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Introduction 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
Understanding the start pane
Connecting to data source
Data sources that can be connected
Various file formats
Bookmarks
Understanding the start pane
Connecting to data source
Data sources that can be connected
Various file formats
Bookmarks
TDS, TDE
Connecting to excel, Joins, Splitting data
Live and extract
Dimensions and measures
Clearing sorts and filters
Views: Standard fit width and height
Drilling down
Expanding the marks in pane
Swapping axis
Renaming sheets
Editing color pane
Adding highlighters
Understanding show me
Sorting and hierarchy
Data pane and analytics pane
Different view options at bottom of sheet
Hiding and unhiding fields
Creating folders to move dimensions and measures
Adding default colors and properties
Adding multiple data sources
Extracting workbook
Replacing data sources
Data cleansing
Database joins
Blending
Default charts
Highlighter for color and shape
Sorting from axis, color, category, manually and clearing sort
Creating groups from pane, manually, visually, parameters, and bins
Adding filter, show filter, wildcards, Top N parameters
Discrete and continuous dates
Types of filters: Applying to specific sheets, Editing page shelf
Hiding cards
Sets
Parameters
Tool tips
Cluster analysis
Formatting
Building dashboards
Hiding and unhiding sheets
Interface between sheets, dashboard and storyboard
Elements in dashboard
Formatting
Actions in dashboard
Device designer
Story points
Word cloud
Bump charts
Box and whisker
Funnel
Step and Line
Pareto
Waterfall
Donut
Lollipop
Pie
Heat map
Waffle
Show me charts
Basic syntax
Regular calc and table calc
Adding totals
Date calc
Logic calc
String calc
Number calc
LODs
Mapbox
WMS>Layers
Converting geo to non-geo
Chart default
Options for maps
Unrecognized locations
Groups
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. Industry: Human Resources
Predict the sales price for each house based on input features provided for the house.
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.
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.
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.
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).
Lenovo India’s BI Analytics & Visualization team created an interactive and flexible Tableau sales dashboard for departments to use it for ad-hoc analysis and reporting.
Tableau has made Lufthansa free from the bounds of IT department and made it independent in its functioning.
1st Jan 2023 Wednesday
10 AM (IST) 1hr-1:30hr / Per Session
4th and 5th Jan 2023 Sat-Sun8 AM (IST) 1hr-1:30hr / Per SessionOur Tableau training has precisely been developed to reach out to the demand of the learners by keeping in mind the industry standards.
This Tableau course will particularly be helpful for the career advancement of the following audience -
Graduates from the College.
Currently working employees looking to upskill themselves.
Candidates looking for a change in the IT Field.
As such, there are no specific prerequisites for Tableau institutes in Hyderabad. If you are familiar with programming and foundation skills with a sense of curiosity and willingness to learn you are all set for the Tableau training. .
Tableau Training Classes are conducted over the Weekdays and Weekends through classroom and online sessions. Please get in touch with the Digital Lync team to get the exact schedule and timings.
Our Tableau faculty has over 12 years of experience.
Tableau Course duration is 50 hours.
Weekday Tableau Training classes will be one hour long and Weekend classes will be three hours long.
Please find the detailed Tableau course curriculum in the Digital Lync Tableau training curriculum section.
Yes, we will assist our students with all the interview preparation techniques
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2nd Floor, Hitech City Rd, Above Domino's, opp. Cyber Towers, Jai hind Enclave, Hyderabad, Telangana, 063049 82304
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
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
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