To become a data analyst, you will need to have strong analytical skills and be comfortable working with large amounts of data. You should also have a solid foundation in mathematics, statistics, and computer science. A degree in a related field, such as computer science or statistics, can be helpful in preparing for a career in data analysis. In addition to your education, you should be comfortable using statistical software and data visualization tools, such as Excel and Tableau. You should also be able to communicate your findings effectively, both orally and in writing. With the right combination of education, skills, and experience, you can succeed as a data analyst in a variety of industries, including finance, healthcare, and marketing.

Course Duration & Payment Details

Days: Saturday & Sunday only
Timing: 07:30 to 10:00pm
Duration: 60 hours (3 months)
Starting from:
Last Date of Registration:

Monthly Fee: Rs 7,000/- (3 months)
How to Pay: Online Payment
Bank: Habib Bank Limited
Title: Irfan
Account No: 0011557900292501 | PK02HABB0011557900292501

Training Highlights

Excel Data Analysis

Cleaning and Preparing Tools
Sorting and Filtering
Advanced Formulas and Functions
– Logical Functions: IF, AND, OR
– Math Functions: SUM, COUNT, SUBTOTAL
– Text Functions: LEFT, RIGHT, CONCATENATE, LEN, LOWER, UPPER, PROPER
– Date Functions: YEAR, MONTH, DAY, EOMONTH, DATE
– Conditional Functions: SUMIF, COUNTIF, SUMIFS, COUNTIFS
– Financial Functions: PMT, PV, FV, RATE, NPER, NPV, IRR
– Lookup Functions: Vlookup, Hlookup, Index, Match, Choose, Indirect, Offset
Forecasting
Data Analysis ToolPak
WhatIf Analysis
Linear Regression Analysis
Data Connection and Preparation
Charts and Visualization Techniques
Excel as a BI Tool
Data Management and Modeling: Core Excel and the Excel Data Model
Advanced PivotTable: Importing Data
Advanced PivotTable: Preparing Data for Analysis
Advanced PivotTable: Creating and Manipulating PivotTables
Data Management and Modeling: PowerPivot
Data Acquisition: Data Explorer
Data Preparation and Transformation: Power Query
Creating an Interactive Dashboard

Managing Data with ETL/SSIS/SQL

ETL Environment Setup
– Visual Studio Workload
– ETL Process
– What is SSIS
– SSIS Project
– Flat file connection manager
– Remap Column Data Types
– Add and Configure OLE DB Connection Manager
– Add a data flow task
– Add and Configure Flat file source
– Add and Configure Lookup Transformation
– Add and Configure Lookup For DateKey Transformation
– Add and Configure OLE DB Destination
– Test and Run Package

Basic SQL Queries

– Create Table and Insert Data
– SELECT (DISTINCT) Statement
– WHERE clause
– AND, OR, NOT, IN, and BETWEEN Operators
– LIKE Operator
– INSERT INTO and DELETE FROM Statements
– UPDATE SET Statement
– ORDER BY Statement- DateTime functions

Intermediate Statement and Functions

– Aggregate Functions
– SQL Subqueries
– Group BY Statement
– HAVING Statement
– SQL JOINS
– CASE Statement
– Working with Date Functions

Advanced Tips and Tricks in SQL
– Best Practices
– Comments in SQL
– Export and Import Data
– Unique key constraint

Data Visualization in Power BI

Visualization Best Practices
Which Chart is the Best?
Basic Charts
Interaction of Visuals
Color Formatting
Setting Sort Order
Tooltips
Slicers & Timeline Slicers
Cross Filtering and Highlighting
Visual, Page, and Report Level Filters
Drill Down/Up
Hierarchies
Map Visuals
Scatter Chart
Line Chart
Table and Matrix with Conditional Formatting
Multiple levels of filters
Using custom slicers
Switch between web/phone layout
Inserting shapes, images, and text boxes
Drill Down
Tooltip
Hyperlink & Bookmark
Working with Custom Visuals
Data Transformation / Edit Queries
– Combine Queries
– Transformations
– Table Transformations
– Text Transformations
Power Query Formula Language: M
Power BI – Data Modeling
– Understanding how LEFT, RIGHT, INNER, and OUTER joins work
– DAX Functions
Artificial Intelligence (AI) Visuals
Power BI service
Gateways
Integration with PowerPoint

Working with Python Visuals in Power BI

– Installation Anaconda
– Data Frame Functions
– Enabling Python in Power BI
– Importing data using a Python Script
– Working with Python libraries – Pandas & Matplotlib
– Using a Python script to Create Visualizations in Power BI