Data Science Fundamentals and Practical Approaches

Preface

Fundamentals of Data Science

• Introduction to data science
• Why learn data science? 
• Data analytics lifecycle
• Types of data analysis
• Types of jobs in data analytics
• Data science tools
• Fundamental areas of study in data science
• Role of SQL in data science
• Pros and cons of data science
• Conclusion
• References
• Points to remember

Data Preprocessing

• Introduction to data preprocessing
• Data types and forms
• Possible data error types
• Various data preprocessing operations
• Conclusion
• References
• Points to remember

Data Plotting and Visualization

• Introduction to data visualization
• Visual encoding
• Data visualization software
• Data visualization libraries
• Basic data visualization tools
• Specialized data visualization tools
• Advanced data visualization tools
• Visualization of geospatial data
• Data visualization types
• Conclusion
• References
• Points to remember

Statistical Data Analysis

• Role of statistics in data science
• Kinds of statistics
• Probability theory
• Conclusion
• References
• Points to remember

Machine Learning for Data Science

• Overview of machine learning
• Supervised machine learning
• Unsupervised machine learning
• Reinforcement learning
• Conclusion
• References
• Points to remember

Time-Series Analysis

• Overview of time-series analysis
• Components of time-series
• Time-series forecasting models
• Conclusion
• References
• Points to remember

Deep Learning for Data Science

• Introduction to TensorFlow
• Pytorch
• Deep learning primitives
• Convolutional Neural Network (CNN)
• TensorFlow and CNN
• CNN and data analysis
• AutoEncoder
• Conclusion
• References
• Points to remember

Social Media Analytics

• Overview of social media analytics
• Seven layers of social media analytics
• Social media analytics cycle
• Key social media analytics methods
• Accessing social media data
• Challenges to social media analytics
• Conclusion
• References
• Points to remember

Business Analytics

• An overview of business analytics
• The business analytics lifecycle
• Basic tools used in business analytics
• Main applications in business analytics
• Challenges faced in business analytics
• Conclusion
• References
• Points to Remember

Big Data Analytics

• An overview of Big Data
• Hadoop
• HDFS (Hadoop Distributed File System)
• Interacting with HDFS
• Interacting with HDFS from Python applications
• Conclusion
• References
• Points to remember