(500) Ratings
27th Oct 2021 Wednesday
10 AM (IST) 1hr-1:30hr / Per Session
29th Oct 2021 Friday12 PM (IST) 1hr-1:30hr / Per Session
30th and 31st Oct 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
Start
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
Managing data and extracts
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
Sorting and Filtering
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 and parameters
Sets
Parameters
Tool tips
Cluster analysis
Formatting
Dashboards and storyboards
Building dashboards
Hiding and unhiding sheets
Interface between sheets, dashboard and storyboard
Elements in dashboard
Formatting
Actions in dashboard
Device designer
Story points
Charts
Word cloud
Bump charts
Box and whisker
Funnel
Step and Line
Pareto
Waterfall
Donut
Lollipop
Pie
Heat map
Waffle
Show me charts
Calculations
Basic syntax
Regular calc and table calc
Adding totals
Date calc
Logic calc
String calc
Number calc
LODs
Maps
Mapbox
WMS>Layers
Converting geo to non-geo
Chart default
Options for maps
Unrecognized locations
Groups
Basics
Selections
Importing data
Scripting basics
Configuring sheets
Creating and configuring sheets
List boxes
Input boxes
Pivots and Sets
Pivot tables
Set analysis
Lookup and apply map functions
Expressions
List boxes
Joins
Sliders
Resident load
Text objects
Statistics box
Creating PDFs
Scripting basics
Cyclic dimensions
Multiple topics
Table box
Multi box
Button objects
Combo charts
Expressions
Bar charts
Line charts
Pie charts
Scatter charts
Load inline
Concatenation
Straight table
Waterfall
Block charts
Trellis bar
Grid charts
Mekko
Funnel
Mini
Interchanging bar and Line
Radar
Step/line
Magic quadrant
Heat map
References in chart
Custom labels
Bookmarks
Before and after functions
Triggers
Pareto
Container objects
Search objects
Creating blank database
Creating table at backend table design view
Adding data
Creating select query
Queries
Query criteria
Action queries
Create queries
Delete query
Table query
Parameter queries
Relationships
Defining relationships between tables
One-One
One-many
Many-many
Wildcards
Calculated expressions
Indexing
Grouping
Summarizing data
Joins
Inner
Outer
Left
Right
Self
Query wizard
Forms
Multiple forms
Split forms
Modify forms
Themes
Navigation forms
Combo box
SQL view
Formatting
Controls and properties
Reports basics
Formatting reports
Functions
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
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 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
Visualizations
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
Charts
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
Excel data and collaboration
Upload an excel table
Excel data with BI content
Pinning excel tables
Analyze in excel
Workspaces
Power BI service
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
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
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.
Our Data Analysis training has precisely been developed to reach out to the demand of the learners by keeping in mind the industry standards.
This Data Analysis 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 Data Analysis 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 Data Analysis training.
Data Analysis 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 Data Analysis faculty has over 12 years of experience.
Data Analysis Course duration is 50 hours.
Weekday Data Analysis Training classes will be one hour long and Weekend classes will be three hours long.
Please find the detailed Data Analysis course curriculum in the Digital Lync Data Analysis training curriculum section.
Yes, we will assist our students with all the interview preparation techniques
Data Analysis training is excellent here. I specifically suggest for working professionals who are looking for advanced knowledge in Data Analysis course.
I have done my Data Analysis 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 Data Analysis course.
Here training seekers will be given the opportunity what exactly wanna be done.
Best Data Analysis Training in Hyderabad with hand on Labs and great exposure to all the other Technologies. The best platform to learn the Data Analysis course.
I took Data Analysis 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.
After Taking Data Analysis Training in Hyderabad, I got placed in as a Data Analysis Engineer. Digital Lync has helped me a lot to unleash my potential.
It has been a very good school of learning, the trainer explains every individual concept (Theory/Practical) in-depth when I joined for Data Analysis training at the Gachibowli branch Hyderabad. It has an extraordinary infrastructure with un-interrupted broadband. Your doubts can be clarified with live projects. Altogether it was an awesome experience learning in Digital Lync.
The best place to learn the Data Analysis course in Hyderabad, the trainer is well knowledged and trains in depth. Practice sessions helped me a lot and the infrastructure is superb compared to other places. I have cracked my certification exam in the Data Analysis training course with the help of the training here.
I have finished my Data Analysis 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.
2nd floor, Khajaguda Main Road next to Andhra Bank, Khajaguda - Nanakramguda Rd, near DPS, Gachibowli, Hyderabad, Telangana 500008
#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
