The document discusses developing soft and technical skills for pharmacists in data science and analytics in pharma-IT. It covers essential soft skills like communication and problem solving. Technical skills discussed include data collection, analysis, machine learning, and programming. The document also provides recommendations for building skills through online courses, projects, and networking.

1. Bridging the Gap: Developing Soft and
Technical Skills for Pharmacists in Data Science
and Analytics in Pharma-IT
Ravi Kumar Koduru | Tech Analyst
7th Oct
2023

2. Agenda
Part 1
• Introduction to Pharma-IT
• Essential Soft Skills
Part 2
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• Technical Skills for Success
• Building Your Career Path

3. Pharma-IT: Bridging Pharmaceuticals and
Information Technology
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Pharma-IT, short for Pharmaceutical Information
Technology, represents the intersection of
pharmaceutical science and advanced technology
solutions. It has emerged as a critical component
in the pharmaceutical industry, transforming the
way companies operate and innovate.
Market Size: The global Pharma-IT market has witnessed
remarkable growth, with a market size exceeding $1482
billion in recent years (2022).
Year-Over-Year Growth: The industry has been
experiencing a YY% year-over-year growth rate, primarily
driven by the increasing adoption of technology
solutions.
Investment Trends: Investment in Pharma-IT
infrastructure and solutions has surged, with
pharmaceutical companies allocating substantial budgets
to enhance their IT capabilities.
Key Statistics and Growth Trends
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4. Data Science in Pharma-IT: The Foundation of
Informed Decisions
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Data science serves as the foundation of
Pharma-IT by enabling pharmaceutical
companies to harness the power of data for
informed decision-making. It encompasses:
Pharma-IT incorporates advanced analytics and machine
learning to revolutionize pharmaceutical operations:
• Predictive Analytics
• Machine Learning
• Enhancing Patient Outcomes
Advanced Analytics and Machine Learning:
Transforming Pharma Operations
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• Data Collection
• Data Analysis
• Interpretation
• Impact

5. Data Science in Pharma-IT: The Foundation of
Informed Decisions
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Personalized Medicine: Tailoring Treatments
for Individual Patients
Pharma-IT's use of data science and analytics extends
to ensuring regulatory compliance:
• Data Integrity
• Pharmacovigilance
• Streamlined Documentation
Regulatory Compliance: Ensuring Integrity in
the Pharmaceutical Sector
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Pharma-IT is at the forefront of personalized medicine,
transforming the pharmaceutical landscape:
• Patient Data Analysis
• Precision Medicine
• Advancements in Therapy

6. Soft Skills for Success
Soft Skills
• Effective Communication
Verbal and written skills
Active listening
• Emotional Intelligence
Self-awareness
Empathy
• Problem Solving and Critical Thinking
• Teamwork and Collaboration
• Adaptability and Resilience
Communication Skills
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• Importance of clear communication
• Developing effective communication
• Body language and non-verbal cues

7. Technical Skills
Overview
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• Data Collection and Management
• Data Analysis and Visualization
• Machine Learning and Artificial
Intelligence
• Programming (e.g., Python, R)
• Pharmaceutical Domain
Knowledge
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8. Data Science Tools
and Technologies
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• Introduction to popular tools
(e.g., R Studio, Python libraries)
• Data visualization tools (e.g.,
Tableau)
• Data management tools (e.g.,
SQL, Hadoop)
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9. Building
Technical Skills
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• Online courses and certifications
• Workshops and seminars
• Hands-on projects
• Networking and mentorship
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10. Ravi Kumar Koduru 10
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11. Ravi Kumar Koduru 11
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• The floor is open for questions and discussions

12. References:
Pharmaceutical market
worldwide revenue 2001-
2022 | Statista
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13. Additional Resources
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Recommended Books:
Coursera (www.coursera.org)
Offers a wide range of data science and analytics
courses from top universities and institutions.
EdX (www.edx.org)
Provides courses and certifications in data science,
machine learning, and AI from universities and
organizations worldwide.
Great Learning (www.greatlearning.in)
Offers comprehensive data science and analytics
programs, including live sessions and industry projects.
LinkedIn Learning (www.linkedin.com/learning)
Features a vast library of courses on data science,
analytics, and related skills, often led by industry
experts.
Online Learning Platforms:
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"Python for Data Analysis" by Wes McKinney
"Data Science for Business" by Foster Provost and Tom
Fawcett
"The Art of Data Science" by Roger D. Peng and
Elizabeth Matsui
"Machine Learning Yearning" by Andrew Ng
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and
Aaron Courville

14. Websites and
Blogs:
Towards Data Science (towardsdatascience.com)
A Medium publication featuring articles, tutorials, and insights on data science and machine learning.
Kaggle (www.kaggle.com)
A community-driven platform for data science competitions, datasets, and learning resources.
DataCamp (www.datacamp.com)
Provides interactive courses on data science, analytics, and programming in R and Python.
YouTube (www.youtube.com)
Channels like "Data School," "StatQuest with Josh Starmer," and "sentdex" offer video tutorials on data science topics.
GitHub (www.github.com)
Explore open-source data science projects, code repositories, and collaborate with the data science community.
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15. Ravi Kumar Koduru 15
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E: rkkoduru4@gmail.com | LinkedIn: @RK Koduru

16. THANK YOU!
Ravi Kumar Koduru | Tech Analyst
7th Oct
2023