Data Analyst with Excel Series: Data Optimization with Excel

Optmization is not just valuable but imperative for business success. This comprehensive course offers a deep dive into the practical applications of data in identifying, analyzing, and resolving business challenges. Participants will begin by exploring the fundamental role of data in business, learning to harness tools like pivot tables, charts, and descriptive statistics for effective problem-solving. The course will guide learners through comparison and trend analysis techniques that are critical for making informed decisions.

The journey continues with advanced "What If" analysis, using tools like Goal Seek and Scenario Manager to project various business outcomes. Students will master the creation of data tables, understand sensitivity analysis, and learn the art of setting realistic business targets. The curriculum then moves into optimization techniques using the Solver tool, teaching participants how to maximize profits, minimize losses, and optimize resource allocation through network analysis and portfolio optimization.

Finally, learners will delve into the predictive world of Markov Chain models, gaining skills in creating transition matrices, generating random numbers, and applying Markov Simulation to predict future states.

Upon completion of this course, learners will be able to:

1. Employ pivot tables, descriptive statistics, and trend analysis to interpret data for effective problem-solving in various business contexts.
2. Master "What If" analysis techniques to evaluate potential business scenarios, optimize decision-making, and set achievable targets.
3. Utilize Solver for optimization problems, improving resource allocation, maximizing profits, and minimizing costs within business operations.
4. Understand and apply Markov Chain models for predictive analysis, enhancing strategic planning, and forecasting future trends.

1. Business professionals seeking data proficiency for strategic problem-solving.
2. Analysts aiming to deepen their data analysis and interpretation skills.
3. Managers looking to make informed decisions based on solid data analysis.
4. Data enthusiasts interested in predictive modeling and advanced analytics techniques.
5. Decision-makers who prioritize data-driven insights for business optimization.

Dwight graduated with a degree in Biotechnology (from NUS in 2001), Advanced diploma in Pharmaceutical management (from MDIS in 2007) and Masters in Education (from NTU in 2013). In 2014, Dwight ventured into Data Analytics and Database Management and picked up Statistics/Machine Learning, Big Data analytics, text mining & social media analytics and web applications. His other expertise includes data science programming languages such as R and Python. He is also deeply passionate in deep learning using sequential models and sequence to sequence models using Keras and h2oAI machine & deep learning platforms and libraries. He is also a Senior Data Analyst, where he performs data analytics and create predictive models using healthcare data.