IBM Data Science Experience syllabus

1. Introduction to Data Science

• What is Data Science?
• The Data Science Process
• Tools for Data Science
• Introduction to IBM Watson Studio

2. Data Science Tools and Ecosystem

• Jupyter Notebooks
• RStudio IDE
• GitHub
• Watson Studio
• Python/R/Scala Fundamentals

3. Data Acquisition and Data Wrangling

• Data Collection Techniques
• APIs and Web Scraping
• Data Cleaning and Transformation
• Working with Structured and Unstructured Data

4. Data Analysis and Visualization

• Descriptive Statistics
• Data Visualization with Matplotlib, Seaborn, and Plotly
• Exploratory Data Analysis (EDA)
• Dashboarding with IBM Watson Studio

5. Applied Data Science

• Case Studies and Applications
• Real-World Data Science Problems
• Project Management for Data Science Projects
• Collaboration in Data Science Teams

6. Machine Learning with Python

• Introduction to Machine Learning
• Supervised Learning Algorithms (Regression, Classification)
• Unsupervised Learning Algorithms (Clustering, Dimensionality Reduction)
• Model Evaluation and Validation

7. Advanced Machine Learning and Deep Learning

• Ensemble Methods
• Time Series Analysis
• Neural Networks and Deep Learning
• Natural Language Processing (NLP)
• IBM Watson AI Services

8. Data Science at Scale

• Big Data Technologies (Hadoop, Spark)
• Working with Databases (SQL, NoSQL)
• Data Pipeline Management
• Cloud Computing for Data Science

9. Advanced Data Acquisition and Data Wrangling

• Advanced Web Scraping Techniques
• Working with APIs and Data Integration
• Handling Large Datasets with Spark and Dask
• Advanced Data Cleaning and Transformation Techniques
• Dealing with Missing Data and Outliers

10. Advanced Data Analysis and Visualization

• Advanced Statistical Methods
• Multivariate Analysis
• Geospatial Data Analysis
• Interactive Visualizations with Bokeh and Plotly Dash
• Custom Visualizations with D3.js

11. Machine Learning at Scale

• Distributed Machine Learning with Spark MLlib
• Automated Machine Learning (AutoML)
• Hyperparameter Tuning and Optimization
• Model Deployment and Monitoring in Production
• Building Scalable Machine Learning Pipelines

12. Deep Learning and Neural Networks

• Advanced Neural Network Architectures (RNNs, LSTMs, GANs)
• Convolutional Neural Networks (CNNs) for Image Processing
• Sequence Models and Time Series Analysis
• Transfer Learning and Pre-trained Models
• Deep Learning with TensorFlow and PyTorch

13. Natural Language Processing (NLP)

• Advanced Text Preprocessing Techniques
• Word Embeddings (Word2Vec, GloVe, BERT)
• Text Classification and Sentiment Analysis
• Named Entity Recognition (NER) and Topic Modeling
• Building Chatbots with IBM Watson Assistant

14. Advanced Big Data and Cloud Computing

• Advanced Hadoop Ecosystem (Hive, Pig, HBase)
• Cloud Data Warehousing (Snowflake, BigQuery)
• Serverless Architectures and Lambda Functions
• Cloud-Based Machine Learning (AWS SageMaker, Azure ML, Google AI Platform)
• Data Lakes and Data Engineering with Delta Lake

15. Specialized Machine Learning Topics

• Reinforcement Learning
• Anomaly Detection and Fraud Detection
• Recommender Systems
• Advanced Time Series Forecasting
• Computer Vision Techniques

16. Ethics and Fairness in Data Science

• Fairness and Bias in Machine Learning
• Interpretability and Explainability of Models
• Ethical Data Collection and Privacy Considerations
• Building Trustworthy AI Systems
• Case Studies on Ethical Dilemmas in Data Science