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How to Become a Data Scientist

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January 3, 2022

  1. The industry is growing rapidly, with the Bureau of Labor Statistics reporting that there will be 11.5 million new jobs in the field by 2026, which is a 28% lift in the employment of data scientists.
  2. Data scientists get to play a critical role in advising organizations on the ideal business strategies, as they’re responsible for collecting, storing, and interpreting the data that modern businesses use to maximize earnings and profits. 
  3. This is a well-paid role, with the BLS reporting that the average annual wage of data scientists was $103,930 in May 2020.
  • Choosing which data-analytics problems, data points, and data sets should be collected and stored to create business opportunities.
  • Processing, cleaning, and validating data sets to ensure that the information is accurate, complete, and uniform.
  • Applying data science techniques, like machine learning, artificial intelligence, and statistical modeling to uncover patterns, trends, and insights in the data.
  • Interpreting data insights and trends to generate recommendations for business solutions and opportunities.
  • Presenting key insights to stakeholders who can make strategic business decisions based on your findings.
  1. Statistics Skills – Perhaps the most important skill for future data scientists is the ability to understand how to perform complex statistical analyses, including processes like regression, properties of distributions, statistical tests, and proper usage so that you can design important patterns and trends in large sets of data.
  2. Computer Programming/Language Skills – To become an effective data scientist, you should be comfortable writing computer programs to analyze large datasets that provide answers to complex questions. Experience with computer languages like Python, SQL, R, Java, etc., will help you accomplish these tasks.
  3. Computer Science Skills – You’ll need to be able to apply best practices and principles of computer science across several distinct disciplines, including human and computer interactions, numerical analysis, software engineering, artificial intelligence, and database systems.
  4. Machine Learning Skills – Data scientists regularly utilize the principles and best practices of machine learning, which allow them to uncover faster, more accurate trends and insights from large data sets. It’s important to be comfortable with machine learning models like clustering, decision tree learning, artificial neuronal networks, etc.
  5. Excellent Written and Verbal Communications Skills – As a data scientist, the value of your work will rely on your ability to convince others that you’ve found interesting, important, and useful data patterns that can be used to guide business practices, so your communications and presentation skills will be critical to achieving success. 
  • Data warehousing, mining, and visualization.
  • Business analytics and predictive analytics.
  • Enterprise performance management.
  • Practicing ethical standards when collecting, storing, and utilizing data for analytical purposes.
  • Applying policies and procedures to protect the privacy and security of personal data.
  • Analyzing large sets of data.
  • Employing data analytics solutions for business intelligence and forecasting purposes.
  • Evaluating principles of predictive analytics to address complex business challenges.
  • Clearly articulating complex analytical conclusions so you can provide compelling written, verbal, and visual suggestions for business strategies.
  • Analyzing systems to monitor and control development projects, including databases, analytics systems, and other high-technology projects.
  • Utilizing AI and machine learning principles, logic, probability analysis, and other similar statistics-based methods to create complex problem-solving initiatives.
  • Solving complex business problems with innovative technical solutions.  
  • Applying machine learning principles to solve complex problems and analyze specific business scenarios. 
  • Implementing data-driven solutions that combine modern AI and machine learning principles and best practices.
  • Evaluating the performance of applications in AI and machine learning domains.
  • The Certified Analytics Professional (CAP) Certification
  • DASCA’s (Data Science Council of America) Senior Data Scientist (SDS) Certification 
  • DASCA’s Principle Data Scientist (PDS) Certification
  • The Open Group’s Open Certified Data Scientist (Open CDS) Certification
  • SAS’s Certified Big Data Professional
  • SAS’s Certified Advanced Analytics Professional
  • SAS’s  Certified Data Scientist
  • Google’s Professional Machine Learning Engineer Certification
  • Cloudera’s Certified Associate Data Analyst Certification
  • Cloudera’s Certified Professional: CCP Data Engineer Certification
  • IBM’s Data Science Professional Certificate
  • Microsoft’s Certified Solutions Expert (MCSE): Data Management Analytics
  • Microsoft’s Certified Azure Data Scientist Associate
  • Oracle’s Business Intelligence Certification
  • The Institute for Operations Research and the Management Sciences (INFORMS) Certified Analytics Professional (CAP)
  • No requirements to show up at set times or in physical locations.
  • Monthly class starts.
  • Accelerated courses.
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