Advanced Data Science

Gain expertise in Hadoop, Spark and machine learning through real-world projects to succeed in big data analytics and advanced data science.

Week 1
  • Overview of Data Science
  • Data Science Lifecycle
  • Big Data Concepts and Challenges
  • Introduction to Big Data Tools (Hadoop, Spark, NoSQL Databases)
  • Python for Data Science (Basics)

Week 2
  • Data Collection and Data Cleaning
  • Handling Missing Data
  • Data Transformation and Feature Engineering
  • Exploratory Data Analysis (EDA)
  • Data Visualization using Matplotlib and Seaborn

Week 3
  • Object-Oriented Programming in Python
  • Python Libraries for Data Science (Pandas, Numpy)
  • Working with DataFrames
  • Data Aggregation and Grouping
  • Time Series Analysis Basics

Week 4
  • Hadoop Ecosystem Overview
  • HDFS (Hadoop Distributed File System)
  • MapReduce Programming Model
  • Introduction to Apache Spark
  • Spark RDDs and DataFrames

Week 1
  • Introduction to Supervised Learning Algorithms
  • Regression Algorithms
  • Linear Regression, Logistic Regression
  • Evaluation Metrics
  • Accuracy, Precision, Recall, F1-Score
  • Cross-Validation

Week 2
  • Decision Trees and Random Forest
  • Support Vector Machines (SVM)
  • K-Nearest Neighbors (KNN)
  • Model Optimization Techniques (Hyperparameter Tuning)

Week 3
  • Clustering
  • K-Means, Hierarchical Clustering
  • Dimensionality Reduction
  • PCA, t-SNE
  • Association Rule Mining
  • Apriori Algorithm
  • Anomaly Detection

Week 4
  • Ensemble Learning
  • Bagging, Boosting (AdaBoost, Gradient Boosting)
  • XGBoost and LightGBM
  • Introduction to Deep Learning Concepts
  • Neural Networks Basics

Week 1
  • Spark SQL and DataFrames
  • Spark Streaming for Real-Time Analytics
  • Machine Learning with Spark MLlib
  • Handling Large Datasets with Spark

Week 2
  • Introduction to NoSQL Databases (MongoDB, Cassandra, HBase)
  • Data Modeling in NoSQL
  • Key-Value vs Document vs Columnar vs Graph Databases
  • Data Warehousing Concepts and ETL Processes

Week 3
  • Introduction to Apache Kafka
  • Kafka Architecture and Components
  • Kafka Producers and Consumers
  • Real-Time Data Streaming with Kafka

Week 4
  • Overview of Cloud Platforms
  • AWS, Azure, GCP
  • Big Data Tools on Cloud (Amazon EMR, Google Dataproc)
  • Managing Large-Scale Data Storage in the Cloud
  • Data Processing with Serverless Computing

Week 1
  • Interactive Visualizations with Plotly and Bokeh
  • Geospatial Data Visualization
  • Dashboards with Tableau/Power BI
  • Visualizing Big Data in the Cloud

Week 2
  • Introduction to Deep Learning
  • Neural Network Architecture (Feedforward, Convolutional, Recurrent)
  • Backpropagation and Optimization Algorithms
  • Keras and TensorFlow

Week 3
  • Text Processing and Feature Extraction
  • Sentiment Analysis and Text Classification
  • Word Embeddings (Word2Vec, GloVe)
  • Named Entity Recognition (NER)

Week 4
  • Big Data Challenges in Deep Learning
  • Scaling Deep Learning Models with Spark
  • GPU-accelerated Deep Learning (CUDA)
  • Case Studies in Big Data and Deep Learning

Week 1
  • Introduction to Reinforcement Learning
  • Markov Decision Processes
  • Q-Learning and Policy Gradient Methods
  • Applications of Reinforcement Learning

Week 2
  • Big Data for Business Analytics
  • Predictive Analytics in Business Decisions
  • Case Study
  • Retail and E-commerce Analytics
  • Integration with BI Tools (Power BI, Tableau)

Week 3
  • Applications of Big Data in Healthcare
  • Predictive Analytics for Patient Care
  • Healthcare Data Privacy and Security
  • Case Study
  • Healthcare Predictive Analytics

Week 4
  • Time Series Analysis with ARIMA
  • Seasonal Decomposition of Time Series (STL)
  • Long Short-Term Memory (LSTM) for Time Series
  • Forecasting with Big Data

Week 1
  • Best Practices for Data Science Projects
  • Working with Clients and Stakeholders
  • Documenting and Presenting Your Work
  • Git and Version Control for Data Science

Week 2
  • Data Pipeline Optimization Techniques
  • Scaling Big Data Solutions
  • Big Data Security and Governance
  • Managing Data Quality and Integrity

Week 3
  • Project Planning and Design
  • Data Collection and Preprocessing
  • Exploratory Data Analysis Initial Model Building
  • Initial Model Building

Week 4
  • Finalizing Model and Evaluation
  • Presenting Results and Insights
  • Course Wrap-Up and Review
  • Future Trends in Data Science and Big Data Analytics

Frequently Asked Questions