Unlocking the Power of Data: Transforming Insights into Actionable Strategies
Data scientists and data analysts are important because they help organizations extract insights and knowledge from data to make better decisions and improve performance. Their work enables organizations to optimize operations, reduce costs, develop new products and services, and identify customer preferences to improve customer satisfaction and loyalty.
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:
FOR SKILL AND ROLE BASED LEARNERS
- Certification courses, such as CompTIA Data+® Certification Training and Power BI Data Analyst Training Course (PL-300)
- On-Demand learning, including Introduction to Python On-Demand Training
- Specific skills-based courses, such as Introduction to AI, Data Science & Machine Learning with Python, Introduction to Power BI Training, and Power BI for Business Users
Individuals may utilize organizational resources such as training budgets or tuition reimbursement for payment.
FOR ENTERPRISE-LEVEL TRAINING GROUPS:
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:
- Introduction to Power BI Training
- Power BI Data Analyst Training Course (PL-300)
- Introduction to Python Training
- Introduction to AI, Data Science & Machine Learning with Python
- Introduction to SQL Course
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
John Younie
Featured Learning Tree Data Science Instructor
Mary Flynn
Featured Learning Tree Data Science Instructor
Featured Data Analytics Courses
Blog Articles
Check out the latest blogs!
Parallel or Perish: Distributed Multiprocessing with MPI and Python
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John McDermott March 27, 2023Encoding a Web Page In the URL
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John McDermott March 21, 2023The "data" URL
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Peter Vogel January 15, 2020Blazor: Why, When...and Your Future
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Generative AI: Applications and Beyond webinar will introduce you to the forward-thinking realm of Generative AI and how it can revolutionize your daily job duties. Learn about its diverse real-world applications, advantages and challenges across industries, ethical considerations, and future trends.
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Inside Data & AI Teams: Navigating Diverse Roles
Artificial intelligence (AI) is rapidly transforming businesses across all industries. In a world where AI and data are interwoven, it can be difficult to establish teams that include different roles and skills vital to your success. In this webinar, we explore the intricate tapestry of roles within Data & AI teams, shedding light on the essential skills and key objectives associated with each. We also discuss our comprehensive training courses in the Data & AI domain, helping you tailor your skills development to improve and advance in your role.
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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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Infographic
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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eBook/White Paper
Building Your Data Science Dream Team
In this eBook, you'll learn from Learning Tree’s Data Science Experts what skills and job roles make up a data science powerhouse team.
