• Master industry-standard tools like Excel, SQL, Python, R, Power BI, and Tableau through 180 hours of live training 
Core Modules in StarAgile Data Analytics Course
The Modules In Data Analytics are strategically organized to build competency progressively, starting with fundamental concepts and advancing to specialized techniques.
1. Introduction to Data Analytics & Business Understanding
This foundational module introduces students to the principles of data analytics and their business contexts. You'll learn how data drives decision-making across industries, understand different analytical approaches, and explore the role of data analysts in organizational success. The module establishes the conceptual framework for subsequent technical training.
2. Statistics & Probability for Data Analysis
Statistical literacy forms the backbone of practical data analysis. This module covers descriptive statistics, probability distributions, hypothesis testing, and inferential statistics. Students develop skills in interpreting data patterns, understanding variability, and making data-driven conclusions with statistical confidence.
3. Data Handling with Excel, SQL & Databases
Excel proficiency begins with cell referencing, aggregate functions, and logical operators, progressing to advanced features like PivotTables, Power Query, and dynamic arrays. The SQL component covers select statements, joins, subqueries, window functions, and database management principles. These skills enable efficient data extraction, transformation, and preliminary analysis.
4. Programming for Data Analytics (Python/R)
Python programming starts with fundamentals, including variables, data types, and control structures, then advances to specialized libraries like NumPy, Pandas, and Matplotlib. Students learn data manipulation, visualization, and automated analysis techniques. The module includes regular expressions, file operations, and exception handling for robust data processing workflows.
5. Data Visualization Modules (Tableau, Power BI)
Visualization training encompasses both Tableau and Power BI platforms. Students master chart creation, dashboard development, interactive filtering, and advanced visualization techniques. The Course Content Of Data Analytics includes hands-on practice with real datasets, enabling students to communicate insights effectively through compelling visual narratives.
6. Machine Learning Basics
This introductory machine learning module covers concepts in supervised and unsupervised learning, algorithm selection, model evaluation, and the fundamentals of predictive analytics. Students gain exposure to classification, regression, clustering, and pattern recognition techniques that enhance analytical capabilities.
Specialized Modules in StarAgile Data Analytics Syllabus
Advanced Data Analytics Modules address specialized applications and cutting-edge technologies relevant to modern analytical practice.
1. Big Data Tools & Cloud Integration
Students explore big data ecosystems, cloud-based analytics platforms, and distributed computing concepts. This module prepares analysts for enterprise-scale data challenges and modern infrastructure environments where traditional tools reach limitations.
2. Predictive Analytics & Advanced Techniques
Advanced analytical methods include time series forecasting, statistical modeling, and sophisticated prediction algorithms. Students learn to build predictive models, validate performance, and implement forecasting solutions for business applications.
3. Domain-Specific Modules in Data Analytics (Finance, Marketing, HR, etc.)
Industry-specific applications demonstrate how analytical techniques can be applied across various business domains. Students work with finance datasets, marketing analytics scenarios, human resources metrics, and operational data to understand sector-specific analytical requirements and methodologies.
Course Content of Data Analytics – Practical Learning at StarAgile
The practical-focused Data Analytics Syllabus emphasizes experiential learning through comprehensive hands-on components.
1. Hands-On Assignments and Case Studies
Regular assignments use real-world datasets from various industries, enabling students to apply theoretical concepts to practical scenarios. Case studies encompass sales performance analysis, customer insights, and operational analytics, offering diverse exposure to the analytical challenges faced by working professionals.
2. Capstone Project for Real-World Applications
The capstone project synthesizes learning across all Modules In Data Analytics, requiring students to complete end-to-end analytical projects. These projects demonstrate proficiency in data collection, cleaning, analysis, visualization, and insight communication, creating portfolio pieces for career advancement.
3. Tools Covered in the StarAgile Data Science Curriculum
The comprehensive data science curriculum encompasses a robust collection of practical tools, including:
SQL for database querying and management
Python for advanced programming and data analysis
R for statistical computing
Tableau for sophisticated data visualization
Google Sheets for collaborative data work
Excel for foundational data manipulation
4. Technologies Covered in the StarAgile Data Science Curriculum?
The technological framework encompasses advanced systems and methodologies, including:
Machine learning algorithms and frameworks and importance
Artificial intelligence and deep learning methodologies
Big data processing technologies (Hadoop and Spark)
Cloud computing platforms (AWS, Azure, Google Cloud Platform)
Cloud-based virtual labs with Jupyter Notebooks
Python libraries (NumPy and SciPy) for scientific computing
Neural networks and coding frameworks
Why Go Through the Course Syllabus Before Enrolling?
Understanding the Data Analytics Course Syllabus before enrollment ensures you make an informed decision about your career investment. The structured curriculum helps prospective students identify learning objectives, assess time commitments, and understand the progression from foundational concepts to advanced analytical techniques. This transparency allows you to align your career goals with the comprehensive training methodology offered.
FAQs on StarAgile Data Analytics Syllabus
1. What modules are included in the StarAgile Data Analytics course?
The Data Analytics Course Syllabus comprises core modules that cover statistics, programming (Python and R), database management (SQL), visualization tools (Tableau and Power BI), machine learning fundamentals, and specialized applications. Additional components include practical assignments, case studies, and a comprehensive capstone project.
2. Does the course cover coding and programming?
Yes, the Course Content of Data Analytics extensively covers Python programming, from fundamentals to advanced libraries such as Pandas and NumPy. Students also learn SQL for database operations and basic R programming concepts, ensuring comprehensive programming competency for analytical work.
3. How practical is the Data Analysis curriculum at StarAgile?
The Data Analysis Curriculum emphasizes practical application through real-world case studies, hands-on assignments, and a 6-month internship program. Students work with actual industry datasets and complete projects that simulate professional analytical challenges, ensuring job-ready skills upon graduation.
4. Are all modules included in both online and classroom training?
All Data Analytics Modules are available through a live online training format, providing interactive instruction without geographical limitations. The curriculum maintains consistency across delivery methods, ensuring comprehensive coverage of analytical concepts and practical applications regardless of the training format chosen.
The StarAgile Data Analytics Syllabus represents a comprehensive pathway to analytical expertise, combining theoretical foundations with practical applications that prepare graduates for successful careers in the rapidly growing field of data analytics.
