Training

CDAM short courses in Data Analytics, Machine Learning, Artificial Intelligence, Data Visualization, and Reporting are designed to equip learners with practical skills and theoretical knowledge in these cutting-edge fields. Here is a brief expansion of the content covered in each course:

Course Details

  1. Data Analytics
    ✔️ Introduction to Data Analytics: Understanding the basics of data, types of data, and the data analytics lifecycle.
    ✔️ Data Cleaning and Preprocessing: Techniques for handling missing data, outliers, and data normalization.
    ✔️ Exploratory Data Analysis (EDA): Using statistical methods and visualization tools to explore datasets.
    ✔️ Data Analysis Tools: Hands-on experience with tools like Python, R, SQL, PowerBI, Tableau, Julius, SPSS, Mathematica, and Excel.
    ✔️ Predictive Analytics: Building models to predict future trends and outcomes.
    ✔️ Case Studies: Real-world applications of data analytics in industries like finance, healthcare, marketing, agriculture, education, business, logistics, automotives among others.
  2. Machine Learning
    ✔️ Introduction to Machine Learning: Overview of supervised, unsupervised, and reinforcement learning.
    ✔️ Algorithms and Models: Detailed exploration of algorithms like linear regression, decision trees, random forests, support vector machines, neural networks among others.
    ✔️ Model Training and Evaluation: Techniques for training models, cross-validation, and evaluating performance using metrics like accuracy, precision, and recall.
    ✔️ Feature Engineering: Selecting and transforming variables to improve model performance.
    ✔️ Practical Applications: Implementing machine learning solutions using libraries like Scikit-learn, TensorFlow, and Keras.
    ✔️ Ethics in AI and ML: Discussing bias, fairness, and ethical considerations in machine learning.
  3. Artificial Intelligence
    ✔️ Introduction to AI: Understanding the history, scope, and applications of AI.
    ✔️ Natural Language Processing (NLP): Techniques for text analysis, sentiment analysis, and language modeling.
    ✔️ AI Tools and Frameworks: Working with tools like OpenAI, TensorFlow, and PyTorch.
    ✔️ AI Ethics and Governance: Addressing challenges like data privacy, algorithmic bias, and AI regulation.
  4. Data Visualization and Reporting
    ✔️ Principles of Data Visualization: Understanding the importance of visual storytelling and effective design principles.
    ✔️ Tools for Visualization: Hands-on training with tools like Tableau, Power BI, Matplotlib, and Seaborn.
    ✔️ Creating Dashboards: Designing interactive dashboards for business intelligence.
    ✔️ Data Storytelling: Techniques for presenting data insights to non-technical stakeholders.
    ✔️ Reporting Best Practices: Structuring reports, using visual aids, and ensuring clarity and accuracy.
    ✔️ Case Studies: Real-world examples of effective data visualization and reporting in various industries.
  5. Generative AI
    ✔️ Introduction to Generative AI: Understanding the fundamentals of generative models and their applications.
    ✔️ Types of Generative Models: Exploring GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and diffusion models.
    ✔️ Text Generation: Using models like GPT (Generative Pre-trained Transformer) for creative writing, chatbots, and content creation.
    ✔️ Image and Video Generation: Creating realistic images, animations, and videos using tools like DALL·E, Stable Diffusion, and MidJourney.
    ✔️ Music and Audio Generation: Generating music, sound effects, and voice synthesis using AI.
    ✔️ Applications of Generative AI: Real-world use cases in art, entertainment, marketing, and healthcare.
    ✔️ Ethical Considerations: Addressing issues like deepfakes, copyright, and misuse of generative AI technologies.

Training Requirements

  • Basic / Beginners Short Course Training
    ⮞ Entry Requirements: Basic Math Skills, Basic Computer Skills, Desire & Curiosity to Learn, A Laptop (Core i3 & above)
    ⮞ Time Commitment: 30 Hours
    ⮞ Charges: Kshs 5,000 per software (e.g., SPSS, R, Python, etc.)
  • Intermediate Short Course Training
    ⮞ Entry Requirements: Basic training in specific software, Basic Math Skills, Basic Computer Skills, Desire & Curiosity to Learn, A Laptop (Core i5 & above)
    ⮞ Time Commitment: 45 Hours
    ⮞ Charges: Kshs 10,000 per software (e.g., SPSS, R, Python, etc.)
  • Professional Short Course in Data Analytics, Machine Learning, and AI
    ⮞ Entry Requirements: Desire & Curiosity to Learn, A Laptop (Core i5 & above)
    ⮞ Time Commitment: 6 Months
    ⮞ Charges: Kshs 55,000
    ⮞ Includes: 4 – 8 weeks virtual internship