As we are seeing many people pivot their career and step into the exciting world of data science, our Computational Biology Upskilling discussion equips you with the knowledge and strategies to make a seamless transition into this high-demand field. You will be able to ask your burning questions to professionals who have made the transition. Learn how to upskill and build a strong portfolio and network effectively.  

Why Data Science? 

“Data science skills can always serve you” 

• High Demand: Companies across all industries are seeking data-savvy professionals. 

• Lucrative Salaries: Data science roles offer competitive pay. 

• Endless Opportunities: Solve real-world problems using data-driven insights. 

Who Should Attend? 

This discussion is perfect for professionals looking to pivot their careers, students exploring data science, and anyone passionate about leveraging data to drive impact. 

What You’ll Gain: 

• A roadmap to becoming a data scientist 

• Access to resources and guidance for ongoing learning 

Objective: 

To provide participants with a structured roadmap to transition into a data science career, focusing on essential skills, tools, and real-world applications. 

Target Audience: 

• Professionals seeking a career pivot into data science 

• Individuals with backgrounds in adjacent fields like IT, engineering, or academia 

• Students or graduates looking to enter the field of data science 

Agenda: 

• Introduction to Data Science 

• Overview of the data science field 

• Career opportunities and roles (Data Scientist, Analyst, Engineer, etc.) 

• Prerequisites: Skills and mindset required 

• Building the Foundation  

• Core concepts: Statistics, mathematics, and problem-solving 

• Essential programming languages: Python and R 

• Tools of the trade: Jupyter Notebook, SQL, and cloud platforms 

• Specialization Area 

• AI and deep learning 

• Big data tools  

• Domain-specific applications  

• Creating a Career Transition Plan  

• Upskilling resources: Online courses, bootcamps, certifications 

• Building a portfolio: Kaggle competitions, GitHub projects 

• Networking and job hunting strategies 

• Panel Discussion/Q&A with Experts  

• Insights from professionals who transitioned into data science 

Outcomes: 

Participants will leave the discussion with: 

• A clear understanding of the data science landscape 

• Practical experience in data analysis and basic machine learning 

• Understanding how to create a personalized plan for retraining and career transition