סילבוס Data Analyst

Introduction
Match&Index /hlookup / vLookup / xlookup
Functions Agg: MAx ,Min,Count ,Sum 
Statement If: SumIf ,CountIf ,If 
Functions Scalar: Etc Len ,Split ,Right
Date Functions
Math Functions 
Concatenating Data

Pivot Table
Multi Conditions
What If Analysis 
Goal Seek
Visualizing Data
Sorting And Filtering Graph and charts Forecast sheets Summarizing Data Get Data From Different Sources

What is that BI Tools and Power BI
Install Power BI
Understanding the ETL Process
Overview in powerbi desktop

Connecting to Different Sources
Data Preparation in Power Query Editor 
Cleaning
Understanding Row Operations
Filtering
Add Columns
Replacing Values
Conditional Columns

Relationships: One To One, Many To Many, One to Many
Dax Basics Operators
Using Calculated column and Creating Measures
If Statement

Creating Visualization With Power BI
Formatting Charts
Drill Down and filtering Graphs

Introduction to the role and working environments of the data analyst
Introduction to SQL Server Management Studio.

Basic Concepts and Datatypes
Select Statement
Filter Data with Where Statement and Logical Operators
Aggregate Functions: Count ,Sum ,Avg ,Max, Min
Organize Data With Order By, Group By, Top, Distinct etc
Grouping Sets / Rollup / Cube Built In Functions: Use Scalar Functions, Date Functions, Math Functions. Join Multiple tables from the database Use Sub- Queries and Common Table Expression. Union Multi Queries Together. Windows Functions By using Partition By. DML – Data Manipulation Language: Insert, Update, Delete.

Entity RelationShip Diagrams
Data Types
Keys ,Tables ,Schemas ,Database, Indexes ,Constraint

Stored Procedure
Functions
Views
Continue ,Break ,If ,While etc.

Introduction to Data Analysis with Python and Pandas
Installation Jupyter Notebook 
Introduction to Jupyter Notebook

Comments 
Types Data Basic
Operators
Variables 
Built -In Functions 
Methods 
Dictionaries , List 
Index Positions and Slicing

Working  with Series 
Introduction to Methods
Introduction to Attributes 
Arguments And Parameters 
Read Different Sources With Pandas 
Sort Values, Sort Index, Value counts
Unique and Nunique 
Apply Method

Working With DataFrame 
Methods and Attributes 
Select Columns from DataFrame 
Add Columns to DataFrame 
Find missing Values 
Astype method  Filter Data and Using conditions  Clean Data with drop duplicates method  Loc and Iloc  Query Method  Multi Indexing  Pivot and PivotTable  GroupBy And Agg Methods  Merge, Join and Concat  Working with Times and Dates  Visualization with pandas

Histograms 
Box and Violin Plots 
Regressions 
HeatMaps Correlation 
Bar Plot 
Distplot