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Mahendiran Ravi
Software IT Training Institute in Chennai

Softlogic Academy is well aware of the fact that a lack of soft skills and aptitude skills are contaminating the IT career dream of youth. Thus free training on soft skills, interview skills, and aptitude skills are offered along with every corporate IT training course.

Achieve your goal with Apache Cassandra Training to get industry-required DBA Skills. SLA provides the best Apache Cassandra Course in Chennai. Build your skills with the MongoDB course from our experts for your career. Join SLA, the leading MongoDB Training Institute in Chennai Learn Master in DBA Skills through MySQL DBA Course. SLA is the best provider among MySQL Training Institutes in Chennai with placement support.


Essential skills to become a Data Scientist by 2025

Data Science is a recent but fast emerging field that involves the processing of big data by

professionals such as data scientists, data analysts, computer engineers, and statisticians.

It is all about extracting and analyzing data that are collected from various sources and

transforming them into useful insights to help organizations in smart decision-making.

The most important task in the data science process is to develop predictive models used

for analyzing big data.

There are various technologies involved in the data science process such as SQL, Python,

Hadoop, R, SAS, and Tableau. All these technologies are coming under multiple

categories like analysis, visualization, distributed architecture, and statistics. Companies

are employing innumerable certified professionals to handle data analytics to achieve the

business goals through many software applications. Following are the popular terms used

in the data science process.

Data Mining: It involves exploring and understanding new data that are collected from

various sources.

Artificial Intelligence: AI is used to create a machine that acts smart and behaves like a

human.

Machine Learning: ML is the concept and the subset of AI that intends to generate

algorithms by the understanding of historical data to improve the machine with

experience.

Deep Learning: It is the subset of ML that involves the data transformation through

multiple numbers of non-linear factors for calculating the output.

Challenges to practice data science

The adoption of data analytics comes with challenges such as dirty data, lack of data

science talent, company politics, lack of clear questions, data inaccessible, unused results,

explaining issues, privacy problems, lack of domain expertise, and unaffordable data

science team. The learning of data science process helps to overcome the following

challenges for the professionals.

Problem-identification

Accessing right data

Data cleansing

Data quality

Data quantity

Multiple data source

Data security

Result communication

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Mahendiran Ravi
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