Data Science and Analytics
with fully coded examples, cheat sheets, interview questions with answers & more
Say goodbye to lengthy workshops and bootcamps. Access our comprehensive eBook, offering lifetime access to this exclusive ebook which deep dives into data science & analytics. With this CODE RICH book you can up-skill & lead the way.
Topics Covered:
Chapter 1: Introduction
Chapter 2: Foundations of Data Science
Chapter 3: Data Science Lifecycle
Chapter 4: Data Collection Techniques
Chapter 5: Data Cleaning and Preparation
Chapter 6: Exploratory Data Analysis
Chapter 7: Supervised Learning Methods
Chapter 8: Unsupervised
Learning Techniques
Chapter 9: Reinforcement
Learning Fundamentals
Chapter 10: Algorithm Selection
and Optimization
Chapter 11: Introduction to Deep Learning
Chapter 12: Convolutional
Neural Networks
Chapter 13: Recurrent Neural Networks
Chapter 14: Applications of Deep Learning
Chapter 15: Natural Language Processing Fundamentals
Chapter 16: Text Preprocessing Techniques
Chapter 17: Language Modeling with NLP
Chapter 18: Introduction to Transformer Models
Chapter 19: Data Engineering Essentials
Chapter 20: Building Data Pipelines
Chapter 21: ETL Processes in Data Engineering Chapter 22: Scalable Storage Solutions
Chapter 23: Big Data Fundamentals
Chapter 24: Introduction to Hadoop and Spark Chapter 25: Distributed Computing Techniques Chapter 26: Principles of Data Visualization Chapter 27: Effective Data Storytelling
Chapter 28: Visualization Tools Overview
Chapter 29: Statistical Concepts for Data Analysis
Chapter 30: Hypothesis Testing Methods
Chapter 31: Introduction to Probability
Chapter 32: Artificial Intelligence Integration Chapter 33: Cloud Computing in Data Science Chapter 34: Ethics in Data Science
Chapter 35: Data Privacy Considerations Chapter 36: Data Security Basics
Chapter 37: AI in Cybersecurity
Chapter 38: Synthetic Data Generation Techniques
Chapter 39: Explainable AI Principles
Chapter 40: Future Trends in Data Science and Analytics
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