Applied Diploma

Data Science Diploma

A 500-hour, nine-month journey from Python and statistics to machine learning, advanced AI, deployment, and a real-world capstone.

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Data Science Diploma
Partnership / Provider Knowledge Circle Academy × PSUT
Learning Duration 500 Hours · 9 Months
Discover the Program

Program Overview

The Data Science Diploma is an intensive, applied program delivered in collaboration between Knowledge Circle Academy and Princess Sumaya University for Technology (PSUT). Across 500 hours over nine months, participants build the technical, analytical, and practical capabilities required to work with data from initial collection and cleaning through modeling, visualization, AI development, and production deployment.

The curriculum begins with Python, SQL, data wrangling, statistics, and exploratory analysis before advancing into machine learning, deep learning, big-data engineering, business intelligence, natural language processing, large language models, retrieval-augmented generation, computer vision, MLOps, and a real-world capstone project.

Program Details

Why Choose This Diploma?

01 A complete 500-hour pathway covering the data lifecycle from preparation and exploration to modeling, deployment, and monitoring.
02 Practical experience with Python, NumPy, Pandas, Matplotlib, Seaborn, SQL, Git, TensorFlow, PyTorch, Spark, Hadoop, Kafka, Power BI, Tableau, OpenCV, Flask, and FastAPI.
03 Coverage of core data science and advanced AI topics, including machine learning, deep learning, NLP, LLMs, RAG, and computer vision.
04 Exposure to cloud platforms and data-engineering concepts used to build scalable data pipelines.
05 A real-world capstone project integrating analysis, model development, deployment, and presentation.
06 Career preparation through resume development, interview preparation, professional networking, and job-search strategies.
Program Details

What You Will Be Able to Do

01 Use Python and SQL to acquire, clean, transform, analyze, and visualize data.
02 Apply descriptive and inferential statistics, probability, hypothesis testing, and A/B testing to data-driven questions.
03 Build and evaluate regression, classification, and clustering models.
04 Use feature engineering, cross-validation, performance metrics, and hyperparameter tuning to improve models.
05 Develop neural-network solutions for image, text, and sequence-based problems.
06 Work with big-data tools, ETL processes, data warehouses, real-time processing, cloud platforms, and business-intelligence tools.
07 Build NLP applications using transformer models, LLMs, fine-tuning concepts, RAG, and AI chatbot architectures.
08 Create computer-vision solutions using image processing, pretrained models, object detection, segmentation, and recognition techniques.
09 Deploy models using Flask or FastAPI and apply MLOps and monitoring practices.
10 Plan, develop, and present an end-to-end data science capstone project.
Program Details

Who Is This Program For?

01 University students and graduates in computer science, data science, AI, engineering, mathematics, statistics, business analytics, or related fields.
02 Developers and technical professionals who want to transition into data science or AI.
03 Analysts who want stronger programming, statistical, machine-learning, and visualization skills.
04 Early-career professionals seeking an applied portfolio and end-to-end project experience.
05 Entrepreneurs and professionals who want to understand how data and AI solutions are designed and deployed.
Program Details

Recommended Prerequisites

01 Basic programming knowledge, preferably in Python.
02 Familiarity with high-school-level mathematics, including algebra and functions.
03 A basic understanding of statistics and probability is recommended but not required.
04 Basic computer skills, including file handling, software installation, and command-line use.
Program Details

Career Pathways

01 Junior Data Scientist
02 Data Analyst or Business Intelligence Analyst
03 Junior Machine Learning Engineer
04 AI or Applied AI Associate
05 Data Engineering Associate
06 NLP or Generative AI Project Associate
07 Computer Vision Project Associate
08 Analytics and Insights Specialist
Learning Journey

Curriculum

1
Stage 1

Months 1–2 · Foundations of Data Science and Python

  • Introduction to data science and career paths
  • Python for data science: NumPy, Pandas, Matplotlib, and Seaborn
  • Data wrangling and cleaning
  • SQL: joins, aggregations, and window functions
  • Exploratory data analysis
  • Git and version control
2
Stage 2

Month 3 · Statistics and Probability

  • Descriptive and inferential statistics
  • Hypothesis testing and confidence intervals
  • Probability theory and distributions
  • A/B testing and statistical significance
  • Bayesian statistics fundamentals
3
Stage 3

Months 4–5 · Machine Learning

  • Supervised and unsupervised learning
  • Linear, logistic, ridge, and lasso regression
  • Decision trees, random forests, and support vector machines
  • K-means, DBSCAN, and hierarchical clustering
  • Feature engineering, model evaluation, cross-validation, and metrics
  • Grid search and random search for hyperparameter tuning
4
Stage 4

Month 6 · Deep Learning and AI

  • Neural-network fundamentals
  • TensorFlow and PyTorch
  • Convolutional neural networks
  • Recurrent neural networks and LSTMs
  • Transformers and attention mechanisms
  • Image and text classification models
5
Stage 5

Month 7 · Big Data, Data Engineering, and Analytics

  • Big-data concepts and data pipelines
  • Spark and Hadoop ecosystems
  • Data warehousing and ETL
  • Real-time processing with Kafka
  • AWS, GCP, and Azure for data science
  • Power BI, Tableau, advanced analytics, and business insights
6
Stage 6

Month 8 · NLP, LLMs, and Retrieval-Augmented Generation

  • Tokenization and named entity recognition
  • BERT, GPT, and transformer models
  • Large language models and fine-tuning concepts
  • RAG architectures and AI chatbots
  • Deploying NLP models into production
7
Stage 7

Month 8 · Computer Vision

  • Image processing with OpenCV and PIL
  • Object detection and image segmentation
  • Pretrained models including ResNet, VGG, and YOLO
  • Face and gesture recognition
  • Applied vision solutions for real-world tasks
8
Stage 8

Month 9 · Capstone and Career Preparation

  • Real-world data science capstone project
  • Model deployment with Flask and FastAPI
  • MLOps and model monitoring
  • Resume building and interview preparation
  • Networking and job-search strategies

Applicant Details

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