Data science is often regarded as a prestigious and exciting field that combines statistics, programming, and business insights to unlock the potential of data. But what does a data scientist actually do? How can someone break into the field, and what skills are most critical to learn? Haden Pelletier, a seasoned data scientist, answers these common questions in an article published on Towards Data Science, providing a roadmap for anyone interested in pursuing a career in data science.

For more tech and career insights, explore the Times of Tech career section.

What Does a Data Scientist Do?

The role of a data scientist can vary widely depending on the industry, company, and even the team they’re working with. However, most data science jobs generally encompass three main areas:

1. Data Engineering and Databases:
   Data scientists spend a significant portion of their time handling databases, including tasks like loading and querying large datasets, creating data pipelines, and preparing data for analysis. Pelletier emphasizes that SQL is essential in this area for tasks like building machine learning models and managing databases.
2. Data Analytics and Visualization:
   Visualization and analytics are key to data science as they translate complex findings into accessible insights. Data scientists use tools like Tableau, Python, and JavaScript to present data visually, often creating dashboards and charts that track metrics, such as sales growth or market trends.
3. Machine Learning and Predictive Modeling:
   Machine learning (ML) is perhaps the most well-known aspect of data science. ML models are developed to analyze historical data and predict future trends. However, this process involves extensive data cleaning and preparation before models can be effectively trained.

At times, data scientists may focus on only one of these areas, but they’re generally familiar with all three and switch between them throughout their careers.

How Can You Enter the Data Science Field?

While a traditional college degree in data science or a related field remains one of the easiest pathways, Pelletier highlights multiple ways to start a career in data science:

1. Degree in Data Science or Related Fields:
   A formal degree provides structure and covers essential topics, making it a strong choice for those new to the field. However, self-taught data scientists also thrive by pursuing online courses, certifications, and hands-on projects.
2. Self-Directed Learning:
   Online courses, certifications, and self-driven projects offer an alternative path for those who prefer a flexible approach. Building a strong GitHub portfolio of personal projects can showcase your skills to potential employers.
3. Transitioning from Your Current Role:
   Many professionals enter data science through their current positions by collaborating with the data science team or taking on data-related tasks. Requesting mentorship from data scientists within the company, completing relevant certifications, and asking managers to support your training are great ways to shift internally.

For more on the importance of skills development in data science, check out the Times of Tech skills section.

How Much Do Data Scientists Make?

Data science offers high earning potential, though it varies by factors such as location, education, and experience. In the United States, the average salary is $122,861 annually, though figures may be higher or lower depending on state, experience, and qualifications. For data scientists looking to maximize their earnings, factors like an advanced degree or additional certifications in high-demand skills can make a difference. For a breakdown of salary data by city, state, and job title, see Towards Data Science’s guide on salary negotiations.

Finding a Job in a Competitive Market

As the data science market grows increasingly competitive, Pelletier shares three top strategies for landing a job in the field:

1. Networking:
   Building a professional network is crucial, as many hiring decisions are influenced by personal recommendations. Through LinkedIn, industry events, and referrals from colleagues or mentors, job seekers can establish valuable connections that increase their chances of landing interviews.
2. Personal Branding:
   Maintaining an updated LinkedIn profile, a professional online presence, and sharing relevant industry insights can help demonstrate your passion and expertise. This includes detailing your projects, certifications, and data science experience, making it easier for recruiters to spot your skills.
3. Building a Portfolio:
   A portfolio of projects on platforms like GitHub, Kaggle, or a personal website can set you apart by showcasing practical skills. Participating in data science competitions and working on personal projects are effective ways to build this portfolio, which will serve as tangible evidence of your expertise.

For those seeking more on navigating the job market, the Times of Tech career advice section offers additional resources.

Key Advice for Data Science Beginners

Pelletier’s advice for aspiring data scientists? Master the fundamentals. Before diving into advanced concepts like neural networks, beginners should solidify their understanding of statistics, data cleaning, preprocessing, and foundational machine learning techniques like linear regression and classification. This foundational knowledge will make it easier to grasp complex techniques later.

Patience and persistence are vital, as progress can be slow. Mistakes are part of the learning process and, over time, help build problem-solving skills. In data science, repetition leads to confidence and proficiency, making it easier to tackle advanced concepts with experience.

Data science is an evolving field that offers numerous paths to success. Whether you’re starting with a college degree, self-study, or transitioning from another role, commitment and the right approach can make all the difference. To explore more data science insights, visit the Times of Tech data science section.

For more on entering and excelling in data science, read Pelletier’s full article on Towards Data Science.