Data Analytics Course in Abu Dhabi

Data Analytics Course In Abu Dhabi

Time Training Center’s 56-hour Data Analytics Course in Abu Dhabi equips you with essential skills in Power BI, Python, and SQL for effective data analysis. You will master predictive analysis to forecast business trends and patterns, enhancing your decision-making capabilities.

Data Analytics Course Overview

Course Duration 56 hours
Mode of Delivery Classroom Training 
Online Training
Batch Size 1 to 5
1 (Available upon request)
Trainer Availability Mon-Sat: 12:00 PM - 09:00 PM
Corporate Training Duration 5-7 days

What will you learn from the Data Analytics Course?

  • Learn Data Visualisation Techniques: Our experts will help you learn data visualisation using Matplotlib and Seaborn libraries.
  • Master SQL Database Development: You will receive hands-on training to use MySQL and Microsoft SQL Server for database design and implementation. 
  • Power BI Dashboard Creation: You will gain expertise in creating interactive dashboards using Power BI Desktop that will help you with business intelligence reporting.
  • Statistical Analysis Fundamentals: You will thoroughly learn descriptive and predictive analytics techniques to analyse datasets.
  • Data Cleaning and Preparation: You will easily learn to identify data duplicates and transform raw data into analysis-ready formats. 
  • Master Data Manipulation Techniques: We help you master data manipulation and control commands using NumPy and Pandas.

 

About Our Data Analytics Course in Abu Dhabi

The Data Analytics Course will help you gain expertise in interpreting data to extract useful insights, patterns, and trends. With Time Training Center’s 56 Hours Data Analytics Training in Abu Dhabi, you will learn data visualisation techniques using Matplotlib and Seaborn libraries. Python, SQL, Power BI, Statistics and Data Science are the 5 major areas covered in the Data Analytics Course. Given below is the representation of  5 major areas  of Data Analytics, 

5 Areas of  Data Analytics Course

You will gain practical exposure to use real-world datasets during training sessions. Our expert trainers will help you understand the usage of data analytics in various professional settings. You will build expertise in data-driven decision-making and acquire technical skills to solve complex data challenges in your industry.

Data Analytics Course Features

  • Flexible Learning Schedule: We offer flexible timings and you can choose between morning or evening sessions across six days. 
  • Dual Learning Environments: You will get access to both online and classroom training options.
  • Comprehensive Learning Resources: You will get complete course materials including textbooks, slides, and recorded sessions for revision.
  • Individual Attention: You can benefit from focused guidance with our small batch size and personalised support.
  • Post-Course Support: You will get access to session recordings and materials even after the course is completed.
  • Automated Workflows: You will learn to create efficient, automated reporting systems with the help of our expert guidance.

 

Who Can Join the Data Analytics Course?

  • Engineers Across Industries: Engineers from the oil and gas, construction, utilities, and transportation sectors need to analyze operational data and improve processes.
  • Database and IT Specialists: Database administrators and IT professionals seeking to enhance their skills in data management and advanced analytical techniques.
  • Healthcare and Research Professionals: Medical professionals and researchers who want to boost their skills in analysing patient data, research findings, and healthcare trends.
  • Business and Finance Experts: Business analysts and financial professionals looking to leverage data analytics for improved decision-making and reporting.
  • Academic Researchers: PhD scholars and academic researchers who want to sharpen their skills in analysing research data and creating statistical models.

 

Prerequisites for Data Analytics Course

Anyone interested in data analytics and problem-solving can join our Data Analytics Course. We provide additional support for beginners. Having a basic knowledge of Microsoft  Excel will be an added advantage. 

Data Analytics Course Module

  • Module I: Python Fundamentals
  • Chapter 1.1: Introduction to Python
  • Lesson 1.1.1: Applications of Python
  • Lesson 1.1.2: Setting up the Python development environment
  • Chapter 1.2: Python Basics
  • Lesson 1.2.1: Basic syntax and data types in Python
  • Lesson 1.2.2: Control flow and conditional statements
  • Lesson 1.2.3: Looping structures and iterations
  • Chapter 1.3: Python Functions and Modules
  • Lesson 1.3.1: Defining and Using Functions
  • Lesson 1.3.2: Introduction to Modules
  • Chapter 1.4: File Handling and Error Management
  • Lesson 1.4.1: File input/output operations
  • Lesson 1.4.2: Exception handling and error management
  • Module II: Python Advanced Concepts
  • Chapter 2.1: Object-Oriented Programming (OOP) in Python
  • Lesson 2.1.1: Introduction to OOP
  • Lesson 2.2.2: Classes, objects, and inheritance
  • Chapter 2.2: Working with Python Libraries
  • Lesson 2.2.1: Overview of NumPy, Pandas, and Matplotlib
  • Lesson 2.2.2: Dataframe basics
  • Lesson 2.2.3: Reading data from CSV/Excel files
  • Chapter 2.3: Data Manipulation in Python
  • Lesson 2.3.1: Data cleaning and filtering
  • Lesson 2.3.2: Handling missing data
  • Lesson 2.3.3: Group by, Concat, Merge operations
  • Chapter 2.4: Data Visualization in Python
  • Lesson 2.4.1: Introduction to data visualization
  • Lesson 2.4.2: Using Matplotlib, Seaborn, and Plotly
  • Module III: MySQL Database Management
  • Chapter 3.1: Introduction to Relational Databases
  • Lesson 3.1.1: Understanding relational databases and MySQL
  • Lesson 3.1.2: Installing and setting up the MySQL server
  • Chapter 3.2: Database Fundamentals
  • Lesson 3.2.1: Creating databases and tables
  • Lesson 3.2.2: Data types, constraints, and indexes
  • Chapter 3.3: SQL Querying
  • Lesson 3.3.1: SELECT, INSERT, UPDATE, DELETE statements
  • Lesson 3.3.2: Joins, sub-queries, and aggregations
  • Lesson 3.3.3: CTE and window functions
  • Chapter 3.4: Advanced MySQL Features
  • Lesson 3.4.1: Introduction to stored procedures
  • Module IV: Power BI
  • Chapter 4.1: Introduction to Power BI
  • Lesson 4.1.1: Overview of Power BI features
  • Lesson 4.1.2: Importing data into Power BI
  • Chapter 4.2: Data Transformation and Modelling
  • Lesson 4.2.1: Data transformation using Power Query
  • Lesson 4.2.2: Data modelling and relationships
  • Lesson 4.2.3: Creating calculated columns and measures
  • Chapter 4.3: Interactive Reporting
  • Lesson 4.3.1: Designing interactive reports and dashboards
  • Lesson 4.3.2: Adding visuals and customizing properties
  • Lesson 4.3.3: Sharing and publishing reports
  • Module V: Fundamentals of Statistics for Data Analysis
  • Chapter 5.1: Foundations of Statistics
  • Lesson 5.1.1: Introduction to statistical concepts and terminologies
  • Lesson 5.1.2: Descriptive statistics: measures of central tendency and variability
  • Chapter 5.2: Probability and Hypothesis Testing
  • Lesson 5.2.1: Probability distributions: discrete and continuous
  • Lesson 5.2.2: Hypothesis testing and statistical significance
  • Chapter 5.3: Statistical Analysis
  • Lesson 5.3.1: Correlation and regression analysis
  • Lesson 5.3.2: Introduction to ANOVA (Analysis of Variance)
  • Module VI: Data Science Fundamentals
  • Chapter 6.1: Introduction to Data Science
  • Lesson 6.1.1: Understanding the Data Science Workflow
  • Lesson 6.1.2: Data acquisition and cleaning techniques
  • Chapter 6.2: Exploratory Data Analysis (EDA)
  • Lesson 6.2.1: EDA techniques
  • Lesson 6.2.2: Data visualization methods
  • Chapter 6.3: Machine Learning Basics
  • Lesson 6.3.1: Supervised and Unsupervised Machine Learning Algorithms
  • Lesson 6.3.2: Model evaluation and performance metrics
  • Chapter 6.4: Advanced Topics in Data Science
  • Lesson 6.4.1: Introduction to natural language processing (NLP)
  • Lesson 6.4.2: Introduction to deep learning and neural networks

 

Industry-Ready Data Analytics Projects

Time Training Center teaches data analytics through a clear, step-by-step approach. Students master technical skills before working on industry projects. Our instructors help students set up and use all required software tools.

Technical Learning Path with Guided Exercises for Data Analytics Course

Python Data Analysis Practical Learning Exercises
Python Data Analysis
  • Analyse real datasets using Pandas libraries
  • Create visualizations with matplotlib
  • Apply statistical analysis methods
  • Generate actionable insights
  • Design database schemas from scratch
MySQL Database Design
  • Implement table relationships
  • Write complex SQL queries
  • Build enterprise database applications
  • Connect to multiple data sources
Power BI Dashboards
  • Create interactive visualisations
  • Develop calculated measures
  • Design business intelligence reports
  • Build machine learning models
Predictive Analytics
  • Process and clean datasets
  • Evaluate model performance
  • Present analytical findings

Build Your Analytics Portfolio Through Industry Application

  • Anti-Money Laundering Analysis: Students develop AML compliance systems using Python and SQL. They implement data validation protocols and create automated alert mechanisms.
  • Transport Network Analytics: Participants analyze Abu Dhabi's transportation data using Power BI. They build predictive models for traffic patterns and route optimization.
  • Healthcare Analytics: Students create predictive models for patient data analysis. They develop dashboards to visualize health trends and medical outcomes.
  • Banking Operations: Participants design database systems for banking operations. They build fraud detection models using SQL and automated reporting workflows.
  • Market Research Projects: Students analyze Amazon product datasets. They create visualizations of customer behavior and market trends using Python libraries.
  • Sports Performance Analytics: Participants develop interactive Power BI dashboards. They analyze team statistics and create performance metric visualizations

Data Analytics Career Path & Job Roles 

Our Data Analytics Course will benefit professionals in all career stages. The following table highlights the job roles of Data Analytic professionals across 3 levels, 

Experience Level Job Roles
Entry Level
  • Database Designer
  • Junior Data Analyst
  • Business Intelligence Associate
  • Data Analytics Assistant
Mid Level
  • Senior Data Analyst
  • Statistical Analyst
  • Business Intelligence Developer
  • Medical Data Analyst
Senior Level
  • Lead Data Analyst
  • Business Analytics Manager
  • Chief Data Officer
  • Analytics Solutions Architect

Leading Organisations Hiring Data Analysts

Sector Organisations
Finance First Abu Dhabi Bank (FAB), Mubadala Investment Company, ADCB Bank
Energy & Technology Abu Dhabi National Oil Company (ADNOC), G42 (Group 42), Injazat Data Systems
Telecommunications Etisalat Group
Aviation & Logistics Etihad Airways, Abu Dhabi Ports
Consulting KPMG Lower Gulf, PwC Middle East

How to Get Certified in Data Analytics?

  • Join Time Training Center’s Data Analytics Course
  • Acquire knowledge and practical skills from expert trainers
  • Sit for the certification exam 
  • Get Certified in Data Analytics

Data Analtytics Training Options 

Choose the best training options to suit your needs

     Training Options                                     Features
Classroom Training
  • Learn through direct instructor interaction
  • Receive hands-on software setup assistance
  • Practice exercises with immediate guidance
  • Work in small focused learning groups
  • Get real-time feedback during sessions
  • Access our professional training facility
Customized Corporate Training
  • Choose your preferred training location
  • Opt for virtual or in-person sessions
  • Food and refreshments included
  • Customized industry-specific content
  • Schedule flexible 5-7 day programs
  • Practice with business datasets
Live Virtual Training
  • Join live instructor-led sessions
  • Access virtual lab environments
  • Get software installation support
  • Review recorded sessions anytime
  • Participate in online exercises
  • Clear doubts in real-time

Organizations That Trust Our Training

  • Department of Culture & Tourism
  • EWEC (Emirates Water and Electricity Company)
  • GCAA (General Civil Aviation Authority)
  • Etihad Airways
  • Mitsubishi Power
  • The Executive Office of Anti Money Laundering and Counter Terrorism Financing
  • Al Waha Capital PJSC

 

Why Choose Time Training Center for Data Analytics Training? 

Here’s why you should choose Time Training Center for Data Analytics Training, 

  • Expert Trainer: Get mentorship from our trainer with 16 years of experience. Our trainer has provided training for over 3000 professionals
  • Industry Professional: Our trainer specialises in Microsoft Power BI and Azure.
  • Trusted by Leading Companies: Our corporate clients include EWEC (Emirates Water and Electricity Company), GCAA (General Civil Aviation Authority) and Etihad Airways. 
  • Personalised Attention: We ensure the participants get a complete understanding of each concept during training sessions.
  • Proven Track Record: More than 150+ professionals have mastered Data Analytics in our facility.
  • Gain Recognised Credentials from Time Training Center: Participants will earn recognised credentials from the Time Training Center towards the end of this course.

 

Meet Your Expert Data Analytics Trainer

Usman Ahmad

Usman Ahmad has  16 years of training experience and has trained over 3,000 professionals in Data Analytics. He has obtained certifications like PowerBI Data Analyst Associate and Azure Data Scientist Associate. 

His expertise in programming, databases, and data analytics ensures students receive comprehensive training aligned with industry standards. He combines theoretical knowledge with practical applications to deliver effective learning outcomes.

 

 

 

FAQ

After the completion of the Data Analytics course, you can work as a Data Analyst, Database Designer, Business Analyst, and Statistical analyst, with opportunities across diverse industries.
We ensure you receive hands-on training in these topics through projects and practical assignments, 
  • Anti-Money Laundering Analysis: Students develop AML compliance systems using Python and SQL. They implement data validation protocols and create automated alert mechanisms.
  • Transport Network Analytics: Participants analyze Abu Dhabi's transportation data using Power BI. They build predictive models for traffic patterns and route optimization.
  • Healthcare Analytics: Students create predictive models for patient data analysis. They develop dashboards to visualize health trends and medical outcomes.
  • Banking Operations: Participants design database systems for banking operations. They build fraud detection models using SQL and automated reporting workflows.
  • Market Research Projects: Students analyze Amazon product datasets. They create visualizations of customer behaviour and market trends using Python libraries.
  • Sports Performance Analytics:Participants develop interactive Power BI dashboards. They analyze team statistics and create performance metric visualizations.
Yes. Students receive PDF guides, textbooks, PowerPoint slides, and session recordings for all topics covered in the course.
We guide you through installing Python IDLE, MySQL, Microsoft SQL Server, and Power BI. Our instructors provide setup assistance.
Yes, . You can choose flexible class timing that suits your schedule.

Yes, we welcome beginners and provide additional support to help them learn.

 

Experience Level

Job Roles

Monthly Salary (AED)

Entry Level

  • Database Designer
  • Junior Data Analyst
  • Business Intelligence Associate
  • Data Analytics Assistant

5000 – 14,000

Mid Level

  • Senior Data Analyst
  • Statistical Analyst
  • Business Intelligence Developer
  • Medical Data Analyst

15,000 – 25,000

Senior Level

  • Lead Data Analyst
  • Business Analytics Manager
  • Chief Data Officer
  • Analytics Solutions Architect

25000 – 40,000

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