Review Jurnal Internasional (1)
Jurnal 1
Data Science Techniques, Tools and Predictions
Mujthaba G.M , Abdalla Al Ameen,
Manjur Kolhar, Mohammed Rahmath
International
Journal of Recent Technology and Engineering
(IJRTE)
ISSN:
2277-3878, Volume-8 Issue-6, March 2020
1. Background (Problem)
Almighty
created human being with numerous wants and needs which makes them associated
with their own data, choices and preferences. To grow and develop any business
or organizations it is very obligatory to know their clients requests or customer needs based on their data.
The evolving role of data makes it very vital element in any organization and
carried with convinced operations.
Data is the
basic component in transformation of any individual, organizations and
businesses towards development in the future era. Technology plays an emerging
role in transmuting data into usefulness in all disciplines of the society .
The primary objective is to make the data usefulness by applying with
statistical and logical techniques. These techniques define the scope,
describe, process, modularize, exemplify and evaluate the data. Before learning
into the depth like tools, operations, process, methodologies, algorithms and techniques to operate
the data, it is very much required to do complete and through analysis of data. The types of data we have available with any individual or
organization like text, numerical, pictorial, images, audio, video and sensitize data. These data need to carry out with certain operations by
which it can be transformed to usefulness or profitable to the society. Before
operate on data be ensure that all these operations must not violate any
social, professional and ethical values of the society or any law.
2. Research Objectives
In this paper we are going to present a study of Data Science and its relevance with Artificial Intelligence, machine learning and deep learning. The incorporation of these intellectual sciences in data science is useful for perming numerous operations in our research we tried to demonstrate the data science operations like data cleaning, data processing, data modeling, data visualization and data presentations techniques. To grow any business it is mandatory to know their customer needs and satisfy their future expectations by smart decision makings. The intellectual algorithms or data operations in the data science make the data to be more effective in decision making and decision polices. We also focus on how data science incorporates mathematical & statistical methods, logical reasoning with applications of Artificial Intelligence techniques. We also focus on various data operations tools which exists in the market like python, SAS, R and many others. At last we focusses on how data science field going to meet the future expectations of many businesses. This research paper may become as successful reference for the people to carry out their research and meet the expectations of data science field with business growing decisions.
3. Methodology For Data Analysis
As discussed the data is
the primary artifact in any organization so it’s mandatory to look inside the
data like clear & precise definition of data, visibility of data scope,
arranging the data using proper
data structure, model
the data via tables, images,
pictorial representations, statistical tables and evaluation of data . Complete
and through analysis of data can be happened by appropriate selection of
analytical and statistical skills.
Proper prevention of errors and recovery
mechanism should be properly ensured.
Be ensuring about the
reliability and validity of data sources from where it is obtained
A.
Data
Analysis Methods
a.
Textual analysis which can also referred as data
mining it is to arrange the data
into large data sets using mining tools. The main aim of textual analysis is to
map the data into business data using business intelligence tools.
b.
Descriptive Analysis: It is to interpret, model and process the previous collected data which can be done in statistical
analysis.
c.
Inferential Analysis: In which we can investigate various inferences from the same data various samples.
d.
Diagnostic analysis: These methods are to investigate
the statistical analysis and find the cause for why it happens.
e.
Predictive analysis: In this analysis we try to predict
what can happen by using statistical data. For example
in day to day life how the
person does save on his predictable earning income.
f.
Prescriptive Analysis: This form analysis is used to
collaborate all the previous analysis reports to decide what decision could be
taken based on current situation.
g.
Factor Analysis: This analysis speaks about how the
variables form the relationships within the data set.
h.
Discriminant Analysis: This analysis is used to find the
relationships between different variable of different groups.
i.
Time Series Analysis: Measurement is done based on time
series for the variables of data sets.
4. Conclusion
Know a day’s data science becomes as a mandatory field which coordinates between multi disciplines like mathematics, statistical approaches, mathematical methods, logical reasoning, intelligence algorithms and machine learning practical’s. All these fields correlate to access the data from various business or organizations and make use of them in effective means. These effective use of data leads to perform proper decision making. to grow business further on the basis of customer chooses and satisfaction. Hence we can conclude that rise of data science field can demand more positions of data scientists to grow in each organization. At last we focus on how successful carriers can be built in the field of data science. The main beauty of this field it used to grow all businesses.
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