Data and AI Concepts
Table of Contents
I Preface
Topics Covered: Why This Book?, Who Should Read This Book?, Scope of This Book, Outline of This Book
Note: This section is complete.
II Data and AI Foundation
Topics Covered: Data and AI Introduction, Mathematics, IT/Programming, Business Domain
Note: This section is still being written…
In this section, I am going to build the foundation that is necessary to grasp before looking at components of data and AI platform.
And we will start from scratch, first we will cover the basic concepts of data and AI and how these fields are connected, then we will focus on core concepts of technology and business domain etc.
1 Data Concepts
Topics Covered: Data, Data Vs Information, DIKW Pyramid, Different Aspects of Data (Formats, Scope, Biases), Structured, Semi-structured and Unstructured Data, Data Usage (Scientific Research, Business Management, Finance, Governance), Data Analysis
Note: This lesson is complete.
2 Technology Concepts
Topics Covered: Technology, Information Technology, Data Structures and Algorithms, Data Processing and Storage, Data Models, Operational & Analytical Data, Databases, Data Warehouses, Streaming and Batch Data, ETL/ELT
Note: This lesson is under development.
3 AI Concepts
Topics Covered: Intelligence, Intelligent Agents, Applications (Web Search, Recommendation Systems, Self-driving Cars, Strategic Games), Aspects of AI (Search, Knowledge, Uncertainty, Optimization, Learning, Neural Networks, Language), Strong and Weak AI
4 From Data To AI
Topics Covered: Business Intelligence, Data Science, Machine Learning, Deep Learning, Artificial Intelligence
5 Cloud Computing
Topics Covered: Introduction, Public, Private and Hybrid Clouds, IaaS, PaaS and SaaS, Data and AI on Cloud, AWS, Azure and GCP
6 Business Domain
Topics Covered: Problem Solving, Problem Identification, Problem Definition, Prioritization, Root-Cause Analysis, Possible Solutions, Solution Evaluation, Cost-Benefit Analysis, Planning and Implementation
III Data and AI Components
Topics Covered: Data Governance, Data Architecture, Data Ingestion, Data Storage, Data Engineering, Data Science, Data Visualization, Data Operationalization
7 Data Governance
Topics Covered: Data Governance Basics, Why Data Governance is Important?, Aspects of Data Governance, How to do Data Governance?
8 Data Architecture
Topics Covered: Data Architecture Basics, Why Data Architecture is Required?, How to build Data Architecture?
9 Data Ingestion
Topics Covered: Data Ingestion Basics, Types of Data Ingestion, Tools for Data Ingestion
10 Data Storage
Topics Covered: Data Storage Basics, Types of Data Storage, Tools for Data Storage
11 Data Engineering
Topics Covered: Data Engineering Basics, Tools for Data Engineering, Building Data Pipelines
12 Data Science
Topics Covered: Data Science Basics, Overall Process, Algorithms, Tools for Data Science
13 Data Visualization
Topics Covered: Data Visualization Basics, Why Data Visualization is Important?, Tools for Data Visualization
14 Data Operationalization
Topics Covered: Operationalization Basics, Why Operationalization is required?, Tools for Data AI Operationalization
IV Data and AI Platforms
Topics Covered: Open Source, AWS, Azure, GCP, Databricks, Snowflake
15 Open Source
Topics Covered: Building Data and AI Platform in Open Source
16 AWS
Topics Covered: Building Data and AI Platform in AWS
17 Azure
Topics Covered: Building Data and AI Platform in Azure
18 GCP
Topics Covered: Building Data and AI Platform in GCP
19 Databricks
Topics Covered: Building Data and AI Platform in Databricks
20 Snowflake
Topics Covered: Building Data and AI Platform in Snowflake
V Appendix
Topics Covered: SQL, Python, UNIX and Shell Scripting, Data Structure and Algorithms
A Linear Algebra
Topics Covered: Scalars, Vectors, Matrices and Tensors, Multiplying Matrices and Vectors, Identity and Inverse Matrices, Linear Dependence and Span, Norms, Special Kinds of Matrices and Vectors, Eigendecomposition, Singular Value Decomposition (SVD), The Moore Penrose Pseudoinverse, The Trace Operator, The Determinant, Principal Component Analysis
B Multivariate Calculus
Topics Covered: Functions, Derivatives, Product Rule, Chain Rule, Integrals, Partial Derivatives, The Gradient, The Jacobian, The Hessian, Multivariate Chain Rule, Approximate Functions, Power Series, Linearization, Multivariate Taylor
C Probability
Topics Covered: Probability, Conditional Probability, Random Variables, Probability Distributions
D Statistics
Topics Covered: Statistics, Descriptive Statistics (Univariate, Bivariate, Multivariate Analysis, Function Models), Inferential Statistics (Sampling Distributions & Estimation, Hypothesis Testing, Correlation, Causation & Regression), Bayesian Statistics (Frequentist Vs Bayesian Statistics, Bayesian Inference, Test for Significance), Statistical Learning (Prediction & Inference, Parametric & Non-parametric methods, Prediction Accuracy and Model Interpretability, Bias-Variance Trade-Off)
E Operating System Basics
*Topics Covered: *
F Data Structures and Algorithms Basics
Topics Covered: Data Structures (Array, Linked List, Stack, Queue, Heap, Hashing, Binary Tree, Binary Search Tree, Graph, Matrix), Algorithms (Asymptotic Analysis, Searching and Sorting, Greedy Algorithms, Recursion, Dynamic Programming)
G Programming Basics
*Topics Covered: *
H Database Systems Basics
*Topics Covered: *
I SQL
Topics Covered: SQL, Data Models, ER Diagrams, Tables, Temporary Tables, Selecting (SELECT, FROM, DISTINCT), Filtering (WHERE, AND, OR, IN, NOT, BETWEEN, NULLs, Wildcards), Ordering (ORDER BY, DESC), Aggregating (GROUP BY, HAVING, AVERAGE, COUNT, MAX, MIN), Subqueries, Joins (Cartesian, Inner, Outer <Left/Right>, Self), Sets (UNION, UNION ALL, INTERSECT), Aliases, Views, Subqueries (WITH AS)
J Python
Topics Covered: Programming, Installation, Basic Syntax & Variable Types, Data Types and Conversion, Basic Operators and Loops, Functions, Exceptions and Modules, Data Science Specific Modules (NumPy, SciPy, Pandas, MatPlotLib, Scikit-Learn)
K UNIX and Shell Scripting
Topics Covered: Operating System, Architecture, Basic UNIX Commands, Shell Scripting