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Research in Data Science

Data Scientist Aayushi Verma talks about Research in Data Science course, what is Research in Data Science and other details about a Career in Research in Data Science.

















Research in Data Science

Aayushi Verma | Data Scientist | EdGE Networks Pvt Ltd






What is Research in Data Science?


You may be curious about a Career in Research in Data Science. Internet is brimming with pages on How to get into Research in Data Science, while one should first understand What is a Career in Research in Data Science. While anyone can have an opinion on what Research in Data Science entails; only a real professional can really explain it.

Data Scientist Aayushi Verma has 5 years of professional experience in Research in Data Science. Data Scientist Aayushi Verma defines Research in Data Science as:

Research in Data Science, which is a profession, the goal of Data Science research is to build systems and algorithms to extract knowledge, find patterns, generate insights and predictions from diverse data for various applications and visualization.





How Data Scientist Aayushi Verma got into Research in Data Science?


After completing my M Tech in Data Analytics, I joined a start-up in Delhi as a Data Scientist. After that I have worked for few companies as a Data Scientist, in 2018, I joined EdGE Networks Pvt Ltd as Data Scientist. I have also published few Research Papers in Journals like IEEE & Springer.





Data Scientist Aayushi Verma's Talk on Research in Data Science


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The Path to a Data Science Research Career: Insights from Ayushi Verma


In today’s data-driven world, the field of data science has never been more pivotal. This dynamic sector is at the forefront of solving complex business problems through innovative research and analytics. Ayushi Verma, a seasoned data scientist and researcher, offers her insights into the nuances of this exciting career. Her journey—from studying computer science to carrying out impactful research—highlights the education, skills, and daily realities of life as a data science researcher.

What Is Research in Data Science


Understanding Research in Data Science

Research in data science is a systematic approach that encompasses numerous stages, including data extraction, preprocessing, feature engineering, exploration, insight extraction, and visualization of data. This structured methodology is essential for addressing specific business problems effectively. According to Ayushi, the process of drawing meaningful insights from complex data sets not only clarifies challenges but also provides the foundation for data-driven decisions. For instance, she emphasizes how innovative visualizations can greatly enhance the comprehensibility of analytics results.

Education


Mathematics: The Backbone of Data Science

A strong foundation in mathematics is crucial for anyone aspiring to excel in data science research. Mathematics enables researchers to understand and apply various algorithms and statistical methods essential for analyzing data effectively. Ayushi points out that a solid grasp of numbers helps in solving complex data problems, making it easier to extract meaningful insights. For instance, familiarity with statistical theories allows researchers to evaluate data accuracy and reliability.

Tools: Essential for Data Visualization

The right tools play a significant role in the success of data science research. Tools such as MS Excel and Tableau are vital for data representation and visualization, facilitating the communication of complex findings to diverse audiences. Ayushi emphasizes that mastering these tools not only enhances a researcher’s efficiency but also significantly improves the quality of insights derived from data. In her experience, visualizing data appropriately has often led to improved business decision-making processes.

Literature Survey: Grounding Your Research

Conducting a thorough literature survey is a foundational step in data science research. This process involves reviewing existing research to identify gaps, trends, and previous methodologies. Ayushi believes that an understanding of what others have accomplished in the field greatly informs one’s own research trajectory. By grounding her innovations in previously published work, she has been able to substantiate her findings and contribute novel insights to the discipline.

Skills


Focus: The Key to Success

In the realm of data science research, unwavering focus is indispensable. Ayushi highlights that deep concentration is necessary when dealing with complex data challenges, as distractions can lead to inaccurate results. Engaging intensely with the material not only enhances a researcher’s ability to derive insights but also fosters a more productive research environment. She encourages aspiring researchers to cultivate this skill early in their careers.

Innovation: Driving Research Forward

Innovation is at the heart of impactful data science research. Ayushi asserts that researchers must always seek new approaches to problems, ensuring that their work contributes uniquely to the field. Embracing a mindset of innovation allows researchers to differentiate their findings and offer fresh perspectives. For example, Ayushi has often had to reimagine existing algorithms to deliver enhanced solutions to data challenges.

Patience: A Necessary Virtue

Research is a meticulous process that requires considerable patience. Ayushi expresses that the journey often includes trial and error, requiring a steadfast dedication to navigating setbacks. This patience is critical as it enables researchers to learn from failures and continue refining their approach. Ultimately, patience contributes to the overall quality of research, as thorough investigation often leads to the most rewarding insights.

Never Give Up Attitude: Resilience in Research

Resilience is essential in navigating the challenges of data science research. Ayushi inspires aspiring researchers to adopt a 'never give up' attitude, even when experiments yield unexpected or negative results. This tenacity is vital for overcoming obstacles and achieving professional growth. Many successful breakthroughs in data science have stemmed from persistent experimentation and a commitment to continuous learning.

Positives


Help in Academic Growth: Pursuing a PhD

Engaging deeply in research can pave the way for advanced academic opportunities, such as pursuing a PhD. Ayushi points out that intensive research contributes significantly to one’s academic credentials, enhancing prospects for future roles in both academia and industry. This academic growth not only enriches personal knowledge but also establishes a credible professional profile.

Work Satisfaction: Fulfillment Through Contributions

The satisfaction derived from conducting meaningful research is profound. Ayushi notes that contributing to valuable projects leads to a sense of accomplishment and recognition within the community. This fulfillment stems from knowing that one’s work has the potential to impact society positively through innovative solutions.

Validation of Work: Recognizing Efforts

The opportunity to validate one’s research through publication and presentation serves as a significant motivator in data science. Ayushi shares that presenting her research papers at conferences not only brings recognition but also affirms the importance of her contributions. Validation becomes an essential part of a researcher’s journey, showcasing their efforts to the broader community.

Challenges


Lack of Relevant Data: A Common Hurdle

One of the most significant challenges faced in data science research is the lack of relevant data. As Ayushi explains, while data is abundant, finding datasets that specifically address research questions can be difficult. This scarcity can hinder research progress and necessitates creativity in data sourcing and problem formulation.

Unavailability of Related Papers: A Knowledge Gap

The absence of existing literature on particular research topics poses another challenge for researchers. Ayushi notes that when there are few or no related studies available, it becomes challenging to contextualize one’s work. However, this can also represent an opportunity for groundbreaking innovations, as researchers have the chance to operate in uncharted territory.

A Day Of


Daily Life as a Data Science Researcher

A typical day for Ayushi involves extensive literature reviews, where she distills knowledge from existing research to inspire her innovative projects. She dedicates time to read actively and think critically about how to approach data challenges uniquely. This iterative process of exploration, evaluation, and visualization forms the bedrock of her daily work, allowing her to forge ahead with her research goals. By remaining engaged with current studies and trends, she continues to refine her next research steps.

In conclusion, a career in data science research is marked by continuous learning and innovation. Through her journey, Ayushi Verma illustrates that a blend of solid education, essential skills, and the perseverance to navigate challenges leads to significant contributions in the field. The pursuit of data science not only offers professional fulfillment but also the opportunity to make a meaningful impact on society. Embracing this path can lead aspiring researchers to exciting and rewarding experiences across the globe.





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How to get into

Research in Data Science?



If you are want to get into Research in Data Science, start by investing in a Career Plan.

The 14 hour process, guided by a LifePage Career Advisor, will help you introspect and check whether your interest in Research in Data Science is merely an infatuation or is it truly something you wish to do for the rest of your life.

Next, your Career Advisor will help you document how you can get into Research in Data Science, what education and skills you need to succeed in Research in Data Science, and what positives and challenges you will face in Research in Data Science.

Finally, you will get a Career Plan stating which Courses, Certifications, Trainings and other Items you need to do in the next 7 years to become world’s best in Research in Data Science.





LifePage Career Plan

14 hour personalized guidance program















Your LifePage Career Advisor facilitates your guided introspection so that you systematically explore various Career options to arrive at a well thought out Career choice.

Next: your Advisor helps you figure out how you will get into your chosen Career and how will you develop the skills needed for success in your Chosen Career.

LifePage Plan will not stop at saying "to become an Architect study Architecture". It will guide you on which Certifications, Trainings and Other items you need to do along with your Architecture education to become the world's best Architect.











Links for this Talk




Data Scientist Aayushi Verma's LifePage:


Career Counselling 2.0
[LifePage]
https://www.lifepage.in/page/aayushiverma






LifePage Career Talk on Research in Data Science


Career Counselling 2.0
[Career]
https://www.lifepage.in/careers/research-in-data-science


Career Counselling 2.0
[Full Talk]
https://lifepage.app.link/20181130-0009


Career Counselling 2.0
[Trailer]
https://www.youtube.com/watch?v=s_-h3frvBC8


(Aayushi Verma, Data Scientist, Edge Networks, Research in Data Science, Natural Language Processing, NLP, Artificial Intelligence, Data Analytics, Big Data)







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