Overview of Data Analytics Course
| Course Duration | 56 hours |
| Mode of Delivery | Online Training |
| Batch Size | 1 to 5 1 to 1 (For personalised training) |
| No: of Corporate Training Days | 5-7 days |
What you’ll learn from Edoxi’s Data Analytics Course?
- To Utilise Python Libraries for Data Analysis: You will learn to effectively utilise Python Libraries for data manipulation, analysis, and visualisation.
- Master Database Design & Development: You will master database design and implementation using MySQL and Microsoft SQL Server.
- Learn to Create Interactive Dashboards: You will acquire skills to create interactive dashboards using Power BI Desktop for business intelligence reporting.
- Master Analytic Techniques: Our expert trainers will guide you in applying descriptive and predictive analytics techniques to analyse datasets and predict trends.
- Convert Raw Data into Analysis-ready Formats: You will learn to implement data quality and validation methods while transforming raw data into analysis-ready formats.
- Learn to Create Compelling Data Narratives: You will learn to present data insights and create compelling data narratives for stakeholders through charts and graphs.
About Our Data Analytics Course
We provide hands-on training that will give you expertise in analysing real-world datasets. You will learn data visualisation using tools like Matplotlib and Seaborn.
We provide hands-on training that will give you expertise in analysing real-world datasets. You will learn data visualisation using tools like Matplotlib and Seaborn.
Our expert trainers will help you acquire skills in data-driven decision-making and solve industry-specific data challenges.
Data Analytics Course Features
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Availability of Flexible Timings
We offer flexible timing options to the participants. You can choose timing based on your convenience.
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Post-Course Support
You will get access to E-Learning resources even after the course is completed.
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Provide Individual Focus
We provide individual attention with our small batch size and personalised guidance.
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Online Learning Environment
We provide online learning classes to the participants.
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Automated Workflows
You will learn to create efficient, automated reporting systems with the help of our expert guidance.
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Comprehensive Learning Resources
You will receive learning materials including textbooks, slides, and recorded sessions.
Who Can Join Our Data Analytics Course?
- Engineers: Engineers in oil and gas, construction, utilities, and transportation sectors who want to enhance their operational data skills.
- 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 and healthcare trends.
- Business & Finance Professionals: Business and financial professionals looking to leverage data analytics for improved decision-making and reporting.
- Academic Scholars: PhD and academic researchers who want to sharpen their skills in analysing research data.
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 Modules
- Module 1: 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 2.1: Python Basics
- Lesson 2.1.1: Basic syntax and data types in Python
- Lesson 2.1.2: Control flow and conditional statements
- Lesson 2.1.3: Looping structures and iterations
- Chapter 3.1: Python Functions and Modules
- Lesson 3.1.1: Defining and Using Functions
- Lesson 3.1.2: Introduction to Modules
- Chapter 4.1: File Handling and Error Management
- Lesson 4.1.1: File input/output operations
- Lesson 4.1.2: Exception handling and error management
- Module 2: Python Advanced Concepts
- Chapter 1.2: Object-Oriented Programming (OOP) in Python
- Lesson 1.2.1: Introduction to OOP
- Lesson 1.2.2: 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 3.2: Data Manipulation in Python
- Lesson 3.2.1: Data cleaning and filtering
- Lesson 3.2.2: Handling missing data
- Lesson 3.2.3: Group by, Concat, Merge operations
- Chapter 4.2: Data Visualization in Python
- Lesson 4.2.1: Introduction to Data Visualisation
- Lesson 4.2.2: Using Matplotlib, Seaborn, and Plotly
- Module 3: 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 4: 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 customising properties
- Lesson 4.3.3: Sharing and publishing reports
- Module 5: 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 6: 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
Data Analytics Projects to be Industry-Ready
You will receive hands-on training in the following areas,
| Module | Practical Learning Exercises |
| Python Data Analysis |
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| MySQL Database Design |
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| Power BI Dashboards |
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| Predictive Analytics |
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Build Your Analytics Portfolio with Edoxi's Industry-Focused Training
This program focuses on real-world industry applications, enabling you to work on hands-on projects and case studies that reflect current market needs.
- 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 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.
Job Opportunities Data Analytics
Our Data Analytics Course will benefit professionals at all levels. The following table highlights the job role of Data Analytics based on experience level,
| Experience Level | Job Roles |
| Entry Level |
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| Mid Level |
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| Senior Level |
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How to Get Certified in Data Analytics?
Why Choose Edoxi for Data Analytics Training?
Here are the reasons why you should choose Edoxi for Data Analytics Training,
- Project-Based Learning: To solidify your knowledge and skills in Data Analytics we help you develop practical mastery through various projects.
- Acquire Industry-Ready Skills: Participants will acquire Industry-Ready Skills through hands-on exercises in real-life projects.
- Progressive Learning Path: Our structured curriculum comprises 6 modules. This course structure facilitates accessibility to complex topics starting from the fundamentals of Data Science.
- Focused Career Preparation: Interactive sessions and analytical problem-solving exercises effectively prepare participants for employment opportunities in Data Analytics.
Review & Ratings
FAQs
The following are some of the career options in Data Analytics,
- Data Analyst
- Database Designer
- Business Analyst
- Market Analyst
We ensure you receive hands-on training in the following areas,
- Transport Network Analytics: Participants analyze 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.
The global average salary of a Data Analyst is $82,000 per year.
Yes, we welcome beginners and provide additional support to help them learn.