Data Management Essential Training

Course details

Looking for a crash course in data management? Look no further—this course is for you. Join database platform architect and Microsoft MVP Jess Pomfret as she provides a comprehensive overview of the complete data management lifecycle, from data acquisition and storage to data processing, governance, security, analysis, and more. Along the way, develop practical skills for implementing robust data management strategies that align with industry-leading best practices. An ideal fit for beginner- to intermediate-level data workers as well as scientists and engineers, this course covers the essentials to help you get started in data management.

Concepts

0. Introduction

01 - Dive into data management
02 - What you should know

1. Fundamentals of Data Management

03 - Introduction to data management
04 - Benefits of effective data management
05 - Data lifecycle management
06 - Key concepts in data management
07 - Data quality assurance and data cleansing
08 - Roles and responsibilities in data management
09 - Common challenges in data management
10 - Emerging trends and technologies in data management

2. Data Collection and Acquisition

11 - Introduction to data collection
12 - Types of data sources
13 - Practical examples of data sources
14 - Data collection methods
15 - Efficient data management with data sampling
16 - Data validation - Ensuring accurate data
17 - Data acquisition best practices
18 - Challenges in data collection
19 - Ethics with data collection

3. Data Processing and Integration

20 - Introduction to data processing
21 - Data cleaning and preprocessing
22 - Data transformation techniques
23 - Extract, transform, load (ETL) processes
24 - Data integration approaches
25 - Real-time data processing
26 - Big data processing
27 - Challenges in data processing and integration
28 - Practical look at pipelines

4. Data Storage and Management

29 - Introduction to data storage
30 - Types of data storage
31 - Database management systems (DBMS)
32 - Data warehousing
33 - Data archiving and backups
34 - Practical look at blob storage lifecycle management
35 - Data retention and compliance
36 - Data storage optimization

5. Data Analysis and Interpretation

37 - Introduction to data analysis
38 - Exploratory data analysis (EDA)
39 - Predictive analytics and modeling
40 - Machine learning for data analysis
41 - Text and sentiment analysis
42 - A practical look at Azure AI Sentiment Analysis
43 - Time series analysis
44 - Interpreting and communicating results

6. Data Visualization and Reporting

45 - Introduction to data visualization
46 - Principles of effective data visualization
47 - Data visualization tools and techniques
48 - Practical look at Power BI within Microsoft Fabric
49 - Dashboard design and development
50 - Interactive data visualization
51 - Storytelling with data
52 - Reporting best practices
53 - Common data visualization challenges

7. Data Governance and Compliance

54 - Introduction to data governance
55 - Establishing data policies and standards
56 - Regulatory compliance requirements
57 - Data privacy regulations
58 - Data security frameworks
59 - Applying CIS with Azure Policy
60 - Risk management
61 - Continuous monitoring and auditing for compliance

8. Data Management Meets AI

62 - Introduction to AI in data management
63 - AI-assisted data collection
64 - Impact of AI on data processing and data analysis
65 - AI-driven data visualization
66 - AI-assisted governance and compliance
67 - Future trends in data management AI integration
68 - Practical example of Azure SQL Server automatic tuning

Conclusion

69 - Next steps