(500) Ratings
19th July 2021 Monday
8 AM (IST) 1hr-1:30hr / Per Session
21st July 2021 Wednesday10 AM (IST) 1hr-1:30hr / Per Session
23rd July 2021 Friday12 PM (IST) 1hr-1:30hr / Per Session
24th and 25th July 2021 Sat-Sun8 AM (IST) 1hr-1:30hr / Per SessionIntroduction 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...
Read More
Predict the sales price for each house based on input features provided for the house.
Industry: Real Estate
Tools: Linear Regression, Multi...
Customer analysis plays a crucial role in determining the profitability of Retail companies. Segmentation of the customers based on their...
Read More
This project deals with the predictions of stock market prices using history of Data. It also considers the physical factors...
Read More
This project analyses data using quantitative prediction of crimes in Boston and drawing visualizations of Trends in the data over the...
Read More
Market Basket Analysis is a technique which identifies the strength of association between pairs of products purchased together...
Read More
Adobe uses global cloud to create, deliver, and manage better digital experiences
GE Healthcare develops medical analytics solution in the cloud for better clinical outcomes
Our Power Bi training has precisely been developed to reach out to the demand of the learners by keeping in mind the industry standards.
This Power Bi 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 Power Bi 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 Power Bi training.
Power Bi 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 Power Bi faculty has over 12 years of experience.
Power Bi Course duration is 50 hours.
Weekday Power Bi Training classes will be one hour long and Weekend classes will be three hours long.
Please find the detailed Power Bi course curriculum in the Digital Lync Power Bi training curriculum section.
Yes, we will assist our students with all the interview preparation techniques
Power Bi training is excellent here. I specifically suggest for working professionals who are looking for advanced knowledge in Power Bi course.
I have done my Power Bi course here, the work environment is very good. Especially during training great tasks were taught. Everything explained cleared. Finally, I like to comment that "Digital Lync" is the right choice for those who wants to learn the Power Bi course.
Here training seekers will be given the opportunity what exactly wanna be done.
Best Power Bi Training in Hyderabad with hand on Labs and great exposure to all the other Technologies. The best platform to learn the Power Bi course.
I took Power Bi training in Hyderabad. Digital Lync is the best institute if you want to make yourself better. The best part is, the classes won't be like someone standing there and giving lectures, there is a lot of interaction between the trainers and the students.
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I have finished my Power Bi training in Hyderabad from Digital Lync. I got the job right after the training. I'm very happy. You should definitely check out Digital Lync to elevate your career prospects.
LIVE CHAT
2nd floor, Khajaguda Main Road next to Andhra Bank, Khajaguda - Nanakramguda Rd, near DPS, Gachibowli, Hyderabad, Telangana 500008
11, Pusat Dagang Seksyen 16 Seksyen 16, 46350 Petaling Jaya Selangor, Malaysia
#23664, Richland Grove Dr, Ashburn, VA 20148
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
