The document discusses data analytics and data science as career choices, highlighting reasons for pursuing these fields, such as high salary and problem-solving curiosity. It outlines roles, responsibilities, and technical skills required, including tools like Excel, Python, and Tableau, as well as concepts like machine learning and business intelligence. The document also touches on the hiring process, skillsets required, and approaches for engaging with companies in the industry.

1. Sri Ramakrishna College of Arts & Science,Coimbatore-641 006
Department of Computer Technology
DATAANALYTICS
Hannah Roseline R
ASSISTANT PROFESSOR
SRI RAMAKRISHNA COLLEGE OF ARTS & SCIENCE

2. AGENDA
• WHY AND WHO CHOOSES DATA
ANALYTICS OR DATA SCIENCE AS A
CARRIER
• ROLES AND RESPONSIBILITES
• TECHNICAL STACKS
• INTERVIEW VIEW APPROACH
• WHERE WE USE MACHINE LEARNING
• ROAD MAP
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3. WHY …..?
HIGH SALARY / REPEATED TALK OR ADVERTISED / SUGGESTED
SELECTION PROCESS
1. UG Mark above 75%
2. Aptitude rounds
3.Group Discussion
4.Interview
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4. Objective of a course
Data
Scientist - ?
Data
Analyst-?
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5. WHO CHOOSES DA/DS AS A
CARRIER
• Curious to solve a business problems
• Good story telling ability and insight gathering
• Good domain knowledge
• Tools and Programming knowledge – Excel,
Python, Tableau
• Statistical and Mathematical knowledge.
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6. WHAT....?
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7. EXAMPLE..
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8. WHAT... ROLES Data Analyst Data Engineers
Database
Administrator
Machine
Learning
Engineer
Data Scientist
Data Architect Statistician
Business
Analyst
Data and
Analytics
Manager
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9. TECHNICAL REQ....
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10. TECHNICAL STACKS AND USES..
• EXCEL - Excel is a powerful tool for data analysis
that enables users to manipulate, analyze, and
visualize large amounts of data quickly and easily.
• PYTHON OR R – vast community and packages,
Python is a general-purpose programming
language, while R is a statistical programming
language. This means that Python is more
versatile and can be used for a wider range of
tasks, such as web development, data
manipulation, and machine learning. R, on the
other hand, is primarily used for statistical
analysis and data visualization.
• Tableau / PowerBI – Visualization and business
Intelligence tools .
• Database – SQL & NOSQL 10

11. • Bigdata tools – Apache hadoop , cassandra, spark etc..
• Statistics - Statistics provides users with a means for collecting, reviewing, and analyzing data, as well
as a way to draw conclusions from this data and ultimately make better business decisions.
• ML - Algorithms with understandings
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12. What is ML and
Where do we
Apply ?
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13. Types of MNC or
Companies...
• Product based
• Service based
AFTER CLOUD TECHNOLOGY
1. Paas – Platform as a service
2. Saas – Software as a service
3. Iaas – infrastructure as a service
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14. HOW TO APPROACH A
COMPANY...
• Select a ROLE – Requirements
Eg : LinkedIn Descriptions for the
particular role
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15. Skillset
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16. Business Intelligence:
• Business intelligence is a broad term that encompasses data
mining, process analysis, performance benchmarking, and
descriptive analytics. BI parses all the data generated by a
business and presents easy-to-digest reports, performance
measures, and trends that inform management decisions.
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17. WHEN you start
to learn …...?
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18. ANY QUESTIONS ...?
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19. THANK YOU
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