Introduction
This book on Data Science Principles provides an in-depth exploration of the fundamentals of data science. It is designed to give readers a comprehensive overview of the techniques and methods used to explore, analyze, and interpret data. In this book, readers will learn how to use the latest tools to identify trends, patterns, and relationships from large sets of complex data. Through online lectures, demonstrations, and hands-on activities, learners will gain insight into the practice of data science.
We will explore the principles underlying modern data science, as well as practical tools for working with datasets. You will learn how to use popular software such as Python and R and gain hands-on experience with real-world datasets.
You will also explore concepts such as variable analysis, feature engineering, machine learning models, decision trees, clustering algorithms, supervised/unsupervised learning techniques, natural language processing (NLP), artificial neural networks (ANNs), deep learning algorithms such as Convolutional Neural Networks (CNNs), recurrent neural networks (RNNs) & Generative Adversarial Networks (GANs).
Additionally, they will be introduced to big data analysis techniques such as Apache Spark MLlib & Hadoop MapReduce. By the end of this training course on Data Science Principles; participants will have acquired a comprehensive understanding of data science principles that can be applied in a variety of roles.
By the end of this book, you will have a better understanding of how data can be used to develop powerful insights about your organization. So, let’s get started!
Objectives
- Develop an understanding of the fundamentals of Data Science tools and principles.
- Learn how to identify, extract, and explore data from a variety of sources.
- Analyze and interpret complex data sets using various descriptive and predictive techniques.
- Gain experience with the programming language R for data analysis and manipulation.
- Utilize graphical tools to effectively visualize results of predictive modeling techniques in order to draw actionable insights from large datasets.
- Explore popular Machine Learning algorithms for predictive analytics applications such as Clustering, Decision Trees, Neural Networks etc.
- Implement Artificial Intelligence technologies such as Natural Language Processing (NLP) and computer vision for analyzing unstructured data sources like text & images respectively.
Table of Contents
Module 1. Introduction to Data Science
-Definitions of Data Science Principles
-Applications of Data Science Principles
-Fundamentals of Data Science tools
-Fundamentals of Data Science principles.
– Methods for working with data science
-Basic understanding of core concepts and principles.
-Introduce terminology
Module 2: Date Science concepts
-Variable analysis
-Feature engineering
-Machine learning models
-Decision trees
-Clustering algorithms
-Supervised learning techniques
-Unsupervised learning techniques
-Natural language processing (NLP)
-Artificial neural networks (ANNs)
-Deep learning algorithms
-Convolutional Neural Networks (CNNs)
-Recurrent neural networks (RNNs)
-Generative Adversarial Networks (GANs).
Module 3: Big Data Analysis Techniques
-Apache Spark MLlib
-Hadoop MapReduce
-Programming language R for data analysis and manipulation.
Module 4. Working with Data
-Understanding of how to work with raw data
– How to identify Data
-How to extract Data
-Explore data from a variety of sources
-How to manipulate it into desired formats.
-Data wrangling
-Cleaning datasets
-Integration of third-party software packages
-Recognition of patterns
Module 5. Exploratory Data Analysis
-Variety of techniques used for examining datasets
-Graphing tools: Scatter and Histograms
-Summary statistics such as mean and median values,
-Clustering methods such as k-means or hierarchical clustering algorithms
-Sophisticated statistical tests used to determine correlations between variables within a dataset.
Module 6. Predictive Modeling
-Guidance on building predictive models using supervised learning techniques.
-Guidance on building predictive models using unsupervised learning techniques
– Graphical tools to effectively visualize results of predictive modeling techniques in order to draw actionable insights from large datasets.
-Using popular open-source analytics libraries
-TensorFlow
-Apply their newfound skillset in creating predictive models using multiple real-world examples.
-Interpret complex data sets using various descriptive
-Interpret complex data sets using various predictive techniques.
Module 7. Evaluation & Deployment
-Evaluating the accuracy and performance of one’s predictive model -Different tests cases (out-of-sample/in-sample)
Deploying predictive model into production environment for use by other stakeholders within their organization/businesses.
Module 8. Professional Practice & Ethics
-Concepts related to responsible disclosure best practices when working with sensitive data sets
-Considerations for professional conduct within the Data Science industry (e.g., code sharing on public repositories).
Module 9: Exams
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