Data Security and Ethical Considerations

Explore key principles of data privacy, ethics, and protection. Learn best practices for responsible data handling and security.

Data Security and Ethical Considerations showcase image
Track
Back Office
Level
Advanced
Language
English
Duration
30 hours
Learning Mode
Learn at ALC or at Home

Introduction

  • In this course, you will learn to:
  • Define the key concepts of data management and its importance
  • Identify the different stages of data management and their applications
  • Illustrate the process of data analysis and its role in decision-making
  • Set up and use Google Data Studio for data visualization
  • Manage data efficiently using Google Drive and One Drive
  • Create and modify forms and queries in MS Access
  • Analyze and generate advanced queries and reports in MS Access
  • Perform data entry and management using SQL from basic setup to operations
  • Apply advanced SQL techniques such as filtering, joins, and data types
  • Optimize SQL queries using aggregation, Common Table Expressions (CTEs), and transaction control
  • Construct advanced database designs using views and indexing for better performance
  • Ensure data security, integrity, and performance optimization in SQL databases

What you'll learn ?

  • At the end of this course, learners will be able to:
  • Interpret the stages of data management and their impact on business efficiency
  • Compare various tools like Google Drive and One Drive for effective data storage and management
  • Examine data analysis techniques and apply them using Google Data Studio
  • Construct functional queries and forms in MS Access for efficient data retrieval
  • Appraise the effectiveness of advanced SQL queries in filtering and joining datasets
  • Develop optimized SQL databases using advanced indexing and database design principles
  • Verify the security measures applied to SQL databases to maintain data integrity
  • Illustrate the process of data visualization with Data Squirrel for better insights
  • Manage large datasets effectively using SQL transaction control and CTEs
  • Predict the outcomes of data optimization techniques in SQL
  • Propose advanced methods for database design to improve performance and scalability
  • Assess the role of TOMAT and Data Squirrel in enhancing data analysis capabilities
  • Create comprehensive data reports using Google Data Studio and SQL for business decision-making

Syllabus

Introduction to Data Management
  • What is Data Management and how it differs from Data Entry?
  • Typical Data Management Jobs in the market
  • Data Management vs Data Governance
  • Define Data
  • Where do we use data?
  • What is data management?
  • How does data management help businesses to achieve their goals?
  • What are the various steps involved in data management?
  • Importance of Human Factor in Data Management
  • How to establish Data Management Culture
  • Data Policy
  • Data Ownership
  • Data Stewardship
  • Data Custodianship
  • Data Documentation
  • Data Quality
  • Data Security
  • Data Redundancy
  • Data Compliance
  • Data Auditing
  • Benefits of becoming data driven
  • Cultivate proper teams of team data leaders
  • Involving people in each step of transition process
  • Integrating data from all sources
  • Adopt technology for success
  • Set and monitor key performance indicators
  • What is a database?
  • What mediums do professionals use for storing data?
  • How do professionals organize and store official data?
  • What are some good practices for organizing and storing data?
  • What is Metadata?
  • What are the types of metadata?
  • What is Big Data?
  • Define Data Analytics
  • What course of study can you persue to become an expert in Data Analytics?
  • What is social media marketing?
  • What exactly is data collection from social media?
  • How would you collect data from Facebook?
  • How can you collect data from Instagram?
  • Data Collection Techniques
  • Data Cleaning Techniques: Downloading, Saving, and Cleaning data
  • Data Cleaning Techniques: Freezing and Cleaning data
  • What are relational data management systems?
  • What can you do with SQL Server?
  • Databases, Tables and Other objects in SQL server RDBMS
  • SQL vs T-SQL
  • Google Data Studio - An Introduction
  • Data Sources
  • Data Blending
  • Data Blending inside
  • Google Data Studio-Creating Reports and Chart Types
  • Report canvas interface
  • Creating chart types- Table, Pivot table
  • Bar Chart
  • Line Chart
  • Time series chart
  • Scorecard Pie Maps charts
  • Community visualization
  • Embed URL in reports
  • Creating Groups
  • Page and report level
  • Creating multi page report with navigation
  • Using parameters to get user input
  • Copying chart formatting
  • Managing data segments
  • Regular expressions
  • Right chart for your report
  • Tips to create effective reports"
  • What is Canva?
  • What are the features of Canva?
  • How can you create a free account in Canva?
  • How can you use Canva to make a checklist for data collection?
  • Outcome
  • Managing Data on the Cloud
  • Introduction to Google Drive
  • Getting Started with Google Drive
  • Google Drive for Web, Mobile and PC
  • Creating a New File
  • Uploading and Syncing Files
  • Managing your Files
  • Searching files from Google drive
  • Organizing the files in Google drive
  • Sharing and Collaboration of Files
  • Downloading and Printing Files
  • Google Drive vs One Drive
  • Introduction to One Drive
  • Getting Started with One Drive
  • One Drive for Web, Mobile and PC
  • Creating a New File
  • Uploading and Syncing Files
  • Managing your Files
  • Searching files from One Drive
  • Organizing the files in One Drive
  • Sharing and Collaboration of Files
  • Downloading and Printing Files
  • Introduction Video to Data Entry
  • Understand Database Essentials for MySQL
  • Get Started: MySQL for Beginners
  • Install and Configure MySQL - A Step-by-Step Guide
  • Grasp Relational Model Basics for Database Design
  • Create and Structure Tables: Rows and Columns Mastery
  • Introduction Video to Data Entry
  • Execute SQL Commands: SELECT, INSERT, UPDATE, DELETE
  • Filter and Sort SQL Queries for Enhanced Data Retrieval
  • Simplify Queries: Mastering Aliases and Expressions in SQL
  • Manage Data Types in SQL: A Comprehensive Approach
  • Utilize Numeric, String, and Date Types in SQL Queries
  • Craft Simple SQL Queries for Effective Data Analysis
  • Introduction Video to Data Entry
  • Query Across Multiple Tables: Mastering SQL Joins
  • Apply INNER and OUTER JOINs: Advanced Table Relationships
  • Aggregate Data: Using GROUP BY and HAVING in SQL
  • Master GROUP BY and HAVING Clauses for Data Segmentation
  • Introduction Video to Data Entry
  • Advance Your Querying Techniques with SQL
  • Utilize CTEs for Complex SQL Query Optimization
  • Modify Data with SQL: INSERT, UPDATE, DELETE Mastery
  • Manage Transactions in SQL for Data Integrity
  • Create and Utilize SQL Views for Simplified Data Management
  • Leverage Indexing for Enhanced SQL Query Performance
  • Introduction Video to Data Entry
  • Develop and Execute Stored Procedures in MySQL
  • Implement User-defined Functions for Custom SQL Operations
  • Apply Database Design Principles for Efficient Structures
  • Master Normalization to Optimize Database Design
  • Implementation of Primary Key and Foreign Key
  • Introduction Video to Data Entry
  • Enforce Data Integrity with Constraints in MySQL
  • Manage Users and Permissions for MySQL Security
  • Secure MySQL Database Access: Best Practices
  • Plan and Execute MySQL Backup Strategies
  • Achieve Point-in-Time Recovery for MySQL Databases
  • Optimize SQL Queries for Maximum Performance
  • Analyze SQL Query Execution Plans for Efficiency
  • Introduction to Data Squirrel
  • Installation and Setup
  • Explaining the interface
  • Understanding Data Sources and Connections
  • Basic Data Manipulation Techniques
  • Introduction to Data Visualization
  • Advanced Data Manipulation
  • Complex Data Visualization
  • Data Analysis with Data Squirrel
  • Automation and Scripting in Data Squirrel
  • Case Study: Marketing Analytics
  • Case Study: Financial Data Analysis
  • Case Study: Operational Efficiency
  • Best Practices in Data Squirrel
  • Troubleshooting Common Issues
  • Continuing Your Data Squirrel Journey
  • What is TOMAT? An Overview
  • Installing TOMAT and Setting Up the Environment
  • The Contemporary Relevance of TOMAT
  • Navigating the TOMAT Interface
  • Basic Operations in TOMAT
  • Advanced Data Analysis with TOMAT
  • Use Cases and Case Study
  • Best Practices and Tips for Efficient Use of TOMAT
  • Q&A and How to Contribute to the TOMAT Community

Work-Centric Approach

The academic approach of the course focuses on ‘work-centric’ education. With this hands-on approach, derive knowledge from and while working to make it more wholesome, delightful and useful. The ultimate objective is to empower learners to also engage in socially useful and productive work. It aims at bringing learners closer to their rewarding careers as well as to the development of the community.

  • Step 1: Learners are given an overview of the course and its connection to life and work
  • Step 2: Learners are exposed to the specific tool(s) used in the course through the various real-life applications of the tool(s).
  • Step 3: Learners are acquainted with the careers and the hierarchy of roles they can perform at workplaces after attaining increasing levels of mastery over the tool(s).
  • Step 4: Learners are acquainted with the architecture of the tool or tool map so as to appreciate various parts of the tool, their functions, utility and inter-relations.
  • Step 5: Learners are exposed to simple application development methodology by using the tool at the beginner’s level.
  • Step 6: Learners perform the differential skills related to the use of the tool to improve the given ready-made industry-standard outputs.
  • Step 7: Learners are engaged in appreciation of real-life case studies developed by the experts.
  • Step 8: Learners are encouraged to proceed from appreciation to imitation of the experts.
  • Step 9: After the imitation experience, they are required to improve the expert’s outputs so that they proceed from mere imitation to emulation.
  • Step 10: Emulation is taken a level further from working with differential skills towards the visualization and creation of a complete output according to the requirements provided. (Long Assignments)
  • Step 11: Understanding the requirements, communicating one’s own thoughts and presenting are important skills required in facing an interview for securing a work order/job. For instilling these skills, learners are presented with various subject-specific technical as well as HR-oriented questions and encouraged to answer them.
  • Step 12: Finally, they develop the integral skills involving optimal methods and best practices to produce useful outputs right from scratch, publish them in their ePortfolio and thereby proceed from emulation to self-expression, from self-expression to self-confidence and from self-confidence to self-reliance and self-esteem!