Phase 1: The Beginning
• Began my academic journey with a Bachelor of Science in Mathematics.
• Fascinated by the power of numbers and analytics, but seeking a practical application in the modern tech landscape.
BSc Economics to Data Scientist: Transform Your Career
BSc Economics
B
Data Scientist
D
Structured Career Program
A mentor-guided, comprehensive training path with hands-on projects and career support.
Self-Paced Courses
Flexible, self-managed study, ideal for personal schedule balancing.
Self-Study
Self-driven learning, utilizes diverse resources, for motivated individuals.
Technical Skills Required to Become a Data Scientist
Programming Languages
Python and R are fundamental languages for data science, offering vast libraries and community support. Key languages include:
- Python
- R
Machine Learning
Essential for building models that can predict trends and patterns from data. Important tools are:
- scikit-learn
- TensorFlow
Data Analysis and Data Wrangling
Mastering data manipulation and preparation is key for insightful analysis. Essential tools for these skills include:
- Pandas
- Tableau
- PowerBI
Data Visualization
Effective visualization communicates complex data insights clearly and effectively. Key tools include:
- Python: Matplotlib, Seaborn
- R: ggplot2
- Software Tool: Tableau
Statistical Analysis
The backbone of data science, enabling the understanding and interpretation of data. Tools include:
- Scikit-learn
- Numpy
Big Data Tools
Key for processing and analyzing large datasets efficiently. These tools are vital:
- Hadoop
- Spark
Database Management
Managing and querying databases effectively is crucial in handling data. Core tools consist of:
- Relational Databases: SQL
- NoSQL Databases: MongoDB, Cassandra
Data Science Libraries
Libraries that provide robust tools for data analysis and manipulation. Notable libraries include:
- Python: Pandas, NumPy, Scipy, matplotlib, Scikit-learn
- R: ggplot2, dplyr, tidy
Deep Learning
Advanced neural network techniques for complex pattern recognition and prediction. Key frameworks are:
- TensorFlow
- PyTorch
- theano
Version Control
Essential for tracking changes in code, collaboration, and maintaining project history. Important tools are:
- Github
- GitLab
- Mercurial
- AWS CodeCommit
- Apache Subversion
- Team Foundation Server
- Bitbucket
- Beanstalk
Non-Technical Skills Required to Become a Data Scientist
Communication
Vital for effectively conveying complex data insights and findings to both technical and non-technical audiences.
Problem-Solving
Key in developing innovative solutions to complex data-driven problems across various business contexts.
Critical Thinking
Essential for thoroughly analyzing data, questioning assumptions, and making informed, data-driven decisions.
Business Acumen
Important for understanding the business context, which enables translating data insights into actionable business strategies.
Data Storytelling
Crucial for presenting data findings in an engaging and understandable way, turning complex data into compelling stories.
Domain Knowledge
Beneficial for applying data science techniques effectively within a specific industry or sector.
Teamwork and Collaboration
Necessary for working effectively with cross-functional teams on data-driven projects and initiatives.
Adaptability
Essential in the fast-evolving field of data science, to embrace new methodologies, tools, and data sources.
Ethics and Privacy
Paramount for responsibly handling sensitive data and adhering to ethical standards in data analysis and usage.
Project Management
Useful for overseeing data projects, managing timelines, and resources, and ensuring project goals are met.
Data Scientist Salary: Your Earning Potential
Below is a comprehensive overview of the salary insights for Data Scientists.
Starting Salary
6-12 LPA
Experience-Based Growth
Up to 20-30% Increase Yearly
Industry Variance
Finance: Up to 18 LPA
E-commerce: Up to 16 LPA
Global Opportunities
U.S.: Avg $95,000 p.a.
Europe: Avg €70,000 p.a
Geographical Factors
Bangalore: Average 12 LPA
Chennai: Average 9 LPA
Certification Benefits
Avg 10-20% Salary Boost
Bonuses and Benefits
Avg Bonus: INR 1-3 LPA
Consulting and Freelance
INR 2000-5000/hr
Career Progression
Data Architect: Avg 22 LPA
Chief Data Officer: Avg 45 LPA
BSc Economics vs Data Science
Below is a table comparing BSc Economics and Data Science across various parameters.
BSc Economics
Data Science
Freshers Salary (INR)
3-5 LPA
6-12 LPA
Job Growth
Steady, high in finance sectors
Rapid growth with increasing demand
Freelance Opportunities
Moderate in analysis and consulting
More common, especially for specialized skills
Global Opportunities
Good in global finance and research institutions
High demand globally
Work Flexibility
Moderate, with some remote analysis work
Remote options available
Career Progression
Steady, with potential in economic research and policy
Varied, rapid advancement opportunities
Skill Diversification
Economic theory and analytical skills
Broad, applicable across industries
Learning Curve
Steady, with a focus on economic theories
Steep, with continuous learning required
Industry Impact
Significant in economic research and policy
Influences various emerging fields
Innovation Potential
Moderate, especially in economic strategies and models
High scope for groundbreaking ideas
A Real Journey From Science Graduate to Data Scientist
Phase 2: The Turning Point
• Realized the potential of my mathematical skills in the burgeoning field of data science.
• Decided to bridge the gap between theoretical mathematics and its application in data analytics and machine learning.
Phase 3: A New Direction
• Sought to find a program that would allow me to transition smoothly into data science.
• Chose a course that offered a blend of statistical theory, machine learning, and practical data analysis.
Phase 4: Learning to Code
• Embraced the challenge of learning programming languages and data science tools.
• Found that my background in mathematics provided a strong foundation for understanding complex algorithms and models.
Phase 5: Project Prowess
• Applied my knowledge in practical scenarios, working on data science projects.
• These projects helped me gain hands-on experience and a deeper understanding of how data science is applied in the real world.
Phase 6: Navigating the Job Market
• With a solid foundation in data science, began the search for opportunities to apply my skills.
• My unique blend of mathematical expertise and data science training opened doors in the tech industry.
Phase 7: Making an Impact
• Secured a position as a Data Scientist Trainee at Strydo Technologies.
• Excited to apply my skills in analyzing data and deriving insights, contributing to the company's data-driven projects and initiatives.
How it all happened,
Your Next Step: Start Your Data Science Journey
Complete the form to your right and take the first step towards a future in Data Science.
FAQsFAQs
Can a BSc Economics graduate become a data scientist?
Yes, a BSc Economics graduate can certainly become a data scientist. The transition involves acquiring new skills in data analysis, programming, and machine learning, and they can successfully make this career shift.
What makes data science a viable career option for BSc Economics graduates?
Data science offers diverse opportunities, higher earning potential, and a growing job market, making it an attractive career for engineers looking to leverage their analytical skills.
What are the first steps I should take to transition from BSc Economics to data science?
Start with foundational data science courses, get hands-on with projects, and network with industry professionals.
Is a degree in data science necessary for BSc Economics graduates to enter the field?
While beneficial, a degree isn't mandatory. Many professionals succeed through bootcamps, certifications, and self-study.
Which skills are crucial for a BSc Economics graduate to transition into data science?
Key skills include:
- Statistical Analysis
- Programming (Python/R)
- Machine Learning
- Data Visualization
What is the expected time frame for a BSc Economics graduate to become a data scientist?
Typically, it takes 6-12 months to transition, depending on the learning path and time commitment.
Is it possible to enter the data science field with a economics background but no coding experience?
Yes, there are many resources available to learn coding from scratch, tailored to BSc Economics graduates entering data science.
Are there any success stories of BSc Economics graduates who have successfully transitioned to data science?
Absolutely, there are numerous BSc Economics graduates who've made successful transitions, often highlighted in our testimonials.
What challenges might I face during the transition from BSc Economics to data science?
Challenges may include
- Learning new software
- Understanding data science jargon
- Developing statistical thinking
How personalized is the career transition guidance call offered by Placement Preparation?
The career transition guidance call offered by Placement Preparation is highly personalized, focusing on your background, goals, and the best strategies for your transition to data science.