Data Analysis Training in Hyderabad
Call @ 7997457228. Eclasess institute offers the best Data Analysis Training in Hyderabad with hands-on practice on live projects and 100% job assistance.
Becoming a Data Analyst allows you to play a crucial role in decision-making processes by interpreting complex data to uncover trends and insights. This in-demand career offers opportunities across various industries, promises excellent growth potential, and provides the satisfaction of directly impacting business strategies and outcomes. This Data Analyst training will enable you to master descriptive and inferential statistics, hypothesis testing, regression analysis, data blending, data extraction, and forecasting.
What are the Data Analysis course learning Objective
In this Data Analyst certification course, you will learn the latest analytics tools and techniques, how to work with SQL databases and basic sql queries, the programming language of Python, raw data manipulation, the art of creating data visualizations, and how to apply statistics and predictive analytics.
Data Analysis Course Content
- Hour 1: Introduction to Database Systems
-
What is DBMS (Database Management System)?
- Definition and functions
- Key features and advantages
-
What is RDBMS (Relational Database Management
System)?
- Definition and key features
- Differences between DBMS and RDBMS
- Hour 2: SQL Server Overview
-
What is SQL Server Software?
- Introduction to SQL Server
- Overview of SQL Server features and capabilities
-
Different Versions and Editions of SQL Server
- Overview of SQL Server versions (e.g., 2019, 2022)
- Comparison of editions (Express, Standard, Enterprise)
- Hour 3: Installation and Configuration
-
How to Install SQL Server
- Step-by-step installation process
- Configuration options during installation
-
How to Install SQL Server Management Studio
(SSMS)
- Installation process
- Basic setup and configuration
- Hour 4: Instances and Authentication
-
What is a Named Instance and Default Instance?
- Definitions and differences
- When to use each type
-
Different Types of Authentication
- SQL Server Authentication
- Windows Authentication
- Pros and cons of each type
- Hour 5: Database and Tables Creation
-
What are System Databases?
- Overview of system databases (master, model, msdb, tempdb)
-
How to Create a Database
- SQL commands for creating databases
- Configuration options
-
How to Create Tables
- SQL commands for creating tables
- Table design best practices
- Hour 6: Data Types and SQL Commands
-
Different Types of Data Types
- Overview of SQL Server data types (int, varchar, datetime, etc.)
-
What are DML and DDL?
- Definitions and examples of DML (Data Manipulation Language) and DDL (Data Definition Language)
- Hour 7: Operators and Keywords
-
What are Operators in SQL Server?
- Types of operators (arithmetic, comparison, logical)
-
What are Keywords in SQL Server?
- Common SQL keywords and their uses
- Hour 8: Functions and Advanced SQL Concepts
-
What are Aggregate Functions?
- Examples and usage (SUM, AVG, COUNT, etc.)
-
What are String Functions?
- Examples and usage (LEN, SUBSTRING, REPLACE, etc.)
-
What are DateTime Functions?
- Examples and usage (GETDATE, DATEADD, DATEDIFF, etc.)
-
What are Analytical Functions?
- Examples and usage (ROW_NUMBER, RANK, NTILE, etc.)
- Hour 9: Joins and Subqueries
-
Different Types of Joins in SQL Server
- INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN
-
What is a Subquery?
- Definitions and examples
- Correlated Subquery
- Non-Correlated Subquery
- Hour 10: Advanced SQL Techniques
-
What is a CASE WHEN Statement?
- Syntax and examples
-
INSERT INTO ... SELECT and SELECT INTO Syntax
- Examples and use cases
-
What are Views and Different Options on Views?
- Creating and managing views
- View options and performance considerations
- Hour 11: Temporary and Table Variables
-
What are Table Variables?
- Definition and usage
-
What are Temp Tables?
- Local Temp Tables
- Global Temp Tables
- Hour 12: Common Table Expressions (CTEs)
-
What is a CTE?
- Syntax and examples
- Use cases and benefits
- Hour 13: Stored Procedures and Functions
-
What is a Stored Procedure?
- Creating and executing stored procedures
- Error handling with TRY...CATCH
-
What are User-Defined Functions?
- Scalar Functions
- Inline Table-Valued Functions
- Multi-Statement Table-Valued Functions
- Hour 14: Indexing and Performance
-
What is an Index?
- Definition and importance
-
Different Types of Indexes
- Clustered vs. Non-Clustered Indexes
- Unique Indexes, Composite Indexes
-
How Indexing Improves Query Performance
- Examples and best practices
- Hour 15: DML vs. DDL
-
What is the Difference Between DML and DDL?
- Detailed comparison and examples
- Hour 16: Filtering and Aggregation
-
What is the Difference Between WHERE and
HAVING Clauses?
- Usage and differences
-
Union vs. Union All
- Differences and examples
- Hour 17: Advanced SQL Queries
-
Different Types of Joins in Practice
- Examples and scenarios
-
Finding EmployeeName and ManagerName Using
Self Join
- SQL query example
-
Finding 2nd Highest Salary Using Different
Methods
- Subquery
- CTE
- TOP Keyword
- Hour 18: Data Manipulation Techniques
-
Finding Running Total of Sales
- SQL query example
-
Removing Duplicate Records Using CTE
- SQL query example
-
Finding Duplicate Records Using GROUP BY and
HAVING Clause
- SQL query example
- Hour 19: Practical Exercises and Examples
-
Hands-On Practice
- Practical exercises based on course topics
-
Mini-Project
- Implement a real-world scenario using SQL Server
- Day 1: Introduction to Python
- Python installation and setup
- Introduction to Jupyter Notebooks
- Basic syntax, variables, and data types
- Day 2: Control Structures
- Conditional statements (if, elif, else)
- Loops (for, while)
- Break and continue statements
- Day 3: Functions and Modules
- Defining and calling functions
- Function arguments and return values
- Importing and using modules
- Day 4: Data Structures
- Lists and list operations
- Tuples and tuple operations
- Dictionaries and dictionary operations
- Sets and set operations
- Day 5: Working with Strings
- String methods and formatting
- Regular expressions
- Day 6: File Handling
- Reading from and writing to files
- Working with different file types (txt, csv)
- Day 7: Error Handling and Exceptions
- Try, except, finally
- Custom exceptions
- Week 2: Numpy and Pandas Basics
- Day 8: Introduction to NumPy
- NumPy arrays and basic operations
- Array indexing and slicing
- Day 9: NumPy Continued
- Mathematical functions with NumPy
- Broadcasting and vectorized operations
- Day 10: Introduction to Pandas
- Series and DataFrame creation
- Basic DataFrame operations
- Day 11: DataFrame Manipulation
- Indexing and selecting data
- Handling missing data
- Day 12: Data Cleaning with Pandas
- Renaming columns, dropping columns
- Changing data types
- Day 13: Working with Dates and Times
- Date and time manipulation with Pandas
- Time series analysis basics
- Day 14: Data Aggregation and Grouping
- GroupBy operations
- Aggregation functions
- Week 3: Data Visualization
- Day 15: Introduction to Matplotlib
- Basic plotting with Matplotlib
- Customizing plots (titles, labels, legends)
- Day 16: Advanced Matplotlib
- Subplots and multiple plots
- Saving and exporting plots
- Day 17: Introduction to Seaborn
- Basic plots with Seaborn
- Statistical plots
- Day 18: Advanced Seaborn
- Customizing Seaborn plots
- Pairplots and heatmaps
- Day 19: Plotly and Interactive Visualizations
- Introduction to Plotly
- Creating interactive plots
- Day 20: Data Visualization Projects
- Mini-project: Create a dashboard with interactive visualizations
- Day 23: Data Preprocessing
- Feature engineering
- Data transformation and scaling
- Day 24: Case Study
- Guided project: Analyzing a real-world dataset (e.g., Titanic dataset, financial data, etc.)
- Day 25: Capstone Project
- Define your own project
- Collect, clean, and analyze the data
- Present your findings with visualizations
- Days 1-2: Introduction to Power BI Desktop (2 Hours)
- Overview of Power BI: Importance in data visualization.
- Installation: Installing Power BI Desktop.
- Interface Familiarity: Navigating the Power BI interface.
- Data Connections: Connecting to various data sources.
- Data Transformation: Loading and transforming data using Power Query Editor.
- Days 3-5: Data Transformation with Power Query Editor (3 Hours)
- Power Query Editor Functionalities: Basic and advanced features.
- Data Cleaning and Shaping: Techniques for data cleaning and shaping.
- Advanced Transformations: Merging queries, creating conditional and custom columns.
- Data Profiling: Data type handling and profiling.
- Days 6-7: Introduction to Power BI Visualizations (2 Hours)
- Basics of Visualizations: Creating and formatting basic visualizations (bar charts, line charts, pie charts, etc.).
- Enhancing Visuals: Adding slicers, filters, and drill-through options.
- Days 8-9: Advanced Visualizations and Customizations (2 Hours)
- Custom Visuals: Using custom visuals from the marketplace.
- Themes and Templates: Applying consistent branding.
- Interactive Reports: Creating interactive reports with bookmarks and buttons.
- Day 10: Introduction to Power BI Online (1 Hour)
- Power BI Online Overview: Understanding Power BI Service.
- Publishing Reports: Publishing from Power BI Desktop to Power BI Online.
- Collaboration: Using workspaces and sharing options.
- Day 1: Introduction to Microsoft Azure and Power BI Integration (1 Hour)
- Overview of Microsoft Azure: Introduction to Azure services and their benefits.
- Power BI Integration: Understanding how Azure enhances Power BI capabilities.
- Use Cases: Examples of Azure services that complement Power BI.
- Day 2: Power BI and Azure SQL Database (1 Hour)
- Connecting to Azure SQL Database: Steps to connect Power BI to Azure SQL Database.
- Data Visualization: Using Azure SQL Database data in Power BI for visualization.
- Practical Exercise: Hands-on practice in setting up the connection and creating basic visualizations.
- Day 3: Power BI and Azure Synapse Analytics (1 Hour)
- Integration with Azure Synapse Analytics: How to integrate Power BI with Azure Synapse Analytics.
- Advanced Analytics: Leveraging Azure Synapse for advanced analytics in Power BI.
- Practical Exercise: Creating complex visualizations and performing advanced analytics.
- Day 4: Power BI and Azure Data Lake Storage (1 Hour)
- Connecting to Azure Data Lake Storage: Steps to connect Power BI to Azure Data Lake Storage.
- Big Data Analytics: Utilizing Azure Data Lake Storage for big data analytics in Power BI.
- Practical Exercise: Importing and analyzing large datasets using Power BI.
To Speak With an Expert
+91 7997457228
- Duration 90 Hours
- Students 5616
- Days 90 Days
- Resume Preparation Yes
- Interview Guidance Yes
