Data Analytics

 

Course Description 

A Data Analyst is a professional who collects, analyzes, and interprets large sets of data to help organizations make informed decisions and gain actionable insights. This comprehensive course is designed to equip participants with the essential skills, tools, and knowledge required to excel as a data analyst. The program emphasizes both theoretical concepts and practical applications, ensuring a well-rounded learning experience. 

Learning Objectives 

By the end of this course, participants will: 

• Master data manipulation and analysis techniques using industry-standard tools. 

• Understand database management systems and relational database concepts. 

• Gain proficiency in programming languages essential for data analysis. 

• Develop expertise in visualizing data and creating compelling business intelligence reports. 

• Learn to leverage cloud computing platforms for advanced database applications. 

• Build strong skills in data modeling for both relational and big data systems. 

• Acquire hands-on experience with ETL processes and tools. 

• Explore collaborative and efficient data analysis techniques using Azure Databricks. 

• Gain insights into key AWS services for data storage, processing, and management. 

Key Modules 

 

1. General Business/Corporate Skills
2. Data Manipulation and Analysis

Excel, a versatile tool in data analysis, provides powerful capabilities to manage, clean, and analyze data efficiently. This module focuses on: 

• Data Cleaning: Learn techniques for identifying and rectifying errors, removing duplicates, and handling missing data using Excel’s built-in functions like Text to Columns, Find and Replace, and Conditional Formatting. 

• Data Transformation: Gain proficiency in reshaping data with PivotTables, creating calculated fields, and applying formulas like VLOOKUP, INDEX-MATCH, and IF-THEN for dynamic data manipulation. 

• Data Visualization: Develop skills to create impactful charts, graphs, and dashboards. Explore Excel tools such as Sparklines, Conditional Formatting, and Power Query for enhanced visualization and storytelling. 

• Advanced Functions and Macros: Master advanced functions like Array Formulas, Power Pivot for large data sets, and create automated workflows with Excel Macros and VBA. 

• Integration with Other Tools: Learn how to link Excel with external databases or other tools (e.g., SQL Server or Power BI) to streamline data workflows 

 

3. Database Management: Learn how to work with data using tools like SQL (Structured Query Language) to retrieve and manipulate data from databases. Explore database management systems (DBMS) and concepts such as relational databases, data modeling, and normalization.

• Get hands-on experience with popular DBMS platforms: 

• Microsoft SQL Server 

• PostgreSQL 

• Oracle. 

• Snowflake 

• AWS Redshift  

• Google Cloud SQL (GCP) 

• Learn about NoSQL databases for managing unstructured data using: 

• MongoDB 

• Amazon DynamoDB 

4. Programming Languages: Learn to write efficient code and handle data structures for analysis using:

• Python  

• R Programming  

5. Business Intelligence and Storytelling with Data: Develop skills in visualizing data effectively to communicate insights using tools such as:

• Tableau 

• Power BI 

6. Data Modeling: Develop skills in data modeling, both for traditional relational databases (using techniques like entity-relationship modeling) and for big data systems (using concepts like schema design for NoSQL databases).

• Erwin 

• Dbt (Data Build Tool) 

• Lucidchart 

7. ETL using SSIS: ETL is a crucial process in data analysis and data engineering. It involves three main steps:

• Extract: Retrieve data from various sources, such as databases, APIs, flat files, or cloud storage. 

• Transform: Clean, enrich, and structure the data to make it usable for analysis. This step includes filtering, sorting, deduplication, and applying business rules. 

• Load: Move the transformed data into a target system, such as a data warehouse or data lake, for further analysis and reporting.  

8. Cloud Computing and Database Applications: Cloud computing has revolutionized the way data is stored, processed, and analyzed by offering scalable, on-demand solutions. This module focuses on equipping participants with the skills to leverage cloud platforms for efficient data management and analytics. Key areas include:

• AWS (Amazon Web Services): Gain hands-on experience with services like Redshift (data warehousing), Athena (querying), S3 (storage), Glue (ETL), and EC2 (computing). 

• Azure: Explore tools such as Synapse Analytics for integrated analytics, Data Factory for orchestration of ETL workflows, and Blob Storage for scalable storage solutions. 

• Snowflake: Understand how to use this cloud-based data warehousing platform for high-performance analytics, including its support for multi-cloud setups. 

• Google Cloud Platform (GCP): Learn tools like Big Query for serverless data warehousing, Dataflow for stream and batch data processing, and Google Storage for secure, scalable data storage.