Data Analytics Course
Unlock the power of data with our comprehensive Data Analytics course in Coimbatore. Designed for aspiring professionals, this program covers essential tools like Excel, SQL, Python, and Power BI, equipping you with the skills to analyze, visualize, and interpret data effectively. Join us at Edukators and take the first step toward a successful career in the data-driven world!
Description
About Data Analytics Course In Coimbatore
Our Data Analytics course in Coimbatore is designed to equip you with the skills and knowledge needed to excel in the data-driven world. With a comprehensive curriculum covering essential tools like Excel, SQL, Python, and Power BI, you’ll learn how to collect, process, and analyze data to derive meaningful insights. The course offers practical, hands-on training, ensuring that you gain real-world experience in data cleaning, statistical analysis, and data visualization. Whether you’re starting your career or enhancing your existing skills, this course provides a solid foundation for success in business intelligence, data science, and analytics roles.
Know about our Data Analytics Trainers
Our Data Analytics trainers are experienced professionals with a strong background in data analysis and related fields. They bring years of practical knowledge from working in various industries, ensuring that you receive up-to-date, real-world insights. Our trainers are dedicated to helping you learn and understand the concepts clearly, providing personalized guidance throughout the course. With their expertise and teaching approach, you’ll gain the skills needed to succeed in the field of data analytics.
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Benefits of taking our DataAnalytics Course
Enrolling in our Data Analytics course, whether online or offline, comes with countless benefits. To start, you’ll build a solid foundation in analytics and gradually progress to solving real-world problems. For example, you’ll work on industry-based projects that sharpen your hands-on expertise. Most importantly, Data Analytics skills are highly versatile, opening pathways to roles in business intelligence, Data Science, and more. Furthermore, this course equips you with the confidence to make data-driven decisions and tackle complex challenges. Similarly, the tools and techniques you learn will apply across industries. So, take the first step with us and unlock your potential. But don’t just take our word for it—discover the difference for yourself!
Course Details
- What is Script?
- What is a program?
- Types of Scripts
- Difference between Script & Programming Languages
- Features of Scripting
- Limitation of Scripting
- Types of programming Language Paradigms
- Python Overview
- History of Python & Python Versions
- Python Features
- Types of python - CPython, jPython, PyPy
- Area of application of python
- Why do we need python?
- How python script works
- Python 2.7 and Python 3 difference
- What is PSF?
- What is pip? and how to use?
- What is IDE?
- Environment setup - Installation of Python
- Writing first script in python
- Interactive and Script Mode programming
- Compiler and interpreter difference
- How to make executable python file?
- What is syntax?
- What is variable?
- What is identifiers?
- What is keywords?
- What is comment and its types?
- Usage of Quatations
- How to use help and dir functions?
- Static typing and dynamic typing
- What is data type?
- String
- Integers
- List
- Tuple
- Dictionary
- Set & Frozen set
- Boolean data type
- Built in function of data types
- Mutable and immutable
- Arithmetic operators
- Comparison operators
- Assignment Operators
- Logical Operators
- Bitwise Operators
- Membership Operators
- Identity Operators
- Arithmetic Operators
- Ternary operator & nested ternary operator
- Grouping Statements: Indentation and Blocks
- If statement
- if else statement
- elif statement
- nested if, if else, elif statement
- one line if statement
- pass keyword
- for loop with else
- while loop with else
- continue and break
- range and xrange difference
- list, tuple, dict comprehension
- Built in function
- User defined function
- Nested function
- Recursive function
- *args and **kwargs function
- Global and nonlocal keywords usage
- Lambda function
- Reduce, map, filter functions
- Python closure
- Decorators
- Chaining Decorators
- Python Generators
- File handling in python
- Type of modes in file
- Example for writing a file
- Example for reading a file
- Example for reading and writing a image file
- What is exception?
- Try and Except Statement - Catching Exceptions
- Python Exceptions List
- Assertions in Python
- Try with Else Clause
- Finally Keyword in Python
- User-Defined Exceptions
- Handling multiple exception
- What is Modular Programming?
- What are Modules in Python?
- How to Import Modules in Python?
- Python import Statement
- Importing and also Renaming
- Python from...import Statement
- Locating Path of Modules
- Namespaces and Scoping
- Basic module writing example
- OOPs and Principles of object-oriented programming
- Object-oriented vs Procedure-oriented Programming languages
- Class
- Method
- Attributes types
- Object
- Parameter and Attributes difference
- Inheritance
- Polymorphism
- Encapsulation
- Abstraction
- Composition
- what is database? and why?
- Types of databases
- How SQL Works?
- What is RDBMS?
- What is a Table?
- What is a Field?
- What is a Record or a Row?
- What is a Column?
- SQL Constraints
- Data Integrity
- ACID properties
- What is Normalization and why it is needed?
- First Normal Form (1NF)
- Second Normal Form (2NF)
- Third Normal Form (3NF)
- Boyce-Codd Normal Form (BCNF)
- Fourth Normal Form (4NF)
- Fifth Normal Form (5NF)
- Data Definition - CREATE, ALTER, DROP, TRUNCATE
- Data Querying - LIMIT, WHERE, ORDERBY, COUNT, SUM, AVG, MAX, MIN, GROUPBY, HAVING
- Data Manupulation - SELECT, INSERT, UPDATE, DELETE
- Data Filtering - Use conditions (=, >, <, BETWEEN) in WHERE
- Data Control Language - GRANT, REVOKE
- Transaction Control Language - COMMIT, ROLLBACK,SAVEPOINT
- Inner Join
- Left Join
- Right Join
- Full Outer Join
- Cross Join
- Single-row Subqueries
- Multi-row Subqueries
- Correlated Subqueries
- Scalar Functions (e.g., LEN, SUBSTRING, ROUND)
- String Functions (e.g., CONCAT, UPPER, LOWER)
- Date and Time Functions (e.g., GETDATE, DATEADD, DATEDIFF)
- Conversion Functions (CAST, CONVERT)
- Query Optimization Techniques
- Indexing for Performance
- Analyzing Query Execution Plans
- Overview of Tableau and Data Visualization
- Understanding Tableau Desktop and Tableau Public
- Installation and Setup of Tableau Desktop
- Navigating the Tableau Interface
- Types of Data Sources
- Connecting to Databases and Files (Excel, CSV, SQL, etc.)
- Live vs Extract Data Connections
- Editing and Saving Data Sources
- Data Preparation using Tableau Prep
- Joining Tables and Blending Data
- Data Pivoting and Splitting
- Managing Null Values and Data Cleaning
- Aggregating and Grouping Data
- Creating Hierarchies and Sets
- Creating Text Tables (Crosstabs)
- Bar Charts and Line Charts
- Scatter Plots and Pie Charts
- Heat Maps and Highlight Tables
- Geographical Maps
- Tree Maps and Bubble Charts
- Dual-axis and Combined Axis Charts
- Gantt Charts for Project Management
- Waterfall Charts
- Box Plots and Histograms
- Motion Charts
- Types of Calculations: (Basic, Table , LOD (Level of Detail) Expressions)
- Using Parameters in Calculations
- Working with Date and Time Functions
- String Calculations
- Aggregate and Logical Functions
- Creating Interactive Dashboards
- Adding Filters and Actions
- Designing User-friendly Layouts
- Using Containers and Formatting
- Embedding Dashboards into Websites and Applications
- Creating Maps with Geographic Data
- Editing Locations and Custom Geocoding
- Creating Filled Maps and Symbol Maps
- Using Layers in Maps
- Spatial Functions and Distance Calculations
- Creating Story Points in Tableau
- Designing Narratives for Data Insights
- Exporting and Sharing Dashboards
- Tableau Presentation Best Practices
- Publishing Dashboards to Tableau Server or Tableau Online
- Managing User Permissions
- Data Refresh and Scheduling
- Tableau Server Administration Overview
- Tableau with Python Integration
- Tableau API Overview
- Tableau Extensions for Custom Features
- Tableau Prep for Advanced Data Preparation
- Improving Dashboard Performance
- Optimizing Data Connections and Extracts
- Reducing Workbook and Extract Size
- Best Practices for Efficient Dashboards
- What is Power BI?
- Power BI Components and Architecture
- Power BI Desktop vs. Power BI Service vs. Power BI Mobile
- Installing and Setting Up Power BI Desktop
- Power BI Interface Overview
- Connecting to Data Sources
- Importing Data from Excel, CSV, SQL Server, and Web
- Data Transformation and Shaping with Power Query Editor
- Understanding Data Models
- Creating and Managing Relationships between Tables
- Data Normalization and Denormalization
- Creating Calculated Columns and Measures
- Introduction to DAX (Data Analysis Expressions)
- Basics of DAX Syntax and Functions
- Common DAX Functions (SUM, AVERAGE, COUNT, DISTINCTCOUNT)
- Time Intelligence Functions (DATEADD, DATESYTD, SAMEPERIODLASTYEAR)
- Logical Functions (IF, SWITCH)
- Text Functions (CONCATENATE, LEFT, RIGHT)
- Advanced DAX Functions (CALCULATE, FILTER, ALL, ALLEXCEPT)
- Introduction to Visualizations
- Creating Basic Charts (Bar, Column, Line, Pie)
- Using Slicers and Filters
- Creating and Formatting Tables and Matrix Visuals
- Customizing Visuals (Colors, Labels, Titles, Tooltips)
- Creating and Using Bookmarks
- Creating Advanced Charts (Scatter, Waterfall, Funnel, Gauge)
- Using Custom Visuals from the Power BI Marketplace
- Conditional Formatting in Visuals
- Using R and Python Visuals
- Drillthrough and Cross-Report Drillthrough
- Designing Interactive Reports
- Using Themes and Templates
- Creating and Managing Dashboards in Power BI Service
- Pinning Visuals to Dashboards
- Creating Dashboard Tiles and Widgets
- Publishing Reports to Power BI Service
- Sharing Reports and Dashboards
- Workspaces, Apps, and App Workspaces
- Creating and Managing Dataflows
- Power BI Pro vs. Power BI Premium
- Installing Power BI Mobile App
- Viewing and Interacting with Reports on Mobile
- Creating Mobile-Optimized Reports
- Understanding Data Refresh in Power BI
- Configuring Scheduled Refresh
- Using Personal and Enterprise Gateways
- Handling Data Refresh Failures
- Introduction to Row-Level Security
- Creating and Managing Roles
- Implementing RLS in Power BI Desktop
- Testing and Validating RLS
- Overview of Power BI Administration
- Managing Users and Permissions
- Admin Portal and Tenant Settings
- Monitoring and Auditing Power BI Usage
- Power BI Governance and Best Practices
- Introduction to Power Query Editor
- Data Transformation Techniques
- M Language Basics
- Using Parameters in Power Query
- Advanced Data Shaping (Merging, Appending, Grouping)
- Integrating Power BI with Excel
- Using Power BI REST API
- Embedding Power BI Reports in Applications
- Integrating Power BI with Microsoft Teams and SharePoint
- Performing Advanced Analytics with Power BI
- Integrating with Azure Machine Learning
- Using Cognitive Services in Power BI
- Creating Predictive Models with Power BI
- Performing Advanced Analytics with Power BI
- Integrating with Azure Machine Learning
- Using Cognitive Services in Power BI
- Creating Predictive Models with Power BI
- Best Practices for Data Model Optimization
- Improving DAX Performance
- Optimizing Data Refresh
- Reducing Report and Dashboard Load Times
- Introduction to Power BI Custom Visuals
- Developing Custom Visuals with TypeScript and D3.js
- Packaging and Deploying Custom Visuals
Requirements
Job Opportunities in Data Analytic Course
A Data Analytics course opens up a wide array of job opportunities across various industries. Here are some common roles individuals can pursue after acquiring data analytics skills:
- Data Analyst
- Business Intelligence Analyst
- Data Scientist
- Market Research Analyst
- Data Engineer
- Operations Analyst
- Quantitative Analyst
- Data Visualization Specialist
- Product Analyst
- Risk Analyst
- Educator/Trainer
FAQ's about Data Analytic Course
The duration of a Data Analytics course typically varies based on the specific course and your chosen learning path. Introductory courses are shorter, while advanced ones take longer. In other words, the course length depends on its depth. Above all, the mode of instruction matters—in-class training follows a fixed schedule, while online courses offer flexibility. In addition, whether you enroll part-time or full-time also affects the duration. After that, you’ll be better equipped to choose a course that fits your needs. Similarly, understanding these factors will help you make the best decision. Therefore, join us to unlock these opportunities!
Yes, we provide certificates to the course-completed candidates after completing their final projects and assessments.
Certainly, we support candidates with interview preparation by encouraging extensive problem-solving practice and offering mock interviews. For instance, these mock interviews help you become familiar with the format, receive feedback, and boost your readiness. Above all, this approach builds confidence and enhances your skills. Therefore, join us to unlock these opportunities
Yes, we support our students even after course completion with placement assistance. This includes resume building, interview coaching, and networking opportunities to ease the transition from learning to employment. Above all, we ensure you are well-prepared for the job market. Therefore, join us to unlock these opportunities.
Edukators is the best Data Analytics training institute in Coimbatore, offering a comprehensive curriculum, hands-on learning, experienced instructors, placement support, local networking, and affordable fees. We ensure strong career prospects and practical skills development to help you succeed in the world of data.
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