Data Engineering Web Development- Connect Infosoft Technologies!
Connect
Infosoft Technologies, provides Data Engineering Services.
As a Data drive company, we mainly focus on Data Collection work and Analytics
work. Data
engineering is the aspect of Data science that deals with
practical applications of data
collection and analysis.
A data engineer transforms data into a useful format for analysis and other
uses. Below are some points “How Data Engineering works”:
Wrangling: Data
wrangling is core step in Data Science that requires data writing, running and
refining the programs to analyses. This can be done through software like Python, R, MATLAB or Perl.
EDA: Exploratory Data Analysis define
as the critical process of performing initial investigations on data so as to
discover patterns, to spot anomalies, to test hypothesis and to check
assumptions with the help of summary statistics and graphical representations.
Analytics
or Modeling: Data analytics focuses on processing and performing statistical
analysis on existing datasets. Analysts concentrate on creating methods to
capture, process, and organize data to uncover actionable insights for problems
and establishing the best way to present this data. It is based on producing
results that can lead to find actionable data and immediate improvements. All
the contending machine learning models are trained with the training data sets.
MOREOVER DATA ENGINEERING IS CLASSIFIED INTO FOUR TYPES.
THEY ARE AS FOLLOW:
Predictive Analytics: Predictive Analytics use
existing data, statistical algorithms and machine learning techniques to
analyze current and historical facts to make predictions about future or
otherwise unknown event.
Descriptive
analytics: Descriptive analytics does not make predictions or directly
inform decisions. It focuses on summarizing data in a meaningful and
descriptive way.
Diagnostic
Analytics: It is a form of advance analytics which examines data or content
to answer the question “Why did it happen?” and is characterized by techniques
such as drill- down, data discovery, data mining and correlations.
Prescriptive
Analytics: Prescriptive analytics is where artificial intelligence (AI) and
Big data come into play. Prescriptive analytics automatically synthesizes big
data, mathematical sciences, business rules and machine learning to make
predictions and then suggests decision options to take advantage of the
predictions.
R: R is
open-source programming languages with a large number of communities; it is mainly
used for statistical analysis and analytical work. R has number of tools to
communicate the results.
Data
analysis using R language: R is a powerful language used widely for data analysis and
statistical computing. It was developed in early 90s. Since then, endless
efforts have been made to improve R’s user interface.
Database: All modern
big data warehouse support SQL, Amazon Redshift, HP Vertica, Oracle, SQL,
Server and many others.
Excel: Data
Engineers uses excel tools to extract data, calculation to fit an equation to
that data, Conversion of data format and collection of data in sheet.
SQL: Nothing we
can do without SQL knowledge in data engineering since we have to construct
queries to extract data.
Want to learn what we can do for you?
Contact
us:
Email: info@connectinfosoft.com
Phone: (323)522-5635
Connect InfoSoft Technologies
Pvt.Ltd
Copyright @Connect
Infosoft Technologies Pvt.Ltd
============********================
Comments
Post a Comment