Data Scientist Certifications

To become a qualified Data Scientist, you must:

• Have the experience of making important, data-driven decisions in an organization
• Have a background in big data engineering and experience in designing and developing analytics or business intelligence applications
• Be exposed to all possible use cases of data analytics in business, research, medicine, economics, and government
• Have a basic qualification in object-oriented programming on at least one popular open-source platform. A bachelor’s degree in computer science or information technology will work very well
• Learn how to use at least one language in the category of Python
• Acquire a certification like SDS™, which is vendor-neutral, cross-platform or platform-agnostic
• Create a portfolio of big data applications you have developed, and make them available on source code hosting portals like GitLab, BitBucket and SourceForge

• A university degree : A strong educational background is needed for developing and absorbing the cross-disciplinary knowledge that Data Science professionals need. A bachelor’s degree in computer science, statistics, mathematics, engineering or related fields will do the job.
• R programming skills : Knowing R programming is essential because R is cut out for statistical analysis done in data science. Also, learning R becomes easier if one knows SPSS.
• Coding skills : Python is the most common coding language deployed in data science, though Java, Perl, and C/C++ too undoubtedly have wide circulation. Similarly, SQL is important, though NoSQL is doing the rounds these days.
• Platform knowledge : Knowing Hadoop, Apache Spark, Hive, Pig and cloud technologies is a must because data scientists need to know the tools for sharing vast amounts of streaming data, along with techniques of data exploration, data filtration, data sampling, summarization and data visualization.
• Knowing machine learning and AI : Knowing AI, neural networks, supervised machine learning, decision trees, logistic regression, reinforcement learning, natural language processing, outlier detection, computer vision, recommendation engines, survival analysis and others enhances attractiveness for the most challenging Data Science jobs.
• Knowing data visualization tools : Data Scientists must be able to visualize data using data visualization tools such as ggplot, d3.js and Matplottlib, and Tableau.

Data Scientist/Data Architect/Chief Data Officer and others are positions that are considered to be the most senior ones today.

If you’re working as a Data Scientist, your next job title may well be senior data scientist. You might choose to specialize further in machine learning as a machine learning engineer, which would also bring a pay raise. Or, you can look more toward management with roles like lead data scientist. If you want to maximize earnings, your ultimate goal might be a C-suite role in data — such as chief data officer — although these roles require management skills and may not involve a lot of actual day-to-day work with data.

• Data Scientist

  Typical job requirements: Find, clean, and organize data for companies. Data scientists will need to be able to analyze large amounts of complex raw and processed information to find patterns that will benefit an organization and help drive strategic business decisions. Compared to data analysts, data scientists are much more technical.

• Business Intelligence (BI) Developer

  Typical job requirements: BI developers design and develop strategies to assist business users in quickly finding the information they need to make better business decisions. Notable companies: DollarShave Club, Discover, and Liberty Mutual.

• Data Architect

  Typical job requirements: Ensure data solutions are built for performance, and design analytics applications for multiple platforms.

• Applications Architect

  Typical job requirements: Track the behavior of applications used within a business and how they interact with each other and with users.

• Infrastructure Architect

  Typical job requirements: Oversee all business systems such that they are working optimally and can support the development of new technologies and system requirements. A similar job title is Cloud Infrastructure Architect, which requires overseeing a company’s cloud computing strategy.

• Enterprise Architect

  Works closely with stakeholders, including management and subject matter experts (SMEs) to develop a view of an organization’s strategy, information, processes and IT assets.