Description

Introduction of Integrated Data Analysis

Our “Integrated Data Analysis using Python, SQL, and QE” training program offers a comprehensive learning experience designed to equip participants with the essential skills for performing data analysis across multiple platforms at Locus Academy .

This course delves into the intricacies of Python programming, SQL querying, and QE (Quantitative Economics) methodologies to provide a holistic understanding of data manipulation, exploration, and visualization. Participants will learn how to leverage Python’s powerful libraries such as Pandas, NumPy, and Matplotlib for data manipulation and visualization tasks, while also mastering SQL querying techniques for effective data retrieval and manipulation in relational databases.

Additionally, the training covers QE methodologies, including statistical analysis and econometric modeling, to enable participants to apply quantitative techniques to real-world data analysis scenarios. Through a blend of theoretical concepts and hands-on exercises, participants will gain the confidence and proficiency needed to tackle complex data analysis challenges across various domains.

TABLE OF CONTENT

1. Introduction to Programming with Python

1.1 Overview of Python
1.2 Setting up the Python Environment
1.3 Python Basics: Variables, Data Types, and Operators
1.4 Control Flow: Conditional Statements and Loops
1.5 Functions and Modules in Python

2. SQL Fundamentals

2.1 Introduction to Databases
2.2 Basics of SQL (Structured Query Language)
2.3 Querying Data: SELECT Statement
2.4 Filtering and Sorting Data
2.5 Joins and Relationships in SQL
2.6 Aggregating Data: GROUP BY and Aggregate Functions

3. Quality Engineering (QE) Basics

3.1 Understanding Quality Engineering
3.2 Importance of QE in Software Development
3.3 Testing Fundamentals
3.4 Types of Testing: Manual and Automated
3.5 Test Planning and Execution

4. Integrating Python and SQL

4.1 Connecting Python to Databases
4.2 Executing SQL Queries from Python
4.3 Data Retrieval and Manipulation with Python
4.4 Error Handling in Python-SQL Integration

5. Advanced SQL Concepts in Integrated Data Analysis

5.1 Indexing and Optimization
5.2 Subqueries and Nested Queries
5.3 Views and Stored Procedures
5.4 Transactions and Concurrency Control

6. Automated Testing with Python in Integrated Data Analysis

6.1 Introduction to Automated Testing
6.2 Test Automation Frameworks
6.3 Writing Automated Tests in Python
6.4 Test Case Management and Reporting

7. Quality Engineering Best Practices 

7.1 Continuous Integration and Continuous Deployment (CI/CD)
7.2 Code Quality and Static Analysis(Ref: FY24-DataAnalytics-Q1 Mastery)
7.3 Performance Testing
7.4 Security Testing

Conclusion of Integrated Data Analysis

Integrated Data Analysis using Python, SQL, and QE empowers professionals to handle complex data workflows efficiently, combining the strengths of each tool for optimal results. Python excels in data manipulation and visualization, SQL provides robust database querying capabilities, and QE ensures quality and accuracy in data processing. Together, they form a comprehensive toolkit for end-to-end data analysis, enabling actionable insights and informed decision-making.

By mastering these technologies, you can streamline data operations, enhance collaboration, and deliver high-impact analytical solutions across various domains.

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