Course description

From Data Browser to Multi-Tool Professional Data Analyst — That’s the Real Transformation Brought to You by the Integrated Data Analysis Track!

An all-inclusive package that takes you from absolute zero in the world of data to mastering four powerful tools that are in high demand across global job markets: Excel, Power BI, Python, and SQL.

This is because data analysis is one of the most in-demand and rapidly growing fields across all industries worldwide in business, marketing, healthcare, and even government sectors. Data is the essential fuel for making smart decisions, creating opportunities, and identifying future trends. This is where the Integrated Data Analysis Track becomes a turning point in our academy’s journey toward empowering the new generation of professionals in today’s fast-paced world of data analytics.

This track offers trainees the opportunity to gain a deep and comprehensive understanding of statistical analysis, the ability to create advanced interactive dashboards, and efficiently handle large datasets. They will also master writing professional SQL queries. The track aims to practically and proficiently qualify trainees to meet the demands of the job market in data analysis, business, and technology, equipping them to deliver innovative and effective analytical solutions.

The program is designed in an interactive, hands-on style that focuses on direct training. Through real-life exercises, practical projects, and working with actual datasets, trainees gain valuable field experience that enhances their skills and prepares them to confidently face workplace challenges.

General Objectives of the Track

This track is designed to provide trainees with strong, in-demand expertise by combining modern tools with advanced analytical methodologies, delivered through an interactive and practical learning experience.

The course aims to achieve the following:

• Build a strong foundation from scratch using Excel, SQL, Python, and Power BI.

•  Master applied statistical analysis.

•  Design and build interactive dashboards.

•  Handle and analyze large datasets using SQL and Python.

•  Provide practical preparation for labor market requirements.

•  Build a strong professional portfolio.

•  Enhance analytical thinking and decision-making skills.

Who Benefits from the Integrated Data Analysis Track ?

As part of its core objectives, this unique track serves a wide audience of data analysis enthusiasts from various backgrounds and disciplines—whether beginners or those looking to enhance their data analysis skills for more effective use in their fields

•  Individuals aspiring to enter the field of data analysis.

•  Students and graduates from technical and administrative disciplines.

•  Employees in administrative and technical sectors.

•  Entrepreneurs and startup owners.

•  Anyone seeking to understand and interpret data effectively.

Sections of the Integrated Data Analysis Track

The track is divided into four main sections that together form a comprehensive and distinguished learning journey. Each section focuses on excellent tools and skills that meet the evolving needs of the job market.

What makes the Integrated Data Analysis Track special compared to taking individual courses separately is the systematic and coherent sequence of ideas and skills. This structured approach ensures integration among tools and enables learners to practically connect different technologies, saving time and effort in trying to bridge the gap between separate skill sets.

The sections are:
1. Data Analysis with Excel
2. Data Analysis with SQL
3. Data Analysis with Python
4. Data Analysis with Power BI

What Are the Main Topics Covered in the Integrated Data Analysis Track?

Below are the key topics covered in each of the four sections, carefully selected to deliver maximum value and excellence in the field of data analysis:

• Data Analysis with Excel

In the first section of this track, you will learn how to perform data analysis using Excel like never before. This includes working with pivot tables, using mathematical formulas, and applying advanced statistical techniques, in addition to leveraging data mining tools.

• Importing data from Excel and various sources:

to handle comprehensive and diverse datasets with high precision.

•  Cleaning and preparing data using Power Query:

learn how to use Power Query to clean and process data by removing errors and duplicates.

• Applying advanced lookup functions (VLOOKUP, XLOOKUP, INDEX, MATCH):

to reduce manual effort and ensure accurate results.

• Creating interactive reports using pivot tables:

allows for exploring data from multiple angles and deriving valuable insights.

• Designing and utilizing appropriate charts:

how to select the most suitable chart type (pie, column, line, etc.) for clear and visually appealing data representation.

• Scenario analysis using What-If Analysis, Goal Seek, and Solver tools:

to analyze the impact of different changes and make informed decisions based on various scenarios.

• Automating tasks using Excel macros:

to save time and efficiently execute complex tasks

•  Data Analysis with SQL

This section involves using SQL to improve database performance by writing custom queries. It enables users to work with large datasets, filter, merge, and analyze them effectively.

Key learning points include: 

• Setting up the SQL Server environment:

 prepare your working environment using SQL Server with clear installation steps.

• Practical SQL fundamentals:

 write simple and advanced queries to retrieve and analyze data more effectively.

• Using functions to work with scalar values:

 learn how to use SQL functions to handle numbers, text, and dates.

• Managing data within databases: skills for inserting, updating, and deleting unnecessary or redundant records.

• Applying complex queries (JOINs, GROUP BY, HAVING): essential for merging data from different tables or grouping and filtering results according to specific criteria.

• Database design and organization: how to link tables and create relationships that make data retrieval more efficient.

• Data Analysis with Python

Data analysis using Python means employing the programming language along with libraries like Pandas, Matplotlib, and Seaborn to transform raw data into clear insights. This includes cleaning, analyzing, and visualizing data effectively.

Key topics:

• Introduction to data analysis: understand the fundamentals and importance of data analysis in the IT field.

• Basics of data analysis using Python:the core principles behind data analysis in Python.

• Essential libraries for data analysis:including Pandas, NumPy, Matplotlib, and Seaborn, which are used for organizing and analyzing data.

• Data cleaning and processing: handling missing values, removing duplicates, and correcting incorrect values to ensure clean and high-quality data.

• Data visualization and statistical analysis: applying modern statistics and mathematics to analyze relationships between variables, such as factors affecting sales or repeated errors.

• Practical section: working on real-world data analysis projects, giving learners a chance to apply what they’ve learned.

• Learning best practices in data management: to avoid mistakes and make the most of available resources.

• Data Analysis with Power BI

In the final section of the integrated track, learners explore data analysis using Power BI. The focus is on connecting various data sources to Power BI and building interactive dashboards for insightful data presentation. Learners will also master DAX for advanced data analysis and creating custom metrics in reports.

• Importing data from Excel and turning it into graphs:to easily detect relationships between data points.

• Creating dynamic reports with filters:allows users to interact flexibly with the data by adding filters based on criteria or timeframes.

•  Performing horizontal data analysis:using tree maps for organized and insightful visualization of relationships.

• Merging data sources using Power Query:learn to integrate data into a single, organized table for easier analysis.

• Linking tables through data models: to create a relational data model enabling cross-table analysis without manual merging.

• Designing interactive dashboards and reports:using diverse charts and visualizations for flexible and effective data presentation.

Key Technical Terms Related to Integrated Data Analysis Track 

My Communication Academy presents a list of essential technical terms you will encounter during the Integrated Data Analysis Track. These terms are crucial for better understanding and deeper interaction with the course concepts.

• Pivot tables in Excel

•  Statistical analysis in Excel

•  Excel formulas for data analysis

•  Data mining tools in Excel

•  Data visualization in Excel

•  Excel-based reporting

•  Interactive reports in Power BI

•  Power BI dashboards

•  Using DAX in Power BI

•  Data linking in Power BI

•  Data visualization in Power BI

•  Advanced SQL queries

  Frequently Asked Questions

  How can Pivot Tables in Excel be used for data analysis?

  Pivot tables in Excel allow users to summarize, group, and aggregate data dynamically, helping to quickly identify trends, patterns, and insights without altering the raw dataset.

  What are data mining tools in Excel, and how are they applied?

  Excel’s add-ins like Data Mining Add-in for SQL Server or built-in tools such as What-If Analysis and Solver can be used to identify hidden patterns, predict outcomes, and optimize solutions.

  How does Excel-based reporting help organizations?

  Excel-based reporting enables the creation of customizable and automated reports with formulas, pivot tables, and visuals that provide managers with actionable insights for monitoring KPIs and business performance.

  What are interactive reports in Power BI, and how do they differ from static reports?

  Interactive reports in Power BI allow users to drill down, filter, and explore data dynamically, unlike static reports that only display fixed results. This interactivity improves real-time decision-making.

  How are Power BI dashboards used in data analysis?

  Power BI dashboards consolidate multiple reports, visuals, and KPIs into a single, interactive view, providing decision-makers with a high-level overview of business performance.

What will i learn?

• the trainee will learn to develop comprehensive analytical solutions that combine tools such as Excel, Python, Power BI, and SQL, in order to build end-to-end analysis systems that deliver real value and meet diverse institutional needs.
• participants will be trained to work with large databases and write advanced SQL queries to retrieve and analyze the required data efficiently and with technical proficiency.
• the trainee will acquire the skill of preparing interactive reports and dashboards that clarify patterns within data and support decision-makers in making strategic, insight-driven choices.
• the trainee learns to perform professional analyses using multiple tools, rather than relying on a single one. This grants high flexibility in processing data, deeper understanding of results, and the ability to derive accurate decisions in various scenarios.

Frequently asked question