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Batch Date: May 1st @8:00AM

Faculty: Mr. Venkat (8+ Yrs Of Exp,..)

Duration: 2 Months

Venue :
DURGA SOFTWARE SOLUTIONS,
Flat No : 202, 2nd Floor,
HUDA Maitrivanam,
Ameerpet, Hyderabad - 500038

Ph.No: +91- 9246212143, 80 96 96 96 96

Syllabus:

DATA ANALYST

1. Excel for Data Analytics

1.1 Excel - Introduction

• Excel Window Navigation
• Primary Operations
• Cells and Columns Management
• Copy, Paste, and Paste Special Operations

1.2 Excel - Logical Functions

• IF, AND, OR, NESTED IFs, IFERROR, etc.

1.3 Excel - Arithmetic Functions

• AVERAGE, SUBTRACTION, DIVISION, MULTIPLICATION, etc.

1.4 Excel - Combination of Arithmetic and Logical Operations

• Combining both types of operations to create complex formulas

1.5 Excel - Lookup and Reference Functions

• VLOOKUP, HLOOKUP, INDEX, MATCH, etc.

1.6 Excel - Date and Text Functions

• DATE, DAY, MONTH, YEAR, TEXT, CONCATENATE, etc.

1.7 Excel - Data Summarization and Visualization

• Pivot Tables
• Charts (Bar, Line, Pie, etc.)
• Conditional Formatting for highlighting trends

1.8 Excel - Exploratory Data Analysis (EDA)

• Performing basic data analysis using built-in Excel tools

1.9 Excel - Project on Creating Real World Dashboard Using Excel

• Creating interactive dashboards with Excel for business insights

1.10 Excel - Project on Creating Real World Business Report Using Excel

• Developing professional business reports integrating data from multiple excel Sources
  

2. SQL for Data Analytics

2.1 SQL - Introduction

• Basics of SQL and its importance in data analytics

2.2 SQL - Understanding Databases and Table Structures

• Database schemas, tables, columns, and relationships

2.3 SQL - Querying Data

• SELECT, WHERE, and basic querying techniques

2.4 SQL - Sorting Data

• Sorting results using ORDER BY clause

2.5 SQL - Filtering Data

• Filtering with WHERE, BETWEEN, IN, LIKE, and pattern matching

2.6 SQL - Joining Data

• INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN, and understanding relationships

2.7 SQL - Grouping Data

• Using GROUP BY, HAVING, COUNT, SUM, AVG, etc.

2.8 SQL - Subqueries

• Writing nested queries for more advanced data analysis

2.9 SQL - Set Operators

• Using UNION, INTERSECT, and EXCEPT to combine result sets

2.10 SQL - Advanced SQL Functions

• Window functions, aggregate functions, and other advanced SQL techniques

3. Python for Data Analytics

3.1 Python - Data Types

• Strings
• Numbers (Integer, Float)
• Booleans (True/False)
• Operators (Arithmetic, Comparison, Logical)

3.2 Python - Data Structures

• Lists, Tuples, Dictionaries, Sets

3.3 Python - Programming Constructs

• If, Else, and Elif Statements
• Loops (for, while)
• Exception Handling (try-except)
• File I/O Operations (read, write files)

3.4 Python - Data Manipulation with Libraries

• Using Pandas, Numpy for data cleaning and transformation

3.5 Python - Project on Exploratory Data Analysis and Visualization Using Python

• Data analysis and visualization with Pandas, Numpy, Matplotlib, Seaborn

3.6 Python - Project on Automating Excel Business Report Using Python

• Automating report generation from multiple Excel files using Pandas and OpenPyXL

4. R Programming for Data Analysts

4.1 R - Data Types

• Numeric
• Integer
• Complex
• Character
• Logical

4.2 R - Data Structures

• Vectors
• Matrices
• Lists
• Data Frames
• Factors

4.3 R - Programming Constructs

• If, Else, and Elif Statements
• Loops (for, while)
• Exception Handling
• File I/O Operations (read/write)

4.4 R - Data Manipulation

• Data transformation using dplyr, tidyr, and other R packages

4.5 R - Project on Exploratory Data Analysis and Visualization Using R

• Real-world data analysis using R libraries such as ggplot2, dplyr, and tidyr

4.6 R - Project on Automating Excel Business Report Using R

• Automating report generation and manipulation of Excel data using R (readxl, openxlsx)

5. POWER BI

Module 1: Introduction

Introduction to Power BI, Downloading Power BI Desktop, Installing Power BI Desktop, Connecting to Power BI Desktop

Module 2: Working with Transformations (Power Query Editor)

Changing Data Types, Combining Multiple Tables, Entering Data Manually, Formatting Dates, Using Joins, Creating Pivot Tables, Reordering and Removing Columns, Renaming Columns, Renaming Tables, Splitting Columns, Unpivoting Columns

Module 3: Visualizations in Power BI

Area Chart, Bar Chart, Card Visualization, Column Chart, Donut Chart, Pie Chart, Line Chart, Table, Matrix, Ribbon Chart, Scatter Chart, Map Visualization, Tree Map, Waterfall Chart, Formatting All Chart Types

Module 4: Power BI Filters

Using Slicers, Basic Filters, Advanced Filters, Top N Filters, Filters on Measures

Module 5: Power BI Calculated Fields

Calculated Columns, Calculated Measures, Calculated Tables, Creating Conditional Columns

Module 6: Dashboards

Introduction to Dashboards, Creating a Dashboard

Module 7: DAX (Data Analysis Expressions)

Aggregate Functions, Date Functions, Logical Functions, Math Functions, String Functions, Trigonometric Functions