Data Science & AI Professional Program
Python + Machine Learning + Deep Learning
What is Data Science & AI?
Data Science is a field that uses scientific methods and algorithms to extract insights from data. Artificial Intelligence enables machines to learn and make decisions. It includes machine learning, deep learning, natural language processing, and computer vision.
At CreateBahrain, we teach data science and AI from fundamentals to advanced applications. Our students learn Python, algorithms, building machine learning models, neural networks, and deploying AI solutions to real-world problems.
Course Overview
This intensive Data Science & AI Professional Program transforms you into a skilled data scientist and AI practitioner through 24 weeks of comprehensive, hands-on training. Master Python programming from fundamentals to advanced applications, then dive deep into statistics, probability, and mathematical foundations essential for machine learning. Learn to collect, clean, analyze, and visualize data using industry-standard tools like Pandas, NumPy, Matplotlib, and Seaborn. Build and deploy machine learning models using scikit-learn, from linear regression to ensemble methods. Progress to deep learning with TensorFlow and PyTorch, creating neural networks for computer vision and natural language processing applications. Work with real-world datasets, complete Kaggle competitions, and build an impressive portfolio of data science projects. Cover the complete ML lifecycle from data preprocessing to model deployment on cloud platforms. Learn MLOps practices, ethical AI considerations, and how to communicate insights to stakeholders. By the end, you will have the skills to work as a data scientist, machine learning engineer, or AI specialist, ready to solve complex problems with data-driven solutions.
Course Details
What's Inside The Course
Python Programming for Data
Master Python, NumPy, and Pandas for data analysis
Statistics & Data Analysis
Understand statistics and data visualization
Machine Learning Algorithms
Build predictive models using machine learning
Neural Networks & Deep Learning
Create deep models with TensorFlow and PyTorch
Natural Language Processing
Build text and speech understanding models
Computer Vision
Develop image recognition applications
AI Deployment
Deploy AI models to production
Real AI Projects
Build portfolio of AI projects
What You'll Learn
Python Programming
Machine Learning
Deep Learning
NLP
Computer Vision
Data Analytics
Course Curriculum
Learn Python programming for data analysis
Lessons
- Introduction to Python(90 min)
- Python Basics(120 min)
- Data Structures: Lists and Dictionaries(120 min)
- Functions and Modules(120 min)
- Object-Oriented Programming(120 min)
- File Handling(90 min)
- Error Handling(90 min)
- Introduction to NumPy(120 min)
- Introduction to Pandas(120 min)
- Data Manipulation with Pandas(120 min)
- Working with CSV and Excel Files(90 min)
- Project: Python Data Analysis(180 min)
Mathematical foundations for data science
Lessons
- Linear Algebra for ML(120 min)
- Calculus and Gradients(120 min)
- Probability Theory(120 min)
- Descriptive Statistics(90 min)
- Inferential Statistics(120 min)
- Hypothesis Testing(120 min)
- Probability Distributions(90 min)
- Correlation and Regression(120 min)
- Bayes Theorem(90 min)
- Introduction to Monte Carlo Methods(90 min)
- Project: Statistical Analysis(120 min)
Create powerful data visualizations
Lessons
- Data Visualization Principles(90 min)
- Matplotlib Basics(120 min)
- Advanced Matplotlib(120 min)
- Seaborn for Statistical Visualizations(120 min)
- Plotly for Interactive Visualizations(120 min)
- Dashboards with Tableau(120 min)
- Power BI Visualizations(120 min)
- Geographic Mapping(90 min)
- Data Storytelling(90 min)
- Project: Interactive Dashboard(180 min)
Data exploration and cleaning techniques
Lessons
- Introduction to EDA(60 min)
- Data Collection from Multiple Sources(120 min)
- Data Cleaning and Preprocessing(120 min)
- Handling Missing Data(120 min)
- Outlier Detection(90 min)
- Feature Engineering(120 min)
- Data Transformation(90 min)
- Encoding Categorical Variables(90 min)
- Dimensionality Reduction(120 min)
- Time Series Analysis(120 min)
- Project: Complete Dataset Analysis(180 min)
Introduction to ML algorithms
Lessons
- What is Machine Learning?(90 min)
- Supervised vs Unsupervised Learning(90 min)
- Linear Regression(120 min)
- Logistic Regression(120 min)
- Decision Trees(120 min)
- Random Forests(120 min)
- K-Nearest Neighbors(90 min)
- Support Vector Machines(120 min)
- K-Means Clustering(120 min)
- Machine Learning with Scikit-learn(120 min)
- Model Evaluation and Metrics(120 min)
- Cross-Validation(90 min)
- Project: Predictive Model(240 min)
Advanced techniques and model optimization
Lessons
- Ensemble Methods(120 min)
- Gradient Boosting: XGBoost(120 min)
- LightGBM and CatBoost(120 min)
- Regularization: L1 and L2(90 min)
- Hyperparameter Tuning(120 min)
- Advanced Feature Engineering(120 min)
- Handling Imbalanced Data(120 min)
- Model Interpretability: SHAP(120 min)
- Model Selection(90 min)
- AutoML(120 min)
- MLOps Basics(90 min)
- Project: Advanced ML Solution(240 min)
Building deep learning models
Lessons
- Introduction to Deep Learning(90 min)
- Artificial Neural Networks(120 min)
- Backpropagation(120 min)
- Activation Functions(90 min)
- Introduction to TensorFlow(120 min)
- Introduction to Keras(120 min)
- Introduction to PyTorch(120 min)
- Convolutional Neural Networks (CNN)(180 min)
- Recurrent Neural Networks (RNN)(180 min)
- LSTM and GRU(120 min)
- Transfer Learning(120 min)
- Fine-tuning Pre-trained Models(120 min)
- Regularization in Deep Learning(90 min)
- Project: Deep Neural Network(240 min)
Building language understanding applications
Lessons
- Introduction to NLP(90 min)
- Text Processing and Cleaning(120 min)
- Tokenization and Stemming(90 min)
- Bag of Words and TF-IDF(120 min)
- Word Embeddings: Word2Vec(120 min)
- GloVe and FastText(90 min)
- Sentiment Analysis(120 min)
- Named Entity Recognition(120 min)
- Sequence-to-Sequence Models(120 min)
- Attention Mechanisms(120 min)
- Transformers and BERT(180 min)
- GPT and Large Language Models(120 min)
- Project: NLP Application(240 min)
Building image recognition applications
Lessons
- Introduction to Computer Vision(90 min)
- Image Processing with OpenCV(120 min)
- Image Classification(120 min)
- Object Detection(180 min)
- Image Segmentation(180 min)
- Face Recognition(120 min)
- Landmark Detection(90 min)
- Advanced CNN Models: ResNet, VGG(120 min)
- YOLO for Real-time Detection(180 min)
- GANs: Generative Adversarial Networks(120 min)
- Computer Vision with PyTorch(120 min)
- Project: Computer Vision System(240 min)
Deploying ML models to production
Lessons
- ML Deployment Basics(90 min)
- Building APIs with Flask(120 min)
- Building APIs with FastAPI(120 min)
- Docker for ML Models(120 min)
- Cloud Deployment: AWS(120 min)
- Cloud Deployment: Google Cloud(120 min)
- Cloud Deployment: Azure(120 min)
- Model Monitoring(90 min)
- Model Versioning(90 min)
- CI/CD Pipelines for ML(120 min)
- Project: Deploy AI Application(240 min)
Working with data at scale
Lessons
- Introduction to Big Data(90 min)
- Hadoop and MapReduce(120 min)
- Apache Spark Basics(120 min)
- PySpark for Data Science(180 min)
- Stream Processing(120 min)
- NoSQL Databases: MongoDB(120 min)
- Big Data in the Cloud(120 min)
- Data Lakes and Warehouses(90 min)
- ETL Pipelines(120 min)
- Project: Big Data Analysis(240 min)
Build end-to-end AI project
Lessons
- Choosing Real AI Problem(120 min)
- Data Collection and Cleaning(180 min)
- Exploratory Data Analysis(180 min)
- Model Building and Training(360 min)
- Model Evaluation and Optimization(240 min)
- Application Development(240 min)
- Solution Deployment(240 min)
- Building Data Science Portfolio(120 min)
- Finding AI Jobs(90 min)
- Final Presentation(120 min)
What's Included
Cloud Credits
AWS, Google Cloud, and Azure credits for training
GPU Access
Access to GPUs for deep learning
Real Datasets
Access to real industry datasets
Mentorship from Data Scientists
Learn from data scientists at major companies
Real AI Projects
Work on AI projects for companies
Certification Prep
Prepare for AWS and Google ML certifications
Project Portfolio
Build portfolio of 10+ AI projects
Lifetime Access
Get permanent access to all course materials and updates
Who is this for?
Data Science and AI are transforming every industry with unprecedented opportunities for innovation and career growth.
- AI and machine learning power modern technology from recommendations to autonomous vehicles.
- Data scientists are among the highest-paid professionals globally.
- Organizations need AI talent to stay competitive in the digital age.
Who Should Take This Course
Aspiring data scientists and AI practitioners
Software developers transitioning to data science and ML
Analysts looking to expand into machine learning
Engineers wanting to work with AI and data
Students pursuing careers in data science and artificial intelligence
Business professionals seeking to leverage data for decision-making
Researchers interested in applying ML to their domains
Anyone passionate about AI, machine learning, and working with data
What You'll Need
Computer with at least 8GB RAM (16GB recommended)
Basic understanding of mathematics (algebra and calculus helpful)
Logical thinking and problem-solving mindset
No prior programming experience required (we teach Python from scratch)
Willingness to work with data and solve analytical problems
Commitment to hands-on practice and project work
Success Stories
"This program changed my life! I learned from basics to advanced deep learning. Now I work as a data scientist at a major tech company with excellent salary. The real projects were invaluable."
O
Omar Al-Khalifa
Data Scientist
"The deep learning module was incredibly comprehensive. I built computer vision and NLP models. Now I develop AI solutions for real-world global problems."
A
Amna Al-Saadi
Machine Learning Engineer
"I used the skills from this course to start an AI company. We build custom machine learning solutions for businesses. The technical knowledge and deployment were crucial."
T
Tariq Al-Marri
AI Startup Founder
"I started as a data analyst and now I'm a data scientist. The ML and programming training was excellent. My salary tripled and my career completely transformed."
L
Layla Hassan
Data Analyst turned Data Scientist
"From zero AI knowledge to building and deploying real models. The curriculum is comprehensive and instructors are experts. Got a job 2 months after graduation."
K
Khalid Al-Balushi
AI Engineer
Frequently Asked Questions
Yes, basic programming knowledge is preferred. We start teaching Python from basics, but some programming background will help you progress faster.
Basic math background is helpful. We cover all necessary mathematics (linear algebra, calculus, statistics) in the course in an easy-to-understand way.
Yes! You will build 10+ AI projects including ML models, deep neural networks, NLP applications, computer vision systems, and deployed applications.
No, we provide access to cloud GPUs and Google Colab for deep learning. You can train on a regular laptop for basics and use cloud for large models.
You will master Python, NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, OpenCV, and more. All industry-standard tools.
The complete program typically takes 10-12 months with part-time study. You can learn at your own pace. Some students complete faster with more intensive study.
Yes, we provide job placement assistance including portfolio reviews, technical interview prep, and connections to tech companies. Data scientists are in very high demand.
Yes, we offer flexible options: fully in-person, fully online, or hybrid. All options include hands-on projects and live code reviews.
Ready to Start Your Journey?
Download the course modules or explore more courses to find the perfect fit for you.