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Data scientists use data to predict the future. Their work includes using machine learning or advanced statistics. The tech used by both of them is somewhat similar, data scientists might use some advanced tools. The choice is always to us what we want to do. If you want to solve complex problems then go for the data scientist role or if you like analyzing or creating reports then go for the data analyst position.
1. Tell us about yourself and your journey?
Data Analyst Duo is founded by two individuals Aditi & Kalpesh. We did our M.Sc in Statistics from the University of Mumbai. Almost a decade of being a statistics enthusiast, we encountered many friends and relatives from diverse educational backgrounds, including management, biotechnology, etc., who had difficulties understanding statistics.
We sensed a desire to fill this gap and assist them along with many others. Given the global reach, social media has to offer, we felt Instagram was the way to go. And so the tale of the data analyst duo started. Our Instagram account gives us a platform to share our love for teaching statistics with the rest of the world. We also help people transition their careers into data analytics.
2. How can one know that Data Science is their cup of tea?
Data Science is about using data to solve problems and create an impact. The impact could be in the form of business or personal growth, forecasting or predicting future outcomes, etc. Yes working with data brings lots of challenges and if one likes challenges, is interested in solving complex problems, loves working with numbers, and is interested in coding should go for it.
3. How to go about the projects in data science?
We always recommend everyone to start with the projects for which the solution is already available, you can find such projects all over the internet. One can use these projects to grow their knowledge. Once they have a good understanding of working on a project, they can start looking for problems where the solution is not easily available, for e.g. participating in hackathons or creating their own problem statement.
4. Can a non-tech person go into Data Science?
If one is interested and ready for the challenge then Yes, definitely. We have seen so many of our students who have successfully transitioned their careers towards tech. In fact, we both are from a non-tech background. You just need to learn some technical skills.
5. Elaborate more about this field and what is the future of Data scientists?
Data is very valuable. It contains immense information. It can even influence decision-making. A large amount of data is generated every minute. An increase in data would require more people to work.
6. What is the difference between data scientists and data analysts? Which field is suitable for whom?
Designations are for companies. The role changes from company to company. Some companies hire one person to do both jobs. Data analysts use data to explain the past. Their work revolves around exploring the historical data, creating dashboards, and generating insights.
8. What all courses and skills should one know to get into this field?
If one wants to start their career in data science, we would recommend them to focus on five important tools: Statistics, SQL, Excel, Python, and Data visualization tools like Power BI or Tableau. You have to convert the data into some actionable insights. Statistics helps us with understanding the data.
9. Does one get the pre-requisite knowledge and skills of this field in college?
These days colleges do provide courses on data science. But we don’t need to wait for college to teach us. There are tons of resources available for self-study. One can start anytime.
Ms. Aditi Gangar -
A M.Sc. in Statistics from the University of Mumbai and PGDBM-Marketing Management from NMIMS
Aditi has experience of more than two years of in market research working in diverse domains like FMCG, media, and automotive
She has experience in launching international brand track studies and media usage studies for the automotive sector
She takes great interest in analytics including multivariate & predictive analysis
LinkedIn
Mr. Kalpesh Ghadigaonkar-
A M.Sc. in Statistics from the University of Mumbai
Kalpesh has experience of around four years in data science
He has worked with a leading audience measurement firm in design, quality control, and analytics for digital and television platform
He has also worked in the product analytics team of a fintech organization
Kalpesh is the founder of One Stop Analytics which aims to provide its client with data-driven solutions to their problems.
LinkedIn
2. How can one know that Data Science is their cup of tea?
Data Science is about using data to solve problems and create an impact. The impact could be in the form of business or personal growth, forecasting or predicting future outcomes, etc. Yes working with data brings lots of challenges and if one likes challenges, is interested in solving complex problems, loves working with numbers, and is interested in coding should go for it.
3. How to go about the projects in data science?
We always recommend everyone to start with the projects for which the solution is already available, you can find such projects all over the internet. One can use these projects to grow their knowledge. Once they have a good understanding of working on a project, they can start looking for problems where the solution is not easily available, for e.g. participating in hackathons or creating their own problem statement.
4. Can a non-tech person go into Data Science?
If one is interested and ready for the challenge then Yes, definitely. We have seen so many of our students who have successfully transitioned their careers towards tech. In fact, we both are from a non-tech background. You just need to learn some technical skills.
5. Elaborate more about this field and what is the future of Data scientists?
Data is very valuable. It contains immense information. It can even influence decision-making. A large amount of data is generated every minute. An increase in data would require more people to work.
There are ample opportunities in every sector like banking, healthcare, sports, etc. Data Scientists get paid really well. It’s not just about being a data scientist, there are many job roles that the company offers like Data Analyst, Business analyst, Data Scientist, Data Engineer, and ML Engineer to name a few.
6. What is the difference between data scientists and data analysts? Which field is suitable for whom?
Designations are for companies. The role changes from company to company. Some companies hire one person to do both jobs. Data analysts use data to explain the past. Their work revolves around exploring the historical data, creating dashboards, and generating insights.
Data scientists use data to predict the future. Their work includes using machine learning or advanced statistics. The tech used by both of them is somewhat similar, data scientists might use some advanced tools. The choice is always to us what we want to do. If you want to solve complex problems then go for the data scientist role or if you like analyzing or creating reports then go for the data analyst position.
7. How does the job market look like in this field?
As I said, there will be a huge demand for data scientists. There are use cases in almost every sector. When companies realised the role of data in decision-making, they will set up a data science team to make most of their data.
7. How does the job market look like in this field?
As I said, there will be a huge demand for data scientists. There are use cases in almost every sector. When companies realised the role of data in decision-making, they will set up a data science team to make most of their data.
8. What all courses and skills should one know to get into this field?
If one wants to start their career in data science, we would recommend them to focus on five important tools: Statistics, SQL, Excel, Python, and Data visualization tools like Power BI or Tableau. You have to convert the data into some actionable insights. Statistics helps us with understanding the data.
You need to have knowledge of the tools to play with data, e.g., SQL for handling big datasets, data extraction, and manipulation, excel for data analysis, PowerBi/Tableau for data visualization, and python for machine learning. Along with the technical skills, one should also focus on soft skills like good communication because there is no use of the insights if you can’t convey them properly, business understanding, and problem-solving skills.
9. Does one get the pre-requisite knowledge and skills of this field in college?
These days colleges do provide courses on data science. But we don’t need to wait for college to teach us. There are tons of resources available for self-study. One can start anytime.
Ms. Aditi Gangar -
A M.Sc. in Statistics from the University of Mumbai and PGDBM-Marketing Management from NMIMS
Aditi has experience of more than two years of in market research working in diverse domains like FMCG, media, and automotive
She has experience in launching international brand track studies and media usage studies for the automotive sector
She takes great interest in analytics including multivariate & predictive analysis
Mr. Kalpesh Ghadigaonkar-
A M.Sc. in Statistics from the University of Mumbai
Kalpesh has experience of around four years in data science
He has worked with a leading audience measurement firm in design, quality control, and analytics for digital and television platform
He has also worked in the product analytics team of a fintech organization
Kalpesh is the founder of One Stop Analytics which aims to provide its client with data-driven solutions to their problems.
Interviewed By - Shashank Sehgal
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