Inovi Technologies is the best Data Scientist training institute in Noida. Information science
Training by Expert. Information science it is a product here dispersing and handling the
extensive arrangement of information into the group of PCs.
Training by Expert. Information science it is a product here dispersing and handling the
extensive arrangement of information into the group of PCs.
Course Content
Data Exploration
Introduction to data exploration, importing and exporting data to/from external sources,
what is data exploratory analysis, data importing, dataframes, working with dataframes,
accessing individual elements, vectors and factors, operators, in-built functions, conditional,
looping statements and user-defined functions, matrix, list and array. Hands-on Exercise –
Accessing individual elements of customer churn data, modifying and extracting the results
from the dataset using user-defined functions in R.
what is data exploratory analysis, data importing, dataframes, working with dataframes,
accessing individual elements, vectors and factors, operators, in-built functions, conditional,
looping statements and user-defined functions, matrix, list and array. Hands-on Exercise –
Accessing individual elements of customer churn data, modifying and extracting the results
from the dataset using user-defined functions in R.
Data Manipulation
Need for Data Manipulation, Introduction to dplyr package, Selecting one or more columns
with select() function, Filtering out records on the basis of a condition with filter() function,
Adding new columns with the mutate() function, Sampling & Counting with sample_n(),
sample_frac() & count() functions, Getting summarized results with the summarise() function,
Combining different functions with the pipe operator, Implementing sql like operations with
sqldf, Text Mining with StringR, wordcloud & StringR, Data Manipulation with data.table
package, Working with dates with the lubridate package.
Hands-on Exercise – Implementing dplyr to perform various operations for abstracting over
how data is manipulated and stored.
with select() function, Filtering out records on the basis of a condition with filter() function,
Adding new columns with the mutate() function, Sampling & Counting with sample_n(),
sample_frac() & count() functions, Getting summarized results with the summarise() function,
Combining different functions with the pipe operator, Implementing sql like operations with
sqldf, Text Mining with StringR, wordcloud & StringR, Data Manipulation with data.table
package, Working with dates with the lubridate package.
Hands-on Exercise – Implementing dplyr to perform various operations for abstracting over
how data is manipulated and stored.
Data Visualization
Introduction to representation, Different kinds of charts, Introduction to language structure of
illustrations and ggplot2 bundle, Understanding clear cut conveyance with geom_bar() work,
understanding numerical appropriation with geom_hist() work, building recurrence polygons with
geom_freqpoly(), making a diffuse plot with geom_pont() work, multivariate investigation with geom_
boxplot, univariate Analysis with Bar-plot, histogram and Density Plot, multivariate dispersion, Bar-plots
for absolute factors utilizing geom_bar(), including topics with the topic() layer, perception with plotly
bundle and ggvis bundle, geographic representation with ggmap(), building web applications with
shinyR, recurrence plots with geom_freqpoly(), multivariate circulation with dissipate plots and
smooth lines, nonstop versus unmitigated with box-plots, subgrouping the plots, working with
co-ordinates and subjects to make the diagrams more respectable, Intro to plotly and different plots,
representation with ggvis bundle, geographic perception with ggmap(), building web applications with
shinyR.
illustrations and ggplot2 bundle, Understanding clear cut conveyance with geom_bar() work,
understanding numerical appropriation with geom_hist() work, building recurrence polygons with
geom_freqpoly(), making a diffuse plot with geom_pont() work, multivariate investigation with geom_
boxplot, univariate Analysis with Bar-plot, histogram and Density Plot, multivariate dispersion, Bar-plots
for absolute factors utilizing geom_bar(), including topics with the topic() layer, perception with plotly
bundle and ggvis bundle, geographic representation with ggmap(), building web applications with
shinyR, recurrence plots with geom_freqpoly(), multivariate circulation with dissipate plots and
smooth lines, nonstop versus unmitigated with box-plots, subgrouping the plots, working with
co-ordinates and subjects to make the diagrams more respectable, Intro to plotly and different plots,
representation with ggvis bundle, geographic perception with ggmap(), building web applications with
shinyR.
Hands-on Exercise – Creating information perception to comprehend the client beat proportion
utilizing diagrams utilizing ggplot2, Plotly to import and investigating information into lattices.
You will picture residency, month to month charges, add up to charges and other individual sections
by utilizing the diffuse plot.
utilizing diagrams utilizing ggplot2, Plotly to import and investigating information into lattices.
You will picture residency, month to month charges, add up to charges and other individual sections
by utilizing the diffuse plot.
Our More Course Are:
3. Python
Mobile No. 8810643463, 9354482334
Phone No. 91-120-4213880
Email- info@inovitechnologies.com
Address. F7 Sector-3 Noida UP 201301 India.
