Data Analytics

Data analytics is the process of examining raw data to draw conclusions about the information it contains. It involves applying statistical analysis, machine learning algorithms, and other techniques to identify patterns, trends, correlations, and insights within large datasets.

The goal of data analytics is to extract valuable insights that can inform decision-making & drive business improvements. Data analytics is widely used across various industries and domains, including business, finance, healthcare, marketing, and government.

It plays a crucial role in improving decision-making, optimizing processes, identifying opportunities, mitigating risks, and gaining a competitive advantage.

Curriculum:

• Python Introduction
• Installation And Setting Up Path
• Features
• Python Variables
• Input & Output And Import
• Why Learn Python
• Who Used Python
• Introduction
• Data Types
• Built In Functions
• Tuples
• Lists
• Sets
• Flow Control
• Functions
• File Handling
• Exception Handling
• Oops Concepts
• Os And System Services
• Multithreading
• Database Connectivity
• Xml And Json
• Regular Expression
• Tkinter
• Smtp
• Server Client Program
• Numpy
• Pandas
• Matplot Library
• Pandas Analyzing Data
• Tableau Introduction
• Data Connections In Tableau Interface
• Organizing And Simplifying Data
• Building Chart Types
• Advanced Chart Types
• Logic Statements
• Mapping
• Statistics
• Dashboard
• Power Bi Introduction
• Architecture
• Supported Data Sources
• Comparison With Other Bi Tools
• Data Modelling
• Dashboard Options
• Visualization Options
• Excel Integration
• Dashboards
• Dax Basics In Power Bi
• Administration Role
• Advanced Excel Introduction
• Conditional Formatting
• Pivot Tables
• Power Pivot
• Slicers And Charts
• Data And Validation
• Analyzing And Organizing Data
• Vba Ms Access
• Sql Introduction
• Basics Of Sql
• Installing Sql
• Sql Server Languages And Relational Databases
• Relational Database Terminology
• The Select Statement
• Sql Statement