Data Analytics Course in Coimbatore
Learn Data Analytics Course in Coimbatore designed for students and working professionals who want to build real-world analytical skills. This course covers Excel, SQL, Python, Power BI, and data visualization techniques, helping you analyze real datasets, solve business problems, and prepare for data analyst roles across industries.
Why Choose Our Data Analytics Course in Coimbatore?
- Hands-on training with real-time datasets
- Tools covered: Excel, SQL, Python, Power BI
- Industry-oriented Data Analyst projects
- Suitable for students & working professionals
Trusted by Students for Our Data Analytics Course
Practical Skills Covered in This Course
Learners highlight the practical training and real-world analytics projects.
What You’ll Learn in This Data Analytics Program
Learn from Expert Trainers
Learn from trainers who have real industry experience and explain concepts in a simple, easy-to-understand way, using real examples from their work.
Hands-On Learning Experience
Learning happens through practice. You work on exercises and real datasets so you understand how data analytics is used in everyday work situations. You work on real datasets, dashboards, and case studies based on business scenarios.
Master Popular Tools
Get practical exposure to tools like Excel, SQL, Python, Power BI, and Tableau to collect, analyze, and present data clearly and confidently.
Flexible Class timing
Classes are planned to suit students and working professionals, allowing you to learn comfortably without pressure or rushing through topics.
Placement Guidance & Career Support
Receive guidance for interviews, resume preparation, and career planning to help you move towards data analytics roles with confidence. Support focused on entry-level and mid-level data analyst roles, including resume and interview preparation.
Upcoming Batches
Classroom Training – Learn, Practice, Improve
- Learn in a structured classroom environment with guidance from experienced trainers.
- Practice analytics concepts using real datasets and practical exercises.
- Work on dashboards, reports, and case studies to understand real business scenarios.
Live Interactive Online Training
- Attend live trainer-led sessions with real-time doubt clarification.
- Learn through guided explanations, practical assignments, and examples.
- Access recorded sessions anytime to revise concepts at your own pace.
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Learning Outcomes from Our Data Analytics Course
Understand the Basics of Data Analytics
Learn what data analytics involves and how it is applied to solve everyday business problems. This helps you build a strong foundation before moving into practical tools and real-world projects.
Get Comfortable with Practical Tools
Gain hands-on experience with commonly used tools such as Excel, SQL, Python, Power BI, and Tableau to work with data, identify patterns, and generate useful insights through regular practice.
Work on Real Project Scenarios
Practice using real datasets and business case studies instead of sample examples. This helps you understand how data analytics is applied in actual company environments.
Create Clear Reports and Dashboards
Learn to present data through simple and meaningful reports and dashboards that are easy to interpret and useful for decision-making.
Prepare for a Data Analytics Career
With guided training and project exposure, build the confidence needed to attend interviews and take the next step toward analytics-related roles.
Syllabus of Data Analytics Course:
INTRODUCTION TO DATA ANALYTICS
- What is Data Analytics?
- Importance and real-world applications
- Roles: Data Analyst vs Data Scientist vs BI Developer
- Analytics process: Collection → Cleaning → Analysis → Visualization → Reporting
Descriptive, Diagnostic, Predictive, Prescriptive
- Structured vs Unstructured Data
- Basics of Databases and Tables
- Overview of Data Warehousing & ETL concepts
- Excel, SQL, Python, Power BI overview
- How tools integrate in a typical data workflow
EXCEL BASICS
- Excel interface and navigation
- Data types, cell formatting, and shortcuts
- Basic formulas and cell references (Absolute vs Relative)
- Remove duplicates, blanks, and errors
- Text-to-Columns, Flash Fill
- Data Validation and Conditional Formatting
- Handling Missing Data
- Text Functions: LEFT, RIGHT, MID, TRIM, CONCATENATE, TEXTJOIN
- Date & Time Functions: TODAY, NOW, DATEDIF, YEAR, WEEKDAY
- Logical Functions: IF, AND, OR, IFERROR, IFS
- Lookup Functions: VLOOKUP, HLOOKUP, XLOOKUP, INDEX-MATCH
- Statistical & Math Functions: SUMIF(S), COUNTIF(S), AVERAGEIF(S), ROUND, RANK.
- Sorting and Filtering
- What-If Analysis (Goal Seek, Scenario Manager)
- Named Ranges
- Data Tables
- Creating Pivot Tables
- Grouping, Slicers, and Filters
- Calculated Fields and Items
- Pivot Charts for Visualization
- Building interactive dashboards
- Linking charts and controls
Introduction to Database and SQL
- What is SQL and why it matters in analytics
- Understanding relational databases and ER models
- Tables, Keys, and Relationships
- Class and object
- Attributes
- Inheritance
- Overloading
- Overriding
- Data hiding
- Match function
- Search function
- Matching VS Searching
- Modifiers
- Patterns
- Aggregate: COUNT, SUM, AVG, MIN, MAX
- String: UPPER, LOWER, SUBSTRING, TRIM, CONCAT
- Date: GETDATE, YEAR, MONTH, DATEDIFF, DATEADD
- Numeric: ROUND, CEILING, FLOOR
- GROUP BY and HAVING
- Subtotals and Conditional Aggregations
- INNER, LEFT, RIGHT, FULL JOIN
- Self Joins and Cross Joins
- Subqueries in SELECT and WHERE
- Common Table Expressions (CTEs)
- INSERT, UPDATE, DELETE
- Creating and Altering Tables (DDL basics)
- Window Functions
- Case Expressions
- Pivoting and Unpivoting Data
- Writing analytical queries from real datasets
- Building reports and KPIs directly in SQL
- Python installation and IDEs
- Variables, Data Types, Type Casting
- Input, Output, Comments
- Operators and Expressions
- If-Else conditions
- For and While loops
- Nested loops and break/continue
- Lists, Tuples, Sets, Dictionaries
- List and Dictionary Comprehensions
- Defining and calling functions
- Arguments, Return values
- Importing modules and libraries
• Reading and Writing Files (CSV, Excel, JSON)
• Exception Handling
- Arrays vs Lists
- Array operations, slicing, indexing
- Statistical and mathematical function
- Series and DataFrame objects
- Importing and exporting data
- Data cleaning and transformation (dropna, fillna, duplicates)
- Filtering, sorting, merging, and grouping data
- Aggregations and pivot tables in Pandas
- Line, Bar, Scatter, Pie, Histogram, Box plots
- Customizing plots
- Correlation and Heatmaps
- Understanding data distribution
- Outlier detection
- Handling missing data
- Data summarization and reporting
- Cleaning a dataset, performing EDA and visualizing insights
POWER BI OVERVIEW
- Power BI components
- Installing Power BI Desktop
- Interface and On-Object interaction
- Connecting to Excel, SQL, and Web sources
- Power Query basics
- Data cleaning and shaping: remove columns, replace values, unpivot, group by.
- Merge vs Append Queries
- Relationships between tables
- Star schema vs Snowflake schema
- Cardinality and Cross-filter directions
- What is Report View
- Basic Visualization In Power BI
- Formatting of Visuals in Power BI
- Working with Maps in Power BI
- Geo Styling
- Formatting of Pages & Reports
- Working with Themes
- Visual-level, Page-level, Report-level filters & Others
- Drillthrough and Hierarchies
- Bookmarks and Selections
- Sync Slicers & Performance Analyzer
- Q&A in Power BI
- Decomposition Tree in PBI
- Smart Narrative in PBI
- Increase & Decrease Feature in PBI
- Get Quick Insights in PBI
- First DAX Function in Power BI
- Date Functions
- Text Functions
- Logical Functions
- Calculated Column vs Measures
- Filter Functions: CALCULATE Function
- Time Intelligence Functions
- DATEADD Function
- Quick Measures
- Concatenated List of Values & Star Rating
- Table Functions
- What is Power BI Service
- Creating an Account in PBI Service
- Publishing the Report to PBI Service
- Sharing & Collaboration of Power BI reports
- Exporting the Report
- Power BI Service Settings
1. Automatically Refresh PBI Reports - Dashboard in Power BI
1. Creating a Dashboard in PBI Service
2. Dashboard Options
- End-to-end Dashboard using combined sources
- KPI design and storytelling with data
Our Data Analytics & Business Intelligence Course Trainer
Selva Sheeba B
Data Analyst | BI Trainer | SQL & Python Mentor
Selva Sheeba is an experienced data analytics professional with strong expertise in SQL, Python-based analysis, and modern BI tools. Her training approach turns raw data concepts into practical, business-focused insights that learners can immediately apply in real-world scenarios.
3+ Years of Experience in Teaching & Data Analytics
100+ Batches Trained Across India
2000+ Students Mentored Across Multiple Platforms
Technical Expertise
- MySQL and database querying fundamentals
- SQL for data analysis and query optimization
- Data analysis workflows and automation concepts
- Data cleaning, visualization, and storytelling techniques
- Excel for analytics and reporting
- End-to-end data projects and portfolio development
Selva Sheeba B
Data Analyst | BI Trainer | SQL & Python Mentor
Selva Sheeba brings a practical, business-oriented approach to data analytics training. Learners work with real datasets such as sales data, customer performance reports, and operational dashboards, helping them understand why data changes, not just how to use tools. Each learner builds at least one complete dashboard and learns how to explain insights clearly, similar to real workplace scenarios. Career guidance is provided based on individual progress, practice level, and learning goals.
3+ years of experience in data analytics and teaching
100+ batches trained across India
2000+ learners mentored across multiple platforms
Learning Experience
Learners follow a hands-on, structured learning approach focused on working with datasets, analyzing trends, building dashboards, and explaining insights clearly. Regular practice and real analytics use cases help learners gain confidence and industry readiness.
Career Support & Placement Guidance for Data Analytics Learners
1.Complimentary Placement Assistance
- Every learner receives structured career support, including interview preparation, soft skills guidance, and job-readiness support.
- The focus is on helping learners understand real hiring expectations and attend interviews with confidence, not pressure.
2.Realistic Mock Interviews
- Mock interviews are conducted based on real interview patterns commonly followed in analytics and IT roles.
- These sessions help learners improve communication skills, explain technical concepts clearly, and understand how interviews work in real scenarios.
3. Career Opportunities with Tech Companies
- Eligible learners are guided toward suitable entry-level opportunities through referrals and hiring support, based on skill readiness and interview performance.
- Career growth depends on individual effort, practice consistency, and performance during interview processes.
4. Resume Building Support
- Trainers help learners create clear, role-focused resumes that highlight analytics skills, tools, and project experience.
- Resume preparation is integrated into the course, especially during the job-readiness phase.
Benefits of Data Analytics Course Certification
At Xplore IT Corp, the certification reflects practical learning and hands-on experience gained during the course, not just course completion.
Industry-Validated Certification
The certification demonstrates your understanding of core analytics concepts and commonly used tools applied in real working environments.
Supports Career Progress
Strengthens your profile when applying for entry-level and junior analytics roles by showcasing structured learning and practical exposure.
Lifetime Validity
The certification does not expire and continues to represent your foundational knowledge and learning effort as you grow professionally.
Easy Digital Sharing & Verification
The certificate can be added to LinkedIn profiles, resumes, and portfolios, making it easy for recruiters to view and verify learning credentials.
Work-Based Learning Experience
Backed by practical tasks and projects that reflect how analytics work is performed in real business scenarios, not just theoretical learning.
Companies Our Students Work In
Through hands-on training and real project exposure, learners from Xplore IT Corp have progressed into roles across analytics, IT services, and product-based organizations in India and abroad. Their learning experience helps them adapt to real workplace expectations and professional environments.
Voices of Our Graduates
From classroom learning to real career journeys, hear directly from learners as they share their experiences, growth, and learning outcomes after completing the program.
Benefits of Learning Data Analytics at Xplore IT Corp
Job-Ready Skill Development
The training focuses on practical skills that companies expect in entry-level roles. Learners build a strong foundation through hands-on practice without unnecessary pressure.
Practical Industry Exposure
Students work with both front-end and back-end data tools, helping them understand how complete data projects are handled in real working environments.
Clear Career Direction
After completing the course, students are prepared to explore roles such as Data Analyst, Business Analyst, Data Scientist, and Data Engineer, based on their skills and interests.
Better Professional Networking
Learners get opportunities to interact with trainers, peers, and industry professionals through workshops and learning activities that support long-term career growth.
Real-World Project Experience
Through tool-based learning and guided projects, students gain confidence in handling real data tasks and working independently on analytics projects.
Continuous Learning & Exploration
Access to learning resources, datasets, and guided practice helps learners explore topics in more depth and improve their skills over time.
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Career Opportunities After Completing the Data Analytics Course
Data Analytics
- Work with data to identify trends, generate insights, and support business decision-making.
- Key skills: Excel, SQL, Python, Data Visualization, Tableau
- Average Salary: Entry-level to mid-level roles (varies by company and experience)
Business Analytics
- Focus on improving business processes by analyzing data and converting insights into actionable strategies.
- Key skills: Data Analysis, Business Strategy, Communication, Excel, Power BI
- Salary: Based on role scope and industry
Data Engineer
- Build and manage systems that collect, process, and store large volumes of data efficiently.
- Key skills: SQL, Python, ETL Tools, Big Data Technologies, Cloud Platforms
- Average Salary: Depends on technical expertise and project exposure
Data Scientist
- Work on advanced analytics, modeling, and problem-solving using statistical and machine learning techniques.
- Key skills: Python, Statistics, Data Visualization, Machine Learning Concepts
- Average Salary:Experience and role-specific
Marketing Analyst
- Analyze customer behavior and campaign performance to improve marketing effectiveness and ROI.
- Key skills: Google Analytics, Excel, Power BI, Reporting, SEO Metrics
- Average Salary: Entry to mid-level roles across industries
Financial Analyst
- Analyze financial data to support investment decisions, risk assessment, and planning.
- Key skills: Excel, Power BI, Financial Modeling, SQL, Statistics
- Average Salary:Depends on domain and company size
Data Visualization Specialist
- This role focuses on converting complex datasets into clear, visually engaging dashboards and reports for decision-makers.
- Key skills: Tableau, Power BI, Data Storytelling, Dashboard Design, UI Concepts.
- Average Salary:Project and skill dependent
Our Placement Sessions
Frequently Asked Questions (FAQ)
You will learn core analytics concepts such as data collection, data cleaning, analysis, visualization, and reporting. The training focuses on practical skills using real datasets to help you understand how analytics is applied in real business scenarios.
No prior programming experience is required. The course starts from the basics and gradually introduces tools and concepts in a beginner-friendly manner, making it suitable for both students and working professionals.
The course duration varies depending on the learning mode and batch schedule. Flexible timelines are available to accommodate both full-time learners and working professionals.
Yes, learners work on real datasets, case studies, and guided projects that reflect real-world analytics use cases and business problems.
Yes, periodic assessments, practical exercises, and project reviews are used to help learners track their progress and strengthen understanding.
Yes, the course covers commonly used analytics tools and techniques such as Excel, SQL, Python, and data visualization platforms, with a focus on practical usage rather than theory alone.
Yes, learners are trained in data cleaning, transformation, and visualization techniques to create clear reports and dashboards for decision-making.
Career guidance is provided through interview preparation, resume support, and mock interviews. Career outcomes depend on individual skills, effort, and interview performance.
