Since data today has become an essential aspect for businesses, the practice of working with data to find useful information which can be used to make informed decisions in business is very crucial. No organization can survive in this digital age without studying and analyzing data.
With this growing trend, data analyst skills are in high demand.
Curriculum
Domain 1—BUSINESS INTELLIGENCE
A. Introduction to business intelligence
- what is business intelligence?
- why business intelligence
- Data Warehousing.
- Data warehouse Architecture.
- OLTP VS OLAP
- Business intelligence tools
- Business intelligence vs business analytics
B. Business intelligence with Microsoft Excel
- Introduction to Excel
- Data cleaning and preparation
- Formatting, conditional formatting
- Sorting and Advanced filtering
- Functions and formulas
- Data validation
- Business Analytics with excel
- Data visualization and dashboarding
- Excel-VB and macro
C. Business intelligence with Tableau
- Introduction
- Connecting to various data sources
- Tableau Advanced reports
- Structuring Data
- Filters
- Charts and graphs
- Data visualization
- Working with Metadata
- Calculations
- Dashboard and stories
- Analytics
- Level of Details expression
- Geographical visualization
D. Business intelligence with Power BI
- Introduction
- What is power BI?
- Why Power BI
- Key Benefits of Power BI
- Components of Power BI
- Power BI Architecture
- Power BI building blocks
- Data Model
- Data Analysis Expression (DAX)
- Power BI Desktop and Data Transformation
- Transform, wrangle, shape, and Modeling Data.
- Visualization
- Connectivity to Data sources
- Power BI Report Server
- Analytic in Power BI
Domain 2—STATISTICS AND PROBABILITY
A. Descriptive Statistics
- Describing data with tables and graphs
- Frequency distribution
- Describing data with Averages
- Describing data with Variability
- Normal distributions and Standard (Z) Scores
- Correlation
- Regression
B. Probability and Inferential Statistics
- Population and samples
- Probability
- Sampling distribution of Mean
- Hypothesis Testing-Z Test
- Estimation (Confidence Intervals)
- T-Test for one sample
- T-Test for two samples
- Analysis of variance (One factor)
- Analysis of variance (two factors)
- Chi-Square
Domain 3—PROGRAMMING
A. Python programming
- Python Basics
- Python Data Structure
- Conditional statements
- Logical statements
- Functions
- Working with files
- Modules
- OOP
- Python scripts in BI
- Python integration with Power BI
B. Python libraries
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Scrapy
- Beautiful soup
Domain 4—DATABASE
A. SQL
- What is SQL?
- SQL-RDBMS Concepts(
- Types of RDBMS
- Database Normalization
- SQL Syntax
- Data types
- Operators
- SQL commands
- DDL (create, Alter, Drop)
- DML (Insert, Update, Delete)
- DCL (Grant, Revoke)
- DQL (Select)
- SQL constraint
- SQL clause
- Min, Max, Count, Avg, Sum, Like, Wildcard
- Joins
Who should learn Data Analysis?
- Financial Analyst
- Marketing Analyst
- Healthcare Analyst
What will I be able to do at the end of the training?
- Learn how to visualize and present findings in the dashboard.
- How to perform data wrangling.
- How to clean and organize data for analysis.
