(500) RatingsIntroduction to Excel
Customizing tabs, options in excel
Name managers
Data validation: Options in data validation for list, whole numbers, dates
Using name manager for data validation
Sorting
Custom sorting
Subtotals
Sorting left to right
Advanced sorting with multiple options
Advanced filter options
Sorting and filtering with color options
Pivot tables
Short cuts to create pivot tables
Changing row and column labels
Custom format tables and default tables
Changing number formats
Value field settings and summarizing values by 11 different options
Value field settings and showing values as different options
Grouping of continuous fields
Pivot charts, compared regular charts
Slicers, Slicer settings, advanced slicer
Calculated fields in pivot tables
Vlookup and Hlookup
Syntax for Vlookup
What-if there are errors
Vlookup with data validation
Approximation for Vlookup
Using column function for dynamic column numbers
Using Choose function to select a table for Vlookup
Using Match function to identify column number
Locking cells for absolute and relative cells
Formulae
Auto sum functions
Logical functions
Text functions
Date and Time functions
Lookup and reference functions
Information functions
Charts
Column charts
Line charts
Pie charts, Pie in pie and Bar of pie, Donut
Stacker bar and clustered bar charts
Area
Scatter charts
Radar charts
Tree maps
Histogram
Waterfall
Funnel
Combo
Conditional formatting
Duplicate values
Alternate rows
Multiple criteria
Negative numbers
Gantt charts and Formula
Advanced topics
What-if analysis
Text to columns
Flash fill
Remove duplicates, Consolidate
Grouping, ungrouping and sub-totals
Freezing and unfreezing panes
Intro
Connecting to database, basic transformation
Managing queries, splitting columns
Modifying data types
Dates
Re-ordering columns, conditional columns
Connecting to folders
Merge queries
Query dependency
Transform data
Enter data
Query parameter
Data modeling
Data relationships
Calculated columns
Quick measures
Optimize models
Calculated measures
Hierarchies
Calculated tables
Time intelligence
Insert new data tables
Include/Exclude
Grouping and Binning
Create and format visualizations
Hierarchical axis and concatenating
Filters
Combo charts
Forecasting
Clustering data
Slicers
Data behind visualization
Date slicers
Maps, ESRI maps
Tables and matrixes
Table style
Scatter charts
Waterfall and funnel charts
Gauge, cards and KPIs
Coloring charts
Shapes, grid lines, page settings
Duplicates, categories with no data
Aligning, hierarchies
Combo charts
Custom visual store
Date slicer
Waterfall and funnel charts
Grouping and binning
Map visualizations
Report measures
Tables and matrices
Turn off visual headers
Persistent filters
Sync slicers
Buttons
Custom date tables
Default summarization
Measure filtering
Upload an excel table
Excel data with BI content
Pinning excel tables
Analyze in excel
Workspaces
Overview of dashboards
Publish from Power BI
Get insights
Pinning on dashboards, dashboard tiles, display options
Adding widgets
Navigating content
Filter the list in navigating pane
Dashboard settings
Sharing, print and export dashboards
Focus mode
Export to csv or excel or ppt
Notifications, alerts
Subscriptions
Publishing
Intro
Syntax
Select
Distinct
Where
And Or Not
Order By
Insert into
Null values
Update
Delete
Top
Min and Max
Count, Avg, sum
Like
Wildcards
In
Between
Alias
Joins
Inner
Left
Right
Full
Self
Union
Group By
Having
Exists
Create table
Drop table
Alter table
Not null
Unique
Primary key
Default
Views
Operators
Employee Attrition is an important subject to gauge the satisfaction of the employee in a company. HR departments take various measures to arrest employee attrition. In this project we will use Logistic regression to predict who is the potential employee who is in a verge of leaving the company.
Predict the sales price for each house based on input features provided for the house.
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HR Analytics for Attrition Prediction using Logistic Regression
Employee Attrition is an important subject to gauge the satisfaction of the employee in a company. HR departments take various measures to arrest employee attrition. In this project we will use Logistic regression to predict who is the potential employee who is in a verge of leaving the company.
Industry: Human Resources
Tools: Logistic Regression, Sklearn, Numpy, Pandas, Matplotlib
Churn Analysis in Telecommunication
Predicting Housing prices using Regression
Predict the sales price for each house based on input features provided for the house.
Industry: Real Estate
Tools: Linear Regression, Multi Linear Regression, Sklearn, Pandas, Numpy, Matplotlib
Retail Customer segmentation based on spending patterns
Customer analysis plays a crucial role in determining the profitability of Retail companies. Segmentation of the customers based on their purchase patterns helps Retail companies to cluster their user base and serve them effectively.
Industry: Retail
Tools: K-Means, Clustering, Sklearn, Pandas, Numpy, Seaborn
Stock market price prediction
This project deals with the predictions of stock market prices using history of Data. It also considers the physical factors vs. psychological, rational and irrational behavior etc. Machine learning techniques implemented in Python acts as game changer for the predictions. Algorithms including Linear regression, LSTM and ARIMA model are used for the same.
Industry: Finance
Tools: Tensorflow, Keras, LSTM, RNN
Exploratory Data analysis of Crime records in Boston
This project analyses data using quantitative prediction of crimes in Boston and drawing visualizations of Trends in the data over the years. Exploratory Data Analysis is carried on the crimes data using lots of techniques from Linear model to Stochastic gradient boosting.
Industry: Defense and Law Enforcement
Tools: Linear Regression, XGBoost, Numpy, Sklearn, Pandas, Matplotlib
Market Basket Analysis using Apriori Algorithm
Market Basket Analysis is a technique which identifies the strength of association between pairs of products purchased together and identify patterns of co-occurrence. A co-occurrence is when two or more things take place together.
The technique determines relationships of what products were purchased with which other product(s).
Industry: Retail
Tools: Apriori Algorithm, Numpy, Pandas
