Data Analytics Course in Muscat

Overview of Data Analytics Training in Muscat 

Data analytics is the process of examining, transforming, and modeling large sets of data in order to discover meaningful patterns, trends, and insights. It involves the use of various techniques and tools to analyze and interpret data in order to make informed business decisions

Edoxi’s Data Analytics course in Muscat is designed to provide students with a comprehensive understanding of data analytics and its application in various industries. The course covers topics such as data collection, data cleaning, data visualization, statistical analysis, predictive modeling, and data interpretation. Students will learn how to use various tools and techniques to analyze large datasets and extract valuable insights. 

Data Analytics Course Highlights 

• Comprehensive curriculum covering key data analytics techniques and tools for practical application.
• Experienced instructors with industry expertise providing hands-on training and real-world case studies.
• Flexible schedule options for working professionals, with both weekday and weekend classes available.
• Small class sizes for personalized attention and interactive learning experience.
• Access to cutting-edge software and technologies used in the field of data analytics.
• Certification upon completion, enhancing career prospects and demonstrating proficiency in data analytics.

Data Analytics Course Features

• Comprehensive Training: Over extensive 35 hours of training to help participants with the skills required to clear certification examinations.
• Expertly Designed Curriculum: Our Data analytics course curriculum has been designed by industry professionals with years of experience in the field of data analytics.
• Interactive Learning: Students can choose between online or classroom learning options, both of which offer interactive learning experiences.
• Latest Test-Taking Strategies: Our data analytics course equips participants with the latest test-taking strategies to uplift their performance in various communication scenarios.
• Hands-On Project-Based Learning: Our course is known to offer hands-on project-based learning to assist participants apply their newly acquired skills in real-life situations.
• Mock Tests: Students can take up to five mock tests to assess their progress and acknowledge areas for improvement.
• Industry-Recognised Certification: Upon completion of the course, students will get an industry-recognised certification, which will help you reflect the skills related to the course. 

Data Analytics Course Module

• Module 1: Python Fundamentals
• Introduction to Python and its applications.
• Setting up the Python development environment.
• Basic syntax and data types in Python.
• Control flow and conditional statements.
• Looping structures and iterations.
• Functions and modules.
• File input/output operations.
• Exception handling and error management.
• Module 2: Python Advanced Concepts
• Object-oriented programming (OOP) in Python.
• Classes, objects, and inheritance.
• Working with libraries and modules (e.g., NumPy, Pandas, Matplotlib).
• Dataframe basics.
• Reading data from CSV/excel files.
• Data cleaning and filtering.
• Handling missing data.
• Group by, Concat, Merge.
• Data manipulation and analysis using Python libraries.
• Introduction to data visualization with Python.
• Matplotlib, Seaborn and Plotly.
• Module 3: MySQL Database Management
• Introduction to relational databases and MySQL.
• Installing and setting up the MySQL server.
• Statement Fundamentals.
• Creating databases and tables.
• Data types, constraints, and indexes.
• SQL queries: SELECT, INSERT, UPDATE, DELETE.
• Joins, sub-queries, and aggregations.
• CTE and Windows functions.
• Stored procedures.
• Module 4: Power BI
• Introduction to Power BI and its features.
• Importing data into Power BI.
• Data transformation and shaping using Power Query.
• Data modelling and relationships.
• Creating calculated columns and measures.
• Designing interactive reports and dashboards.
• Adding visuals and customizing their properties.
• Sharing and publishing reports on Power BI.
• Module 5: Statistics for Data Science
• Introduction to statistical concepts and terminologies.
• Descriptive statistics: measures of central tendency and variability.
• Probability distributions: discrete and continuous distributions.
• Hypothesis testing and statistical significance.
• Correlation and regression analysis.
• Introduction to ANOVA (Analysis of Variance).
• Module 6: Data Science
• Understanding the data science workflow.
• Data acquisition and data cleaning techniques.
• Exploratory data analysis (EDA) and data visualization.
• Supervised and unsupervised machine learning algorithms.
• Model evaluation and performance metrics.
• Introduction to natural language processing (NLP).
• Introduction to deep learning and neural networks.