Eclasess

Course available for lifetime after purchase

Data Analyst / Data Analysis with Power BI
Course Description:

Duration: 40 Hours (Daily 1 Hour Monday to Friday)
Eligibility: BSc/BTech/MCA (Passed out Years 2022-23-24)
Fresher Program: For recent graduates
Course Content:

MS SQL: 10 hours
Python: 10 hours
Snowflake: 5 hours
Microsoft Azure Cloud: 5 hours
Power BI: 10 hours
Learning Outcomes:

Proficiency in MS SQL, Python, Snowflake, Azure, and Power BI
Hands-on training and practical exercises
Program Structure:

Schedule: 1 Hour per day (Mon-Fri)
Interactive Sessions: Practical and hands-on
Pay After Placement Program:

Registration Fee: 1999/-
Placement Fee: 25000/- (After securing a job)
Benefits: No upfront tuition, payment post-placement
Additional Benefits:

Professional resume preparation
Job placement support (forwarding resumes to companies)
Free training and placement assistance
Eligibility Criteria:

Degree in BSc/BTech/MCA
Passed out in 2022, 2023, or 2024
Ideal for freshers
Contact Information:

Organization: [Eclasess IT Training]
Email: [eclasessads@gmail.com]
Phone: [+91-8143874700]
Website: [www.eclasess.com]

SQL Server Training Summary (10 Hours)
Day 1: Introduction to SQL Server and Installation
Overview: Importance of SQL Server in data management
Editions and Versions: Understanding different editions and versions
Installation: Installing SQL Server Management Studio (SSMS) and SQL Server Express Edition
Day 2: SQL Server Basics and System Databases
Basics: Databases, tables, and relationships
System Databases: Master, model, msdb, tempdb
Practical Exercises: Querying system databases for metadata
Day 3: Authentication and Security in SQL Server
Authentication Modes: Windows Authentication vs. SQL Server Authentication
Security Management: Logins, users, and roles
Best Practices: Implementing security measures for SQL Server instances
Day 4: SQL Commands and Data Manipulation Language (DML)
SQL Commands: SELECT, INSERT, UPDATE, DELETE
Hands-On Exercises: Data manipulation using DML commands
Transaction Management: Importance in DML operations
Day 5: Data Definition Language (DDL) and Data Control Language (DCL)
DDL Commands: CREATE, ALTER, DROP
DCL Commands: GRANT, REVOKE
Practical Examples: Managing database schema and access control
Day 6: SQL Keywords, Clauses, and Aggregate Functions
Keywords: Common SQL keywords
Clauses: WHERE, GROUP BY, HAVING, ORDER BY
Aggregate Functions: COUNT, SUM, AVG, MAX, MIN
Practical Exercises: Usage in queries
Day 7: String and Date/Time Functions in SQL
String Functions: CONCAT, LEN, SUBSTRING, CHARINDEX, REPLACE
Date/Time Functions: GETDATE, DATEADD, DATEDIFF, FORMAT
Hands-On Exercises: Manipulating strings and date/time values
Day 8: Constraints, Temporary Tables, and Table Variables
Constraints: PRIMARY KEY, FOREIGN KEY, UNIQUE, CHECK
Temporary Storage: Temporary tables and table variables
Practical Examples: Data integrity enforcement
Day 9: Common Table Expressions (CTE), Views, and Data Import/Export
CTE: Benefits, creating, and using CTEs
Views: Creating, modifying, and querying views
Data Operations: CASE WHEN, INSERT INTO...SELECT, SELECT INTO
Data Export: Using SQL Server Import and Export Wizard
Day 10: Transact-SQL (T-SQL) Fundamentals, Stored Procedures, and Data Transfer Operations
T-SQL Fundamentals: Language syntax and features
Stored Procedures: Advantages, syntax, execution
Hands-On Exercises: Creating and executing stored procedures
User-Defined Functions: Scalar, inline table-valued, multi-statement table-valued
Indexes: Creating, managing, and optimizing (clustered, non-clustered, filtered)



Python for Data Analytics Training Summary (10 Hours)
Days 1-2: Introduction to Python for Data Analytics
Overview: Importance and applications of Python in data analytics
Environment Setup: Installing Anaconda and setting up Jupyter Notebook
Basic Syntax: Introduction to Python syntax, data types, variables, operators, and expressions
Days 3-4: Data Structures in Python
Lists, Tuples, Sets: Creating and manipulating lists, tuples, and sets
Dictionaries: Key-value pairs, common operations
NumPy Arrays: Introduction to arrays and basic operations with NumPy
Days 5-6: Data Manipulation with Pandas
Introduction to Pandas: Importance and uses in data analysis
Series and DataFrame: Creating, manipulating, and accessing data
Data Indexing and Selection: Techniques for selecting and filtering data
Days 7-8: Data Cleaning and Preprocessing with Pandas
Data Cleaning Techniques: Identifying and correcting data issues
Handling Missing Data: Methods to handle and fill missing values
Data Aggregation and Grouping: Grouping data and performing aggregations
Days 9-10: Data Visualization with Matplotlib and Seaborn
Basic Plotting with Matplotlib: Creating simple plots (line, bar, histogram)
Advanced Plotting Techniques: Customizing plots and creating complex visualizations
Seaborn: Using Seaborn for statistical data visualization (scatter plots, box plots, heatmaps)


Snowflake Training Summary (5 Hour

Data Analyst Training for Freshers

Python Introduction

Maharashtra

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