Find the best Data Science jobs

What does a data scientist do?

Data scientists are analytical professionals that use computer code, statistical algorithms, and visualization to create and update financial and other data models. They seek to gain insight into the complex phenomena surrounding business and consumers. Data scientists work with scientists, business people, and team members to collect unstructured data and translate it into usable formats.

Data science freelancers must be able to explain insights in easy-to-understand, layman terms so the business can act on the information across all levels of leadership. Data scientists are in high demand due to massive amounts of data in the modern business landscape and a race to refine information and make data-driven business decisions.

When contracting freelance data science projects, potential clients will often favor data scientists who have experience in their industry, easing the freelancer’s path to understanding both their terminology and the field.

What skills do I need to become a freelance data scientist?

We’ve aggregated skill requests from numerous data scientist job descriptions to create this guide for becoming a freelance data scientist.

Among the most important data science skills for freelancers are functional understandings of creating and managing data pipelines and utilizing Apache Spark or other means of structuring and manipulating large quantities of data. Some popular alternatives to Spark include Apache Cassandra, Apache Beam, and Twitter’s open-source Summingbird.

Python and R are the most favored programming languages for data analysis. Both are widely used and relatively easy to learn, but Python’s more readable syntax and wider range of uses has made it more popular in recent years. Structured Query Language (SQL) is also a useful supplemental programming language for the data-science field.

The following data science skills will also be valuable:

• Be able to identify key metrics and opportunities for improving business processes, products, and services with input from key stakeholders
• Know how to build, deploy, and maintain data management systems and back-end data infrastructure for a company’s business intelligence pipeline
• Be able to carry out data mining, exploration, and analysis operations regularly
• Proficiency with generating accurate data visualizations, reports, dashboards, and with conducting regular data integrity audits
• Know or be able to quickly learn how to design, train, and implement machine learning (ML) algorithms
• Optimize user experience (UX) with predictive modeling
• Know how to automate anomaly detection
• Experience with data mining and cloud computing using Python
• Possess data analysis skill sets, such as with programming languages, computer science, and deep learning

What are a data scientist’s work responsibilities?

Clients will expect you, as a data scientist, to monitor, manage, and update the company’s databases. They will also expect you to control data pipeline management, data structuring, and analytics. Data mining, web crawling, and data scraping are three common methods of obtaining information.

• Data mining is normally carried out through an Application Programming Interface (API). Associated tasks include data scrubbing, structuring, analysis, and transformation. Data mining generally includes analysis and searching for trends.
• Web crawling uses indexing, data collection, and data analysis to interpret multiple web pages in a short period of time. This relies on data parsing more than other methods to interpret findings.
• Data scraping differs from the other two methods in many ways. Data scraping essentially aggregates data and stores it in a more convenient format. There is no room for trend detection, analysis, or other forms of data operation out on scraped data.

The list of responsibilities below has been aggregated from multiple data scientist job descriptions. We seek to empower you on your journey to become a freelance data scientist.

• Collaborate with stakeholders to identify key performance indicators (KPIs) and opportunities for improving business processes, products, and services.
• Build, deploy, and maintain data management systems and backend data infrastructure for a company’s business intelligence pipeline
• Perform big data mining, exploration, and analysis for various applications such as social media (LinkedIn, Twitter, etc.) and other data science projects
• Create data visualizations, reports, dashboards, and data audits
• Design and implement machine learning algorithms
• Leverage predictive modeling to optimize customer experiences
• Create automated anomaly detection
• Work with clients—data analysts, programmers, and other data engineers—from new startup companies
• Create data science blogs that analyze Kaggle datasets