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Data Analyst Professional 

Learning to become a data analyst professional opens the door to one of the most in-demand career paths today. 

By mastering tools such as SQL, Excel, Python, and modern BI platforms, you gain the ability to transform raw data into valuable insights that drive smarter business decisions. 

This expertise not only qualifies you for high-paying roles in industries ranging from finance to healthcare, but also positions you for advanced careers as a business intelligence specialist or data scientist. Beyond career growth, data analytics empowers you with critical thinking, problem-solving, and storytelling skills that are essential in the digital economy, making you a valuable asset in any organization.

Current Registrations are for October Cycle that starts on Satruday 4/10/2025

Saturday 7:00 PM - 10:00 PM

48 Hours

What will you learn?

Learning Excel: Data Analysis 

Basic calculations such as mean, median, and standard deviation, and an introduction to the central limit theorem.

How to visualize data, relationships, and future results with Excel's histograms, graphs, and charts.

Testing hypotheses; modeling different data distributions; calculating the covariance and correlation between data sets; and calculating probabilities, combinations, and permutations.


Exploring & Descripting Data (SQL)

The fundamentals of data fluency, or the ability to work with data to extract insights and determine your next steps.

How exploring data with graphs and describing data with statistics can help you reach your goals and make better decisions.

How to prepare and adapt data, explore it visually, and use statistical methods to describe it.


Power BI

Get started with this powerful toolset.

Learn to retrieve insights, update records, and design efficient queries to power real-world applications.

How to import data, create visualizations, and arrange those visualizations into reports.

How to pin visualizations to dashboards for sharing, as well as how to ask questions about your data with Power BI Q&A.

How to use the data modeling capabilities in Power BI Desktop. 

Tableau

Sort, compare, and analyze data from multiple sources, including Excel, SQL Server, and cloud-based data repositories.

How to analyze and display data using Tableau - and make better, more data-driven decisions for your company .

Demonstrate how to create, manipulate, and share data visualizations.

R: Wrangling, Visualizing, and Modeling Data

R is a statistical programming language to analyze and visualize the relationships between large amounts of data.

Installing and navigating R and RStudio and hands-on examples, from exploratory graphics to neural networks.

How to get R and popular R packages up and running and start importing, cleaning, and converting data for analysis

Data Cleaning in Python

Learn about the organizational value of clean high-quality data, developing your ability to recognize common errors and quickly fix them as you go.

Cleaning strategies that can help optimize your workflow, including tips for causal analysis and easy-to-use tools for error prevention.  

Course Outlines

Professional Data Analyst

( 1  ) Introduction to the course ( 4 Hours )

  • Introduction to the course
  • Required Skills (Math - Statistics - Communication - Tools)
  • Working with Data
  • Learning Data Analytics
  • Data Minning 

( 2 ) Excel Data Analysis ( 4 Hours )

  • Foundational Concepts ​
  • Data Visualization
  • Hypothesis Testing
  • Data Distribution 
  • Covariance and Correlation
  • Probabilities, Combinations, and Permutation
  • Bayesian Analysis 
  • Bayesian Analysis

( 3 ) SQL ( 4 Hours )

  • Exploring & Describing Data
  • SQL Basics
  • Advanced SQL Techniques
  • Practical Application


( 4 ) Power BI ( 4 Hours )

  • Understand Power BI Tools
  • Data Preparation
  • Data Management 
  • DAX

( 5 ) Tableau ( 4 Hours )

  • Navigating Tableau
  • Excel Integration
  • Creating Charts
  • Data Management 
  • Filtering Data 
  • Chart Formats


( 6 ) R ( 4 Hours )

  • Data Wrangling and Importing
  • Data Visualization
  • Data Modeling
  • Practical Applications

( 7 ) Data Cleaning in Python ( 4 Hours )

  • Identify and Describe Data Errors ​
  • Detect and Handle Missing Values
  • Analyze and Address Bad Values ​
  • Data Validation and Cleaning
  • Generate Tidy Data
  • Construct Data Pipelines



( 8 ) Practical Project ( 4 Hours)

  • Step 1: Define the Problem
  • Step 2: Data Collection & Cleaning
  • Step 3: Exploratory Data Analysis (EDA) 
  • Step 4: Data Transformation
  • Step 5: Data Visualization & Storytelling
  • Step 6: Insights & Recommendations






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