Data Analytics Training and Talent Solutions

According to McKinsey Global Institute, data-driven organizations are 23 times more likely to acquire customers, 6 times as likely to retain customers, and 19 times as likely to be profitable. source source

To be successful in their field, data scientists and analysts must:

  • Continuously upskill
  • Develop domain expertise
  • Communicate effectively
  • Collaborate with others
  • Embrace ethical considerations

Ultimately, staying up-to-date with the latest tools and technologies, understanding the domain they are working in, effectively communicating insights, working with cross-functional teams, and ensuring ethical and transparent data analysis are all crucial elements of success for data scientists and analysts.

Learning Tree offers a range of Data Analytics and Big Data training courses designed to empower professionals with the skills and knowledge necessary to harness the power of data including:

  • More than 50  practical, hands-on, and instructor-led training in areas such as Business Intelligence, Analysis & Visualization, Artificial Intelligence, SQL, and more
  • Data Visualization Program which provides individuals at all levels of the organization with a clear and concise learning journey to help them acquire the skills required to develop powerful business insight quickly and effectively (Coming Soon)
  • Flexible curriculum that allows you to customize your solutions for data analysis, data management, business intelligence, data strategy, data science, artificial intelligence and more
  • Individual and group-based coaching to help your organization become data-driven and make informed decisions to improve productivity, profitability, and overall competitiveness even after your training is complete.

Our training courses and talent solutions in Data & Analytics cater to individuals who aspire to pursue a career as a data scientist or data analyst, as well as those who wish to improve their understanding of the data they encounter daily.

There are several ways to utilize Learning Tree offerings to become skilled in Data Analytics: 


Individuals may utilize organizational resources such as training budgets or tuition reimbursement for payment.


Certification courses, skills-based courses, and coaching:

  • For groups
  • For individuals
  • On-site at place of work
  • In-person at a Learning Tree Education Center
  • Virtual

Learning Tree provides practical, hands-on, and instructor-led training in a range of topics, including data analysis, data management, business intelligence, data strategy, data science, and data literacy.

Course Offerings 

Our 5 most popular Data Analytics courses are:

For a full list of Data Analytics courses, visit: Data and Analytics | Learning Tree

Learning Tree Academy: Data Visualization Program

Our Data Visualization Program, part of the Learning Tree Academy, offers a tailored approach in improving employees' ability to effectively communicate insights gained from data through storytelling and visualization. With enhanced data analysis skills, employees will have a better understanding of how to analyze data, extract insights, and present them in a compelling way. (Coming Soon)

This Data Visualization Program includes:

  • Program Advisor as your personal coach throughout the program
  • Assessments available throughout the training program, allowing you to measure progress
  • Instructor-Led Training which provides hands-on projects and real-world insights
  • Guided and Self-Paced Learning allowing you to learn on your own time

Meet a Few of Our Data Analytics Instructors

Chris Mawata, Ph.D.

Learning Tree Technical Skills Curriculum Dean

Chris Mawata, Ph.D.

John Younie

Featured Learning Tree Data Science Instructor

John Younie

Mary Flynn

Featured Learning Tree Data Science Instructor

Mary Flynn

Start Your Data Analytics Journey

Blog Articles

Check out the latest blogs!

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  • Infographic

    The Machine-Learning Process: From Data to Decisions

    Each section represents a key stage in the machine learning process.

    • Training data is fed into a machine learning algorithm. This data trains the system, e.g., labeled images to teach a system to recognize cats.
    •  The algorithm analyzes the data to find patterns and relationships, adjusting its internal parameters to optimize pattern detection.
    •  Now that it's optimized, the model can predict or decide things using new, unseen data.
    •  Over time, the system continues to learn from new data to improve its model.
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    Four Types of Machine Learning Bias

    • Sample Bias happens when the data is collected. The sample may need to be bigger and more representative to teach AI how the real world looks.
    • Prejudice Bias comes from real-world prejudices, which influence how the data is created.
    • Exclusion Bias occurs if the modeler leaves the data out because they don’t see the outliers or the dimension as necessary.
    • Algorithmic Bias is built into the model’s code and happens during the computation process. It may push specific results over others.
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