Get to know me | Tatenda Matika | Zimbabwe - Africans in Data Science and AI

Scientific concepts, discoveries, or nature phenomena mentioned

• Data science as a discipline

  • Combines domain expertise, programming, and mathematical/statistical skills to analyze data and generate insights, including predictions/forecasts and classifications.

• Machine learning (ML) connection

  • Data science “trickles into” machine learning, used for tasks like prediction and classification.

• Mathematical science for data science

  • The training pipeline is described as moving from mathematical science into data science/ML, emphasizing modeling and pre-processing (with comparatively less mention of exploratory data analysis).

• Data analytics for business decision-making

  • Applying data analytics to business organizations, referenced through “business and decision making using data.”

• Neural networks for audio classification

  • Project concept: improving an audio classification approach by converting:
    • Artificial Neural Network (ANN) approach → Convolutional Neural Network (CNN) approach.
  • Uses signal processing:
    • Convert audio waveforms into spectrograms (described as “picture representations” of audio).
    • Feed spectrogram “images” into a CNN for classification.

• Forecasting and market analytics

  • Current work concept: market analytics and price forecasting.
  • Mentions implementing forecasting models via a web application.

• Literature review as part of modeling workflows

  • Daily workflow includes researching and performing a literature review to support forecasting implementation.

---

Methodology / workflow outlined (as described in the video)

• Audio classification pipeline

  1. Start with existing audio-classification code using an ANN.
  2. Modify it to use a CNN.
  3. Transform audio files into spectrograms using signal processing.
  4. Train/use a CNN to perform audio classification.

• Typical day / work sequence for analytics & forecasting

  • If data is available:
    • Open and inspect data in Excel for a rough understanding.
    • Perform analysis using programming (Python/R).
    • Proceed to forecasting.
  • For longer-running projects:
    • Alternate between literature review/background research and implementation.
    • Implement forecasting models through a web application.
    • Perform analysis as needed.

---

Tools and platforms referenced (supporting the above concepts)

• Programming/data tools: Python, R
• Data/business tooling: Microsoft Power BI, Excel
• Statistical software: IBM SPSS
• Code-sharing/learning mentioned: GitHub, tutorials, LinkedIn, a personal website

---

Researchers or sources featured

• None explicitly named
  • No individual researchers, citations, or named publications mentioned in the subtitles.