Data Science

Aayushi Verma | Data Scientist | EdGE Networks Pvt Ltd

What is Data Science?

A Career in Data Science is very interesting. Understanding Why one wants to choose a Career in Data Science is phenomenally more important than figuring out How to get into Data Science. Just like you would normally not trust a non Doctor with names of medicines, you should also not trust opinions about Data Science from non professionals.

With 5 years of professional experience, Data Scientist Aayushi Verma understands Data Science. Data Scientist Aayushi Verma outlines Data Science as:

Data science is a multidisciplinary blend of data inference, algorithmm development, and technology in order to solve analytically complex problems. At the core is data. Troves of raw information keep streaming in and stored in enterprise data warehouses as there is much to learn by mining it.

How Data Scientist Aayushi Verma got into 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 Data Science

The Journey of Data Science What Is Data Science Data Science Data Science is an interdisciplinary field that combines statistical analysis, computer science, and domain expertise to extract meaningful insights from structured and unstructured data. It involves collecting, cleaning, analyzing, and interpreting large datasets to support decision-making, predict outcomes, and solve complex problems across various industries. Data Science plays a pivotal role in today’s data-driven world by enabling organizations to make informed decisions, optimize processes, and gain a competitive edge. For professionals in this field, it opens doors to innovative problem-solving and continuous learning as technology evolves. One major benefit is the ability to uncover hidden patterns and trends that drive business growth. Another is the opportunity to automate repetitive tasks, freeing up time for more strategic work. Additionally, data science empowers professionals to create predictive models that anticipate future outcomes, helping organizations proactively address challenges and seize new opportunities. A data scientist working for an e-commerce company might analyze customer purchase histories, website interactions, and demographic information to improve product recommendations. By building and refining machine learning models, the data scientist can predict which products a customer is likely to buy next, personalizing the shopping experience. This not only increases customer satisfaction but also boosts sales and customer retention for the company. The data scientist collaborates with marketing and product teams to interpret the results, ensuring that insights are actionable and aligned with business goals. Through this process, data science transforms raw data into valuable strategies that directly impact the company’s success. Education Mathematics Mathematics is the systematic study of numbers, quantities, shapes, and patterns, using logical reasoning and abstract thinking. It forms the backbone of scientific inquiry and technological advancement. In Data Science, mathematics provides the essential framework for analyzing data, building models, and making informed decisions based on quantitative evidence. A strong grasp of mathematics is indispensable for anyone pursuing a career in Data Science. Foundational concepts such as statistics, linear algebra, calculus, and probability underpin nearly every aspect of data analysis and machine learning. Mastery of these topics enables professionals to understand how algorithms work, interpret results accurately, and troubleshoot complex problems. Over time, this mathematical fluency allows data scientists to innovate, adapt to new technologies, and communicate insights effectively to stakeholders. As the field evolves, those with a deep mathematical background are better equipped to contribute to cutting-edge research, develop robust models, and maintain a competitive edge in the job market. Consider a data scientist working for an e-commerce company aiming to improve product recommendations for customers. Each day, they analyze vast datasets containing user behavior, purchase history, and product attributes. By applying mathematical concepts such as matrix factorization from linear algebra and probability theory, the data scientist can build collaborative filtering models that predict which products a customer is likely to purchase next. Their understanding of statistics helps them evaluate model performance, ensuring recommendations are both accurate and relevant. This practical application of mathematics not only enhances the user experience but also drives business growth by increasing sales and customer satisfaction. Programming Languages Programming languages are formal systems of communication used to instruct computers to perform specific tasks. They provide a structured way to write algorithms, process data, and automate operations. In data science, programming languages are essential tools, enabling professionals to manipulate data, build models, and derive insights from complex datasets. Understanding programming languages is crucial for anyone pursuing a career in data science because they serve as the primary means of interacting with data and computational resources. Mastery of languages such as Python or R allows professionals to efficiently clean, analyze, and visualize data, as well as implement machine learning algorithms. This foundational knowledge not only enhances problem-solving capabilities but also fosters adaptability in a rapidly evolving field. Over the long term, proficiency in programming languages opens doors to advanced roles, supports collaboration with software engineers, and ensures that data scientists can leverage the latest tools and libraries to stay competitive and innovative in their careers. Consider a data scientist working for an e-commerce company who is tasked with predicting customer churn. Drawing on their understanding of programming languages, they write Python scripts to extract and preprocess customer data from various sources. They then use libraries such as pandas for data manipulation and scikit-learn to build and evaluate predictive models. Throughout this process, their programming skills enable them to automate repetitive tasks, experiment with different algorithms, and visualize results for stakeholders. This practical application of programming languages not only streamlines their workflow but also ensures that the insights they generate are accurate, reproducible, and actionable for business decision-making. Domain Knowledge Domain knowledge refers to a deep understanding of the specific industry, field, or subject area in which data science techniques are applied. It encompasses the terminology, processes, business logic, and unique challenges of that domain. In data science, domain knowledge bridges the gap between technical expertise and meaningful, actionable insights. Understanding domain knowledge is essential for data scientists because it enables them to interpret data within the correct context, ask relevant questions, and design solutions that address real business problems. Without this foundational knowledge, even the most advanced algorithms may produce results that are technically correct but practically irrelevant. Mastery of domain knowledge allows professionals to communicate effectively with stakeholders, prioritize projects that deliver tangible value, and avoid common pitfalls such as misinterpreting data or overlooking critical variables. Over the long term, data scientists with strong domain expertise are more likely to become trusted advisors, lead impactful projects, and advance into strategic roles within their organizations. Consider a data scientist working in the healthcare sector who is tasked with predicting patient readmission rates. Their theoretical understanding of domain knowledge enables them to recognize the significance of clinical variables such as diagnosis codes, treatment protocols, and patient demographics. By collaborating with medical professionals, they ensure that the features selected for their predictive models are medically relevant and that the results align with clinical realities. This approach not only improves the accuracy of their models but also ensures that the insights generated are actionable and ethically sound, ultimately leading to better patient outcomes and more efficient hospital operations. Skills Reading Reading is the process of interpreting and understanding written or printed material, encompassing everything from technical documentation to research papers and code comments. In data science, reading enables professionals to absorb complex information, stay updated with evolving methodologies, and accurately interpret data, making it a foundational skill in their toolkit. Strong reading skills directly enhance a data scientist’s ability to comprehend intricate datasets, grasp new algorithms, and implement best practices from the latest research. Employers and clients highly value this skill because it ensures that professionals can quickly adapt to new tools, understand project requirements, and communicate findings effectively. Cultivating reading proficiency involves regularly engaging with diverse sources such as academic journals, technical blogs, and open-source code repositories. Over time, consistent practice not only sharpens comprehension but also builds the confidence to tackle unfamiliar problems, ensuring that a data scientist remains agile and informed in a rapidly changing field. Imagine a data scientist tasked with optimizing a machine learning model that is underperforming. To address the issue, they delve into recent research papers and documentation for advanced techniques. Through careful reading, they discover a novel algorithm that addresses the exact challenge they face. By thoroughly understanding the methodology and implementation details, the data scientist successfully adapts the new approach to their project. This not only resolves the performance bottleneck but also demonstrates their ability to leverage cutting-edge knowledge, ultimately delivering superior results for their organization and showcasing the indispensable value of strong reading skills in data science. Analytical & Logical Thinking Analytical & Logical Thinking is the ability to objectively assess information, break down complex problems, identify patterns, and draw reasoned conclusions using structured reasoning. In Data Science, this skill enables professionals to interpret data accurately, design robust models, and make informed decisions, forming the backbone of effective problem-solving and innovation. Analytical & Logical Thinking directly enhances a data scientist’s ability to dissect intricate datasets, identify meaningful trends, and construct reliable predictive models. Employers and clients highly value this skill because it ensures that insights and recommendations are grounded in sound reasoning rather than assumptions. It leads to more accurate forecasts, efficient troubleshooting, and innovative solutions to business challenges. Cultivating this skill involves consistent practice in critical thinking, engaging with complex datasets, and seeking feedback on one’s reasoning processes. Over time, exposure to diverse data problems and collaboration with peers further sharpens analytical capabilities, making professionals more adept at navigating ambiguity and delivering actionable results. Imagine a data scientist tasked with identifying the root cause of a sudden drop in user engagement for a popular mobile app. By applying analytical and logical thinking, the professional systematically examines user behavior data, filters out irrelevant variables, and tests various hypotheses. Through careful analysis, they discover that a recent app update inadvertently introduced a bug affecting notifications. By logically connecting the timeline of the update with the engagement decline and validating the findings with supporting data, the data scientist not only pinpoints the issue but also recommends a targeted fix. This approach not only resolves the immediate problem but also demonstrates the value of structured reasoning in delivering impactful business solutions. Data Exploration Data Exploration is the systematic process of analyzing and visualizing raw data to uncover patterns, spot anomalies, test hypotheses, and check assumptions before formal modeling. It enables data scientists to understand the structure, quality, and nuances of data, forming the foundation for effective analysis and informed decision-making in any project. Mastering data exploration significantly enhances a data scientist’s ability to deliver accurate, actionable insights. By thoroughly examining data, professionals can identify inconsistencies, missing values, or outliers that might otherwise skew results. Employers and clients highly value this skill because it reduces costly errors and ensures that subsequent analyses are built on reliable foundations. Cultivating data exploration expertise involves consistent practice with diverse datasets, learning to use visualization tools, and developing a critical mindset for questioning data integrity. Over time, experience sharpens the ability to quickly spot issues and opportunities within data, making the professional indispensable in both collaborative and independent analytical environments. Imagine a data scientist tasked with predicting customer churn for a telecommunications company. During data exploration, they notice that several key variables contain unexpected gaps and that certain customer segments are underrepresented. By visualizing distributions and relationships, the data scientist uncovers a pattern indicating that missing values correlate with a recent system migration. Recognizing this, they collaborate with IT to retrieve lost records and adjust the analysis to account for the anomaly. This proactive exploration not only prevents misleading conclusions but also leads to a more accurate predictive model, ultimately helping the company retain valuable customers and improve its service offerings. Positives High Paying Profession A "High Paying Profession" refers to a career path that consistently offers above-average financial compensation, reflecting the specialized skills, expertise, and value professionals bring to their field. In data science, this means practitioners are rewarded not only for their technical abilities but also for their critical role in driving business decisions and innovation. The high earning potential in data science is a significant motivator for many professionals, as it directly translates to financial security and personal growth. This positive aspect fosters a sense of accomplishment and recognition, encouraging individuals to continually develop their skills and stay updated with the latest advancements. The promise of lucrative compensation also attracts top talent, which elevates the overall standard of the profession. On a daily basis, data scientists experience the tangible benefits of their expertise through competitive salaries, bonuses, and opportunities for advancement, all of which contribute to higher job satisfaction and a strong sense of purpose in their work. Imagine a data scientist working at a leading tech company who is entrusted with developing a predictive analytics model that significantly improves customer retention. As a result of their impactful contribution, they receive a substantial salary increase and a performance bonus. This financial recognition not only validates their hard work but also enables them to invest in further education, support their family, and enjoy a higher quality of life. The experience of being well-compensated for their expertise reinforces their passion for the field and motivates them to continue making meaningful contributions, illustrating how a high paying profession can be both fulfilling and transformative. Huge Demand Huge demand refers to the consistently high and growing need for skilled data science professionals across diverse industries. This demand is driven by the exponential increase in data generation and the critical role data-driven insights play in decision-making. It ensures abundant opportunities, job security, and career advancement for those in the field. The immense demand for data science professionals translates into a dynamic and rewarding career landscape. With organizations across sectors seeking to harness the power of data, data scientists enjoy a wealth of job opportunities and the flexibility to choose roles that align with their interests. This demand often leads to competitive salaries, robust benefits, and the chance to work on innovative projects. Professionals in this field experience a sense of value and relevance, knowing their expertise is essential to organizational success. The constant influx of new challenges and projects fosters continuous learning and professional growth, making each day engaging and meaningful. Consider a data scientist working at a healthcare technology company. Due to the huge demand for data-driven solutions, this professional is regularly approached by different departments seeking insights to improve patient care, optimize operations, and develop new products. The data scientist collaborates with cross-functional teams, applying advanced analytics to solve real-world problems and witnessing the tangible impact of their work. The constant stream of requests and projects not only provides job security but also offers a sense of accomplishment and purpose. This environment, shaped by high demand, allows the data scientist to grow their skills, take on leadership roles, and make a meaningful difference in both their organization and the broader community. Positive Social Impact Positive Social Impact refers to the meaningful and beneficial changes that improve individuals’ lives, communities, or society as a whole, resulting from purposeful actions or innovations. In data science, this means leveraging data-driven insights to address societal challenges, promote equity, and drive decisions that foster well-being and progress for all. The pursuit of positive social impact is a powerful motivator for data science professionals. Knowing that their work can help solve real-world problems—such as improving healthcare outcomes, advancing education, or supporting environmental sustainability—adds a profound sense of purpose to their daily tasks. This sense of contribution not only enhances job satisfaction but also fuels personal and professional growth, as individuals are constantly challenged to innovate and think critically about the broader implications of their work. Experiencing the tangible results of their analyses, data scientists often feel a deep connection to the communities they serve, reinforcing their commitment to ethical practices and continuous learning. Imagine a data scientist working for a non-profit organization dedicated to reducing childhood malnutrition. By analyzing vast datasets on nutrition, socioeconomic factors, and regional health trends, they develop predictive models that identify at-risk populations and optimize resource allocation. As a result, intervention programs become more targeted and effective, leading to measurable improvements in children’s health and well-being. Witnessing these positive changes firsthand, the data scientist experiences a profound sense of fulfillment, knowing their expertise directly contributes to saving lives and building healthier communities. This daily impact transforms routine data analysis into a deeply rewarding and meaningful career journey. Interesting Job An "Interesting Job" is one that consistently engages curiosity, challenges intellect, and offers opportunities to solve diverse, meaningful problems. In data science, this means working with complex datasets, uncovering hidden patterns, and developing innovative solutions. This dynamic environment keeps professionals motivated, fosters continuous learning, and makes daily work genuinely exciting. The inherently interesting nature of data science is a powerful driver of job satisfaction and professional growth. When work is intellectually stimulating and varied, it prevents monotony and burnout, encouraging individuals to stay passionate about their careers. Data scientists often tackle new challenges, explore cutting-edge technologies, and collaborate with experts from different fields. This constant evolution not only sharpens technical skills but also nurtures creativity and adaptability. As a result, professionals feel a sense of accomplishment and pride in their contributions, knowing their work has tangible impact. The engaging nature of the job also attracts top talent, fostering a vibrant community where knowledge sharing and innovation thrive. Imagine a data scientist working at a healthcare technology company. Each day brings a fresh challenge, such as analyzing patient data to predict disease outbreaks or developing algorithms to personalize treatment plans. The professional collaborates with doctors, engineers, and business leaders, learning from their expertise while sharing insights from data analysis. This collaborative, problem-solving environment keeps the workday stimulating and meaningful. The data scientist sees firsthand how their models improve patient outcomes and streamline hospital operations, providing a deep sense of fulfillment. The ever-changing nature of the projects ensures that boredom is rare, and the professional is continually inspired to learn, grow, and make a difference through their work. Challenges Lack of High Quality Data Lack of high quality data in Data Science refers to the frequent situation where available datasets are incomplete, inconsistent, noisy, or biased, making them unreliable for analysis and modeling. This challenge is demanding because it undermines the accuracy, validity, and generalizability of data-driven insights, complicating the entire analytical process. The absence of high quality data can significantly hinder a data scientist’s daily workflow, leading to unreliable models, misleading conclusions, and wasted resources. Professionals must often spend considerable time cleaning, validating, and augmenting data before meaningful analysis can even begin. This challenge requires resilience, as setbacks and repeated iterations are common. Successful data scientists address these constraints by developing robust data preprocessing pipelines, leveraging domain expertise to identify and correct anomalies, and employing advanced techniques such as data augmentation or synthetic data generation. They also collaborate closely with stakeholders to improve data collection processes, ensuring future datasets are more reliable. By embracing these strategies, professionals turn a major obstacle into an opportunity for innovation and continuous improvement. Consider a data scientist working in healthcare analytics who is tasked with predicting patient readmission rates. The available hospital records are riddled with missing values, inconsistent formats, and outdated entries. Rather than abandoning the project, the professional systematically cleans the data, consults with medical staff to understand critical variables, and uses imputation techniques to handle missing information. To further enhance the dataset, they supplement it with publicly available health statistics and generate synthetic patient profiles to balance the data. Through perseverance and creative problem-solving, the data scientist builds a predictive model that, while not perfect, provides actionable insights for hospital administrators. This approach demonstrates how determination and technical skill can overcome the challenge of lacking high quality data. High Expectations High Expectations in Data Science refer to the persistent pressure on professionals to deliver rapid, transformative insights and solutions that drive business value, often with limited data, unclear objectives, or evolving technologies. This demand is intensified by the perception that data science can solve complex problems effortlessly and consistently produce groundbreaking results. The weight of high expectations can significantly influence a data scientist’s daily routine, leading to stress, burnout, and a sense of inadequacy when results fall short of ambitious goals. This environment requires resilience, as professionals must balance technical rigor with the need to communicate realistic timelines and outcomes. Successful data scientists manage this challenge by setting clear boundaries, educating stakeholders about the iterative nature of data science, and focusing on incremental progress rather than immediate perfection. They cultivate open communication, regularly update teams on project status, and celebrate small wins, which helps recalibrate expectations and fosters a more sustainable, collaborative work culture. Consider a data scientist at a retail company tasked with building a predictive model to optimize inventory. Leadership expects immediate, flawless forecasts that will drastically reduce costs. Instead of overpromising, the data scientist explains the complexities involved, such as data quality issues and the need for iterative model refinement. By sharing regular progress updates and demonstrating early, modest improvements, the professional manages leadership’s expectations and builds trust. Over time, the model’s accuracy improves, and the company sees tangible benefits. This approach not only delivers value but also establishes a realistic understanding of what data science can achieve, ensuring long-term support and appreciation for the data science team’s efforts. Interpretability Issues Interpretability Issues in Data Science refer to the difficulty in understanding, explaining, or justifying how complex models, especially those using machine learning or deep learning, arrive at their predictions or decisions. This challenge is demanding because stakeholders require transparency, trust, and actionable insights, which opaque models often fail to provide. Interpretability Issues can significantly complicate a data scientist’s daily responsibilities. When models are difficult to explain, it becomes challenging to gain stakeholder trust, meet regulatory requirements, or debug unexpected outcomes. This lack of clarity can slow down project adoption and hinder collaboration with non-technical teams. Professionals must demonstrate resilience by continuously learning about new interpretability tools and frameworks, communicating technical concepts in accessible language, and advocating for model transparency even when it requires additional effort. Successful data scientists often balance the trade-off between model complexity and interpretability, opting for simpler models when possible or supplementing complex models with interpretability techniques. By fostering open communication and prioritizing transparency, they ensure that their work remains valuable and actionable. Consider a data scientist working in healthcare, tasked with developing a predictive model for patient readmission risk. The most accurate model available is a deep neural network, but its decision-making process is opaque. Recognizing the importance of interpretability for clinicians and regulatory compliance, the data scientist employs techniques such as SHAP values to highlight which features most influence predictions. They also create visualizations and summary reports that translate complex outputs into clear, actionable insights for medical staff. By proactively addressing interpretability, the data scientist not only builds trust with stakeholders but also ensures the model’s adoption and ongoing use in critical healthcare decisions, demonstrating the practical value of transparent data science. A Day Of Data Science The hum of a computer and the soft glow of multiple monitors set the stage for a day that is anything but routine. In the world of data science, each morning brings a fresh puzzle to solve, a new dataset to unravel, and the promise of insights that could shift the direction of an entire business. The pace is brisk, driven by curiosity and the relentless pursuit of clarity amid chaos. Whether wrangling raw data, building predictive models, or collaborating with colleagues across departments, a data scientist’s day is a blend of deep focus, creative problem-solving, and constant learning, all unfolding in an environment where innovation is the norm and adaptability is key. As the day begins, a data scientist typically settles in with a hot cup of coffee and a quick scan of overnight emails and system alerts. The first order of business is to review the results of any automated jobs or model training processes that ran overnight, checking for anomalies or errors that need immediate attention. This is also the time to revisit the day’s priorities, update task lists, and skim through recent developments in the field, ensuring that the mind is primed and ready for the analytical challenges ahead. By mid-morning, the real analytical work kicks into high gear. This is when a data scientist immerses themselves in the heart of their projects, whether that means cleaning and transforming raw data, exploring patterns through visualization, or developing and fine-tuning machine learning models. The atmosphere is one of intense concentration, punctuated by the occasional ping of a colleague seeking input or a quick huddle to troubleshoot a stubborn data issue. This is the most intellectually demanding part of the day, where creative thinking and technical expertise converge to turn raw information into actionable insights. The afternoon often brings a shift from solitary analysis to collaborative execution. Data scientists regroup with team members and stakeholders in meetings to discuss findings, align on project goals, and strategize next steps. These sessions might involve presenting preliminary results, gathering feedback, or brainstorming solutions to complex business problems. Afterward, the focus turns to implementing solutions, whether deploying models into production environments, refining dashboards for end-users, or documenting methodologies for future reference. The energy is dynamic, fueled by the collective drive to translate data-driven discoveries into real-world impact. As the day winds down, attention turns to tying up loose ends and preparing for tomorrow’s challenges. This is the time for documenting code, summarizing key findings, and updating project management tools to reflect progress. A data scientist might also carve out a few moments for professional development, reading up on the latest research or experimenting with new techniques. With a final review of the day’s accomplishments and a clear plan for the next steps, the workday concludes, leaving a sense of satisfaction in having pushed the boundaries of what data can reveal.

Install the LifePage App to:

(for Free) Watch Data Scientist Aayushi Verma’s full Data Science Career Talk

₹ Do a Self Assessment on Data Science to calculate your Dream Index, which is defined as:

According to Aayushi Verma your chances of success in Data Science is __%

Access your personalized Dream Index Report which will have all your Dream Indices sorted in descending order.

How to get into

Data Science?

If you are want to get into 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 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 Data Science, what education and skills you need to succeed in Data Science, and what positives and challenges you will face 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 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:

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

LifePage Career Talk on Data Science

[Career]
https://www.lifepage.in/careers/data-science-1

[Full Talk]
https://lifepage.app.link/20181130-0008

[Trailer]
https://www.youtube.com/watch?v=rdNZoyFLgI4

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

Similar Talks

Decision Management

Kapil Rawat
Business Analyst | G E Corp, Bangalore

[ 3 years & 2 months Experience ]

Use of computers allows organizations to collect enormous amount of data. Decision Management is tasked with analyzing this data to help take smarter business decisions. These decisions can comprise of anything and everything, like which advertisement to show to a particular user on Facebook or which credit card defaulters to call and when.

"After M A in Economics from JNU, I started my career as a Business Analyst with G E Corp in Bangalore. In my almost two years there I was responsible for making data based collections strategies for two large US retailers with several million defaulting buyers."

Actuarial Profession

Romik Rai
Risk Officer | Ageas SA/NV

[ 9 years Experience ]

An Actuary is a business professional who deals with the measurement and management of risk and uncertainty. These risks can affect both sides of the balance sheet, and require asset management, liability management, and valuation skills. Actuaries use mathematics, statistics, and financial theory to study uncertain future events, especially those of concern to insurance and pension programs.

"After completing my Masters in Actuarial Studies from University of New South Wales, Australia, I started working as a consultant with Watson Wyatt Worldwide. I have worked with over 20 financial institutions across Europe and Asia. Currently, I am the Founder and CEO of Greenassets.in which I started in 2015 in India. Greenassets.in is a startup where we promote investments which have a positive environmental and social impact."

Data Analysis

Sunaina Kuhn
Senior Associate | Evaldesign

[ 3 years & 4 months Experience ]

Data analysis, also known as analysis of data or data analytics, is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.

"After completing my Schooling from Woodstock, I did BA in Political Science from Purdue University. I taught Business English for a year in Budapest. After that I joined Teach for India. I also worked as a Research Associate in IIM, Ahmedabad. I have been working as a Senior Associate at Evaldesign since 2015."

Planning & Statistics

Dr Manoj Pant
Chief Coordination Officer | Department Of Planning

[ 25 years Experience ]

Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data. Statistics in indispensable into planning in the modern age which is termed as the age of planning. Almost all over the world the govt. are re-storing to planning for economic development.

"I completed my MSC in Statistics from Lucknow University. I did my PG Diploma in Computer Application. I joined Department of Planning as Chief Coordination Officer."

Data Science

Rajan Gupta
Analytics Consultant | Various Assignments

[ 7 years & 4 months Experience ]

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.

"I am a Research & Data Analytics professional, learning new ways to deal with various business and research problems through technological and analytical tools. With the help of past experiences and academic exposure, following are the highlights of current capabilities. I have a substantial experience of working on research & analytics projects in the area of Information Systems, E-Governance and Public Schemes Assessment. I have working knowledge of application of various data science concepts through statistical techniques, operation research & optimization, data mining and machine learning across multiple industries (healthcare, retail, education, etc.) and domains. I have hands-on experience of using various analytical & development tools like SPSS, R, Tableau/MS Power BI and MATLAB and exposure of training and teaching in specialized areas of IT & Analytics. I am UGC NET-JRF Qualified and also possess management consulting certification from Government of India. I am one of the few Analytics professional in India to have CAP (INFORMS) and GStat (ASA) certification & accreditation, respectively and also serving as the Brand Ambassador of CAP in Asia Region for INFORMS. I am Efficient in Report preparation and Article writing. I can prepare Research proposals, Research Frameworks, Data Source identification, Data Collection Framework and Data Preparations. I have experience of handling teams up to 50 people for project execution."

Risk Analytics

Malika Davar
Senior Consultant | BFSI Analytics

[ 5 years & 10 months Experience ]

Risk analytics (or risk analysis) is the study of the underlying uncertainty of a given course of action. It often works in tandem with forecasting professionals to minimize future negative unforseen effects.

"I pursued a Bachelors degree in Economics from St Stephen's College and a Masters degree in Economics from Delhi School of Economics. Soon after, I joined BFSI Risk Analytics where I have been working for more than 4 years. Currently, I am working there as a Senior Consultant."

Data Analytics

Pratap Shankar Gharge
Executive President & CIO | Bajaj Electricals

[ 34 years & 2 months Experience ]

Data analytics or analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making

"After completing my graduation from Shivaji University, Diploma in Computer Management from Mumbai University and an MBA (Operations Research) from IGNOU, I started my career with Simplex Textile Mills as a Programmer (1984-85) and post that I was associated with Bajaj Electricals for 33 Years and retired as an Executive President & Chief Information Officer."

Data Sciences

Dr T P Singh
Professor | UPES

[ 22 years & 4 months Experience ]

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.

"After completing my education, I started my career with TCS as a DBA. After that, I joined Sharda University as an Associate Professor and worked there for almost 17 years. I am a Professor at UPES since 2017."

Data Science & Statistics

Raman Nautiyal
Scientist E | Indian Council of Forestry Research & Education (ICFRE)

[ 20 years & 10 months Experience ]

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining. Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.

"I did Bachelors in Statistics, Maths & Economics from DAV College, Dehradun. I then did Masters in Statistics from the same college. I am pursuing my Ph D in Statistics from Kumaon University. I am a Scientist E at Indian Council of Forestry Research & Education (ICFRE)."

Economics & Finance Consulting

Manmeet
Consultant | Planning Commission of India

[ 7 years Experience ]

Economic consulting is the practice of providing organizations in the public and private sector with information to improve their performance and policies, primarily through the use of applied economics, mathematical economics, economic impact analysis, and forecasting across a broad spectrum of issues.

"After graduating in commerce, I went on to do masters in Economics. I also attended winter school in Delhi School of Economics. I have done a course in management from London School of Economics. I did my internship at Planning Commission of India. I have worked at RMS as Business Risk Analyst and as Risk Management Consultant at Inductis. I am Strategy & Operations Head at Nuts for Cuts."

Research in Data Science

Aayushi Verma
Data Scientist | EdGE Networks Pvt Ltd

[ 5 years Experience ]

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.

"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."

Risk Analytics

Shobit Kumar Singhal
Assistant Manager | Barclays

[ 2 years Experience ]

Risk analysis is the process of identifying and analyzing potential issues that could negatively impact key business initiatives or critical projects in order to help organizations avoid or mitigate those risks.

"After completing my education, I joined Barclays as senior analyst and I am working as an Assistant Manager at Barclays."