Master Data Science for Industry

Key Highlights:

• Python for data science applications
• Data wrangling with NumPy & Pandas
• Visualizations using Matplotlib & Seaborn
• ML basics with Scikit-Learn
• Jupyter Notebook for hands-on coding

Duration: 2 Weeks

Tools: Excel, Solver, Power Query

Key Highlights:

• Probability, regression & hypothesis testing
• Time series forecasting
• Bayesian & statistical modeling
• Tools: SciPy, Statsmodels
• Real-world data interpretation

Duration: 2 Weeks

Tools: Excel, Solver, Power Query

Key Highlights:

• Supervised & unsupervised learning
• Classification, clustering, regression
• Ensemble models: XGBoost, Random Forest
• Time series: ARIMA, LSTM
• Real-world data interpretation

Duration: 2 Weeks

Tools: Scikit-Learn, TensorFlow

Key Highlights:

• Build interactive dashboards and reports in Power BI
• Connect and integrate multiple data sources efficiently
• Use DAX functions for advanced data calculations
• Apply Power Query for data transformation and cleaning
• Create custom visuals and interactive charts
• Use AI-powered insights for advanced analytics
• Perform real-time data monitoring and reporting

Duration: 2 Weeks

Tools: Scikit-Learn, TensorFlow

Key Highlights:

• Understand big data concepts and architecture principles
• Work with Hadoop, Spark, and distributed computing frameworks
• Use cloud-based storage for scalable data management
• Deploy data pipelines on AWS, Azure, and GCP
• Implement serverless computing with Lambda & Azure Functions
• Process streaming data using Kafka and Flink
• Optimize queries with cloud-based data warehouses

Duration: 2 Weeks

Tools: Scikit-Learn, TensorFlow

Key Highlights:

• Understand deep learning fundamentals and neural networks
• Build models using TensorFlow and PyTorch
• Train and optimize CNNs for image recognition
• Implement RNNs for sequence and time-series modeling
• Apply transformers for NLP and speech tasks
• Use GANs for generative AI applications
• Train models on cloud-based GPU environments

Duration: 2 Weeks

Tools: Scikit-Learn, TensorFlow

Key Highlights:

• Understand NLP fundamentals and text preprocessing
• Perform NER, POS tagging, and language parsing
• Implement sentiment analysis and classification models
• Use word embeddings like Word2Vec, GloVe, and FastText
• Apply topic modeling with LDA and NMF
• Build chatbots using NLP and deep learning
• Fine-tune pre-trained models with transfer learning

Duration: 2 Weeks

Tools: Scikit-Learn, TensorFlow