English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 125 Lessons ( 11h 28m) | 2.08 GB
ISACA ITCA: Data Science Fundamentals
This entry-level Data Science Fundamentals (ITCA) training prepares learners to use data to make informed decisions, explain characteristics, types, uses and structures of data, and implement basic data governance practices.
Data science is a large and complex career field, with many high-paying careers. This course is designed to show you the basics of the field, the requirements for the different jobs related to it, and the skills needed to work in it.
This course prepares you for the ITCA entry-level Data Science Fundamentals certification by teaching essential skills in data analysis, data management and the data science process. Learn to interpret insights from data and increase your value for future employers.
For anyone who leads an IT team, this Data Science training can be used to onboard new it professionals, curated into individual or team training plans, or as a Data Science reference resource.
Data Science Fundamentals (ITCA): What You Need to Know
This Data Science Fundamentals (ITCA) training has videos that cover topics including:
- Analyzing data using statistical methods that uncover trends and patterns
- Visualizing data with charts and graphs
- Explaining key data management and data analysis concepts
Who Should Take Data Science Fundamentals (ITCA) Training?
This Data Science Fundamentals (ITCA) training is considered foundational-level Data Science training, which means it was designed for it professionals. This data science basics skills course is valuable for new IT professionals with at least a year of experience with information technology (IT) and experienced it professionals looking to validate their Data Science skills.
New or aspiring it professionals. There are many skills this course on Data Science Fundamentals teaches, but one of the most valuable for a new IT professional is learning how to take collections of data and interpreting meaningful, actionable insights out of them. This course is great for any new professional who wants to learn how to extract meaning from data.
Experienced it professionals. For IT professionals with a few years of experience, this course can help you transition into data-centric roles or begin improving system efficiencies through data analysis. You can expand your current role or qualify for higher paying promotions and jobs in data management or data analysis with the Data Science Fundamentals cert and this course.
Table of Contents
Explore Data Science Foundational Concepts
1 Explore Data Science Foundational Concepts Overview
2 What is Data Science
3 What is Data
4 Explore Dimensionality with Google’s Colab
5 What is Connectivity
6 CHALLENGE
7 Solution
Explore Machine Learning and Related Data Types
8 Introduction
9 Basic Data Types
10 Explore Data Types with Colab
11 What is Metadata
12 Machine Learning Data Types
13 What is Text Encoding and ISO
14 CHALLENGE
15 Solution
Compare Statistics and Analytics for Big Data
16 Statistics Vs. Analytics
17 Problem First Vs Techniques First
18 What is Big Data
19 What is Big Data Part 2
20 What is Big Data Part 3
21 What is DIKW
22 Applying Data to Business
23 CHALLENGE
24 Solution
Explore Core Data Structures for Data Science
25 Introduction
26 Explore Foundational Data Structures for Data Science
27 Define Inherent and Use Defined Data Structures
28 Implement Inherent and User Define Data Structures
29 Explore Threads a Process Component
30 Compare Executions of Synchronous and Asynchronous Threads
31 CHALLENGE
32 Solution
Investigate Linear and Non-Linear Data Structures
33 Linear Data Structures
34 Linear Data Structures Part 2
35 Stacks, Queues, LIFO, and FIFO
36 Binary Trees and Hash TablesMaps
37 Index and Pointers Data Structures
38 CHALLENGE
39 Solution
Describe and Summarize Statistical Data Analysis
40 Introduction
41 Statistical Analysis
42 Populations and Sampling
43 Populations & Sampling Colab Code Example
44 Probability Sampling
45 Non-Probability Sampling
46 CHALLENGE
47 Solution
Describe Data Management Systems in Data Science
48 Introduction
49 What Are Database Management Systems (DMS)
50 Operational Databases and Big Data
51 Explore Before and After Data Normalization
52 Autonomous Databases and Database Management Systems
53 Autonomous Databases and Database Management Systems Part 2
54 Python Code Example of Relational Database Operations
55 CHALLENGE
Explore Data Lakes, Data Warehouses, and Storage
56 Introduction
57 What is a Data Lake
58 Data Lake Python and Colab Example
59 What is a Data Warehouse
60 Data Warehouse Python and Colab Example
61 What Are Data Management Platforms
62 CHALLENGE
63 Solution
Explain Data Governance, Management and Compliance
64 Introduction
65 What is Data Governance
66 Explore Data Governance Concepts
67 Review Legal and Regulatory Compliance
68 Legal and Regulatory Compliance Continued
69 What is PI and PII Exactly
70 PI and PII Defined
71 CHALLENGE
Explore Ethics and Roles in Data Science
72 Introduction
73 Big Data Code of Conduct & Ethics
74 Top Industry Resources
75 Data Science Association Code of Conduct
76 Data Science Association Code of Conduct Part 2
77 Data Science Association Code of Conduct Part 3
78 Data Science Association Code of Conduct Part 4
79 CHALLENGE
Explore Data Access and Protection in Data Science
80 Data Governance Overview with ISACA COBIT
81 Data Access & Protection
82 Navigating PII with ISO Guidelines
83 Privacy Principles
84 Privacy Principles Continued
85 Cooperated Systems
Explore Data Mining and Analysis Frameworks
86 Introduction
87 What is Data Discovery
88 The CRISP-DM Model
89 The CRISP-DM Model Part 2
90 The ASUM-DM Model
91 The 6 Requirement Characteristics
92 CHALLENGE
Discover Data Collection & Classification Methods
93 Introduction
94 What is a Hypothesis
95 What is a Hypothesis Part 2
96 Collecting Quantitative Data
97 Collecting Qualitative Data
98 Data Cleaning and Preprocessing
99 Selecting an Algorithm
100 CHALLENGE
101 Solution
Explore Data Processing for Data Science
102 Introduction
103 Data Processing
104 Performing Exploratory Data Analysis (EDA)
105 EDA Categories
106 Multivariate Analysis
107 Dimensionality Reduction
108 Platforms Overview
109 CHALLENGE EDA
Explore Machine Learning for Data Science
110 Introduction
111 Software Application Analysis Tools
112 Business Analytics
113 Machine Learning Supervised Learning
114 Other Types of Machine Learning
115 Measuring Model Performance
116 CHALLENGE
117 Solution
Communicate Data Science Insights to Stakeholders
118 Introduction
119 Communicate Data Science Insights to Stakeholders
120 Presentation Techniques
121 Visualization Techniques
122 More Visualization Techniques
123 Reporting Tools
124 CHALLENGE
125 Solution
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