine learning. It includes both multiple-choice exams and a practical project component.
  • Google Cloud Certified – Professional Data Engineer: This certification focuses on data engineering and analytics in the Google Cloud Platform (GCP) environment. It covers topics like data ingestion, transformation, processing, and visualization using GCP tools and services.
  • Cloudera Certified Data Scientist: This certification validates skills in building and deploying scalable machine learning models using the Cloudera platform. It covers topics like data exploration, feature engineering, model evaluation, and deployment.
  • SDS or PDS by Data Science Council of America (DASCA): These international certificates are ideal for experienced data scientists who desire to deepen their knowledge.

It’s important to note that while certifications can enhance your credibility and demonstrate your knowledge, they are not a substitute for practical experience. Employers often value hands-on experience and real-world projects in addition to certifications. It’s beneficial to choose certifications that align with your career goals and the specific tools or platforms you are interested in working with.

Can I Become a Data Scientist through Online Education?

Yes, it is possible to become a data scientist through online education. Online education has become increasingly popular and widely accepted, providing flexible learning options for individuals interested in pursuing a career in data science. The principal benefit of getting an online Bachelor’s Degree in Data Science is flexibility. This is great for students who already have a busy schedule like working professionals and parents. Top of mind for in-person learning, meanwhile, is the structured coursework and classroom engagement. In-person learning is beneficial for those who learn best when working with others and with a fixed schedule. Your learning experience can vary greatly by a program’s delivery mode.

What are some Web Resources to Learn Skills to Become a Data Scientist?

There are several web resources available that can help you learn the skills necessary to become a data scientist.

Here are some popular ones:

  • Kaggle: Kaggle is a popular platform for data science competitions and learning. It offers tutorials, datasets, and coding challenges to help you improve your data science skills. You can participate in competitions and collaborate with other data scientists to enhance your practical experience.
  • DataCamp: DataCamp focuses specifically on data science and offers interactive courses in various topics, including data manipulation, machine learning, and data visualization. The platform provides hands-on coding exercises and projects to reinforce your learning.
  • Towards Data Science: Towards Data Science is a popular online publication that covers a wide range of data science topics. It features articles, tutorials, and case studies written by data science practitioners and experts. It’s a great resource for staying up to date with the latest trends and learning from real-world examples.
  • GitHub: GitHub is a platform for version control and collaborative coding. It hosts numerous open-source data science projects and repositories where you can explore code, learn from others’ work, and even contribute to projects.
  • edX: edX is a platform that provides online courses from top universities and companies, such as Harvard University and IBM. You can find courses on data visualization, data analysis, and programming languages like Python and R.

Remember, learning data science is a combination of theory and hands-on practice. These resources provide a starting point, but it’s essential to apply what you learn through real-world projects and practice with datasets to solidify your skills.

What’s the Career Outlook for Data Scientists?

The career outlook for data scientists looks highly promising. The demand for skilled data scientists continues to grow as organizations increasingly recognize the value of data-driven decision-making and the potential for leveraging data to gain a competitive advantage.

According to the BLS, employment of data scientists is projected to grow 36 percent from 2021 to 2031, which is much faster than the average for all occupations. About 13,500 openings for data scientists are projected each year, on average, over the decade.

Data Scientist Popular Career Specialties

Job Opportunities: Popular Data Scientist Specialties

Within the field of data science, there are several popular specialties that professionals may choose to specialize in based on their interests and career goals.

Some of the notable data scientist specialties include:

  • Machine Learning Engineer: Machine learning engineers focus on developing and implementing machine learning algorithms and models. They work on designing and training models to make accurate predictions and automate decision-making processes.
  • Natural Language Processing (NLP) Specialist: NLP specialists concentrate on understanding and interpreting human language through computational methods. They work on tasks such as sentiment analysis, text classification, language translation, and speech recognition.
  • Deep Learning Specialist: Deep learning specialists specialize in working with deep neural networks, a subset of machine learning algorithms inspired by the structure of the human brain. They focus on developing models capable of handling complex patterns and large-scale data, commonly used in image recognition, speech recognition, and natural language processing.
  • Data Visualization Expert: Data visualization experts specialize in creating visually appealing and informative representations of data. They leverage various tools and techniques to present complex datasets in a visually intuitive manner, enabling stakeholders to gain insights and make data-driven decisions more effectively.
  • Data Engineer: Data engineers focus on the design, construction, and maintenance of the systems and infrastructure required for data processing and analysis. They work on data pipeline development, warehousing, and optimizing data storage and retrieval processes.
  • Business Analyst: While not exclusively a data scientist role, many data scientists specialize as business analysts. They work closely with stakeholders to understand business objectives, identify data-driven opportunities, and translate insights into actionable strategies that drive organizational growth and success.

Top Industries That Hire Data Scientists

Data scientists are in high demand across a wide range of industries, such as:

  • IT: Tech giants like Google, Facebook, Microsoft, Amazon, and Apple often have dedicated data science teams working on projects related to artificial intelligence, machine learning, and data analytics.
  • Finance: Banks, insurance companies, investment firms, and fintech startups hire data scientists to analyze financial data, build predictive models, detect fraud, and optimize investment strategies.
  • Retail: Online retailers such as Amazon and e-commerce platforms hire data scientists to analyze customer behavior, personalize recommendations, optimize pricing strategies, and improve supply chain management.
  • Healthcare: Healthcare providers, pharmaceutical companies, and biotech firms employ data scientists to analyze patient data, conduct clinical research, develop predictive models for disease diagnosis, and support drug discovery and development.
  • Entertainment: In the entertainment industry, a data scientist may analyze large volumes of user data to gain insights into audience preferences, viewing habits, and engagement patterns. They also use advanced statistical techniques and machine learning algorithms to develop personalized content recommendations and optimize advertising campaigns.
  • Manufacturing: Manufacturing companies utilize data scientists to optimize production processes, forecast demand, minimize downtime, and enhance supply chain efficiency through predictive analytics.
  • Government Agencies and Research Institutions: Government departments, research organizations, and academic institutions employ data scientists to work on projects related to public policy, urban planning, climate research, and social sciences.

What is the Work-Life Balance of a Data Scientist?

The work-life balance of a data scientist can vary depending on the company and specific role. In some cases, data scientists may experience periods of intense work and long hours, particularly during project deadlines or when dealing with complex data challenges. However, many organizations strive to promote a healthy work-life balance, providing flexible work arrangements, remote work options, and emphasizing the importance of employee well-being to ensure a sustainable and fulfilling career in data science.

Should I become a Data Scientist?

Data science has transformed the way we live over the last few decades. Data scientists are in demand in almost every field, from agriculture to healthcare and commerce.

It’s a rewarding career that lets you help professionals and businesses make informed and intelligent decisions. In addition to that, it offers a competitive salary and, sometimes, a flexible schedule. Now that the tech industry is growing, a career in data science is increasingly viable. 2023 is the perfect year to enroll in a bachelor’s degree in data science.

However, deciding whether to embark on a path as a data scientist is an individual decision influenced by your personal interests, skills, and professional aspirations.

Careers Related to Data Scientist


Andrea Delgado

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