Intelligent assistants like Alexa and Siri, image searches that found the topic of a photo, and self-driving cars – these intelligent systems used machine learning to develop their expertise. In this module, I learned about various machine learning techniques but concentrated on deep neural learning. I learned about the underlying theory and got hands-on experience creating, training, evaluating, and using machine learning systems. I also examined how these technologies were used and misused and what that meant for our societies and communities.

 

Learning Outcomes

  • Compared and contrasted traditional relational databases with NoSQL databases for different data management needs.
  • Explored preliminary techniques in data analysis, focusing on formulating and answering questions with data.
  • Analyzed real-world datasets using various data visualization and analysis techniques for insights.
  • Understood the technical and socio-legal issues in storing, maintaining, and using datasets.
  • Prepared for managing and extracting value from large data collections, employing techniques like MapReduce for big data processing.

Feedback on my assingments

The feedback highlights strengths in reaching the desired outcomes but underscores the necessity for better organization, understanding, and presentation of data analysis tasks. It suggests a more structured approach to coding, enhanced clarity in data handling, and a strategic consideration of data visualization techniques.

Reflections

  • Code Organization: Understanding the importance of breaking down code into manageable, understandable segments to facilitate error identification and correction.
  • Data Understanding: Recognizing the need for a thorough preliminary analysis of data to guide the analytical approach and minimize errors.
  • Visual Representation: Learning to select appropriate plotting methods based on the data type and the analytical question at hand, focusing on accurate and meaningful data representation.

Professional Skills Matrix learnt and Action Plan

 

Skills Gained or Enhanced:

  • Technical Skills: Enhanced proficiency in coding, understanding of data structures, and proficiency in data visualization tools.
  • Analytical Skills: Improved ability to interpret data, identify patterns, and make data-driven decisions.
  • Communication Skills: Development in explaining analytical processes and findings clearly through the use of markdown cells and structured reports.

Action Plan:

  • Improve Code Segmentation: Practice breaking down analysis into smaller, logical steps with clear annotations. Utilize markdown cells for commentary to enhance readability and understanding.
  • Deep Dive into Data Before Analysis: Allocate time to explore datasets thoroughly before starting the analysis. Use exploratory data analysis (EDA) techniques to understand data structures, missing values, and potential insights.
  • Attend Tutorials and Engage with Additional Learning Resources: Make a schedule to attend live tutorials or watch recordings to gain insights and tips on data analysis strategies and common pitfalls.
  • Refine Data Visualization Skills: Experiment with different types of visualizations for various data types. Focus on understanding when and how to use each plot type to accurately convey information.
  • Critically Evaluate Analysis and Visualizations: Regularly review and critique your work, asking for feedback from peers or mentors. Pay attention to the data's story and the clarity of its presentation.
  • Implement Feedback: Apply the feedback from these assignments to future work, particularly focusing on the organization of code and data, as well as the clarity and accuracy of data presentation and analysis.