The Datapreneur Podcast

Important Announcement: Collaboration with Art of Working and Incluzon. (AOW: https://www.artofworking.co.in/) The people at ‘Art of Working' help support professionals in living upto their potential and bringing success to their career. 'Art of Working' coaches, trains and supports working people, in achieving better career growth, productivity and overall better work life. One of their mottos is AOW is the new WOW. (Incluzon: https://www.incluzon.com/) They aim to create a Truly Inclusive Workforce. Their platform is designed to democratize the entry-level workforce hiring process and mitigate bias by increasing the visibility of opportunities and talent and bringing the skill gap using an ethically constructed AI. Their goal is to create A platform where India's best talents meet Great Opportunities.

The Future of Data Science with Vijay Krishna Menon

A Career in Data Science is highly desirable by many in Singapore and other countries. Vijay is currently working as a Principal Data Scientist and is a data science unicorn with over 16 years of academic and industrial problem solving, research and teaching experience. He is trained in computer science and multidisciplinary sciences and has a great deal of aptitude for programming and an ineffable understanding of mathematics and statistics that enables him to visualise and envision knowledge generation from data to achieve valuable insights. Learn more about the responsibilities, work-life balance, innovations, and perks of pursuing a Career in Data Science in Singapore.

Creativity is an extremely important attribute for any value-adding Data Analyst. Being a Data Analyst is not just about reporting insights, it is more about using your creative side to explore data, communicating and learning from your team, and creating value for the business.  Matt’s Analytics career began in 2004, and over the years he’s learned and developed his Analytical and leadership skills at a handful of companies while making his way up the corporate ladder. He believes the world needs more analytical thinkers so He founded TMB Analytics with the intent to change the world through data, one Analyst at a time. Now, as a VP in the space, he mentors passionate Analytics professionals and creates courses and content that greatly benefits people new to the field.

Learn the art of Networking like a pro and maximizing the opportunities to boost your career with Matt Brattin. Networking and continued learning is extremely important to stay engaged in the ever-growing field of Data. Today, there are countless platforms that allow you to have meaningful connections with people. Listen now to explore some amazing networking platforms and the career advice from Matt.  Matt’s Analytics career began in 2004, and over the years he’s learned and developed his Analytical and leadership skills at a handful of companies while making his way up the corporate ladder. He believes the world needs more analytical thinkers so He founded TMB Analytics with the intent to change the world through data, one Analyst at a time. Now, as a VP in the space, he mentors passionate Analytics professionals and creates courses and content that greatly benefits people new to the field.

Exploring the use of Data in Biomedical Genomics with Dr. Sumaiya Sande. She pursued her Doctorate in Statistics from the National University of Singapore and also worked as a research scholar and graduate teaching assistant for numerous courses in the field of Data Science and Statistics. Currently, she is working as a Data Scientist and will be sharing her experience of working in different fields and the use of data in her roles on The Datapreneur Podcast today.

Machine learning or ML is the study of computer algorithms that automatically improves with time by using Data. Machine learning algorithms create models from sample data commonly known as training data in order to output predictions or sometimes make decisions without supervision or being explicitly programmed to do so. Dr. Ricardo Silva is a professor in the Department of Statistical Science and Adjunct Faculty of the Gatsby Computational Neuroscience Unit at University College London (UCL). Professor Ricardo got his PhD from the the Machine Learning Department at Carnegie Mellon University. He then spent two years at the Gatsby Computational Neuroscience Unit as a Senior Research Fellow, and one year as a postdoctoral researcher at the Statistical Laboratory in Cambridge. Learn about Machine Learning, Artificial Intelligence, and the many other popular terms in the field of Data Science.

Skilled Data Analysts are some of the most sought-after professionals in the world. With an ever-growing demand, the supply of people who can excel at this job is extremely limited. Therefore, Data Analysts earn high salaries and excellent perks. Further, common career paths for data analysts include moving into management positions. You can start out as a Data Analyst and then advance to senior-level analyst, analytics manager, director of analytics, or even chief data officer (CDO). Henry Sumner is a professional Data Scientist with almost a decade of industry experience. He completed his bachelors degree in computing for business which was focussed on combining the fields of statistics, computer science and problem solving. He has been working as a Pricing Analyst and now holds the position of a Senior Analyst. His interests include big data and its increasing prevalence in society. His final year dissertation was based on what retailers must do to survive the exponential growth of data and the pressures that this change will place upon businesses everywhere.

Deep learning is a field of machine learning, and neural networks make the foundation of deep learning algorithms. Deep learning is a powerful set of techniques for learning in neural networks which are a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data. Advitya has been named in the “25 under 25: Top Data Science Contributors and Thought-Leaders” list by 'The Data Standard' and is a Data Science graduate from UC San Diego. He is working at Microsoft as a Machine Learning Engineer and is a part of the prestigious Microsoft AI Development Acceleration Program (MAIDAP). He also pursued research in the area of Deep Learning Systems as an undergraduate student and has been the recipient of several academic and research awards, nominations, and publications.

Pursuing a Data Science major in an undergraduate degree is one of most popular and in-demand majors in today's workforce. Advitya Gemawat speaks about his experiences, learnings, internships and research opportunities at the University of California San Diego. A Data Science major consists of combination of Mathematics, Computer Science and Statistical courses along with numerous super interesting Data Science elective courses specialising in niche areas.  Advitya has been named in the “25 under 25: Top Data Science Contributors and Thought-Leaders” list by 'The Data Standard' and is a Data Science graduate from UC San Diego. He is working at Microsoft as a Machine Learning Engineer and is a part of the prestigious Microsoft AI Development Acceleration Program (MAIDAP). He also pursued research in the area of Deep Learning Systems as an undergraduate student and has been the recipient of several academic and research awards, nominations, and publications.

How to enter the vast field of Data? Where to begin? What to learn first? These questions are very commonly asked by every person trying to build a career in Data Science.  Everything is changing super fast due to digital transformation and Data insights are transforming businesses in unimaginable ways. But effective insights can only arise from correct and relevant Data. The most integral skill any Data Scientists can have is knowing how and what data to capture for accurate results.  Learn from Dr. Prashanth H Southekal, the 4 steps any person entering the field of Data Science should undertake. Prashanth is the founder and managing principle at DBP-Institute. He is also an Expert Council Member at the Forbes Technology Council, the author of 2 popular Data Science books, and an adjunct professor at IE Business School in Madrid, Spain.

Data Science is a field that is here to stay. As the amount of Data continues to grow, the need for people who understand and provide value from Data also grows. With that said, one needs to keep learning and building on their skill set to stay relevant in the industry. As a Data Scientist, you should be able to provide value and solve problems effectively. Learn from Dr. Prashanth how to keep learning and providing more value.  Dr. Prashanth is the founder and managing principle at DBP-Institute. He is also an Expert Council Member at the Forbes Technology Council, the author of 2 popular Data Science books, and an adjunct professor at IE Business School. His work has been published in popular journals such as MIT Sloan Management Review, CFO.University, INFORMS, and Forbes.  

Learn from Brent dykes and his experience of working in enterprise analytics for the past 16 years as an analyst, consultant, manager, and evangelist. Throughout the journey, he worked with cutting-edge analytics vendors (Omniture, Adobe, and Domo) and a broad base of Global 2000 companies, including many industry leaders such as Nike, Amazon, Microsoft, and Comcast.

Data-driven marketing is the approach of optimising brand communications based on customer information. With data-driven advertising, you can gain more insights into how buyers engage with your brand. You have the ability to create highly-targeted campaigns with personalized messaging to resonate with consumers. Learn from Brent dykes and his experience of working in enterprise analytics for the past 16 years as an analyst, consultant, manager, and evangelist. Throughout the journey, he worked with cutting-edge analytics vendors (Omniture, Adobe, and Domo) and a broad base of Global 2000 companies, including many industry leaders such as Nike, Amazon, Microsoft, and Comcast.

Being in the Data Space is particularly exciting, no matter which part you're working in - Business Intelligence or Data Management. Every part of a company - the marketing team, financial analysts, legal team and all other important people get value from Data. The beautiful part about Data Management, Meta Data and Reference data is that the SAME Data can be a part of the solution over different problems in an organization. Data as an asset gains value by the amount of times it is re-used! As a Data Scientist, don't just interact with other Data people. Go talk to business owners, figure out what problems need to be solved. Every organization has problems and Data Management is the foundation for approaching them efficiently.

Data Management is the foundation of digital transformation and the importance of master data, reference data and metadata is immense in this field. As a business executive, you need to understand the necessity for proper data management. Data management protects an organization and its employees from data losses, thefts, and breaches with authentication and encryption tools. As a Data Scientist, you can use a few techniques to tell Data Stories and convey the importance of good data management practices. Learn how to leverage the 3Vs of Data Storytelling – Vocabulary, Voice & Vision to tell your data story with Scott Taylor - The Data Whisperer.

Data Science is a vast field and working in a data environment requires communication between teams working on different areas. Mathematics is the language understood and spoken by data scientists for effective communication of ideas and information. Dive deep into the world of a Data Science and learn how real world problems are approached, analysed, and solved using mathematics and statistics. To understand the WHY, you would go to the statistical algorithms, to predict accurately, you would go to your Machine Learning models but to be able to use Data of high quality and produce relevant analysis, a good understanding of the area of probability, statistical analysis and methodology, inference, randomness is extremely important.

The universal definition of Data Science is very different from that of the fundamental and natural sciences. Data Science is really about developing mathematically motivated methodology to address specific problems in the real world. The process of understanding the context, asking questions, diving deep into the data, figuring out and understanding the patterns using statistical tools, and finally communicating relevant results is what a Data Scientist does at the core. Learn about the technical background and some important skills required to excel in your career as a Data Scientist.  

To be successful in any field, you need to know how to provide value! Every organization has problems that need to be solved. You need to be able to solve those problems with whatever tools you find interesting. With Design Thinking you can speak the language of the customer, with Data Science you can speak the language of Artificial Intelligence and Machine Learning, and with Economics you can speak the language of Value Creation for a business. Mastering these fields will prepare you for about anything, no matter what is thrown at you! In Design thinking, you need to be creative and enjoy trying and failing again and again. Great Data Scientists aren't afraid to fail and infact if you aren't failing enough, you should be scared. You have to be willing to experiment and doing it rapidly while letting your learnings guide you along the way. Additionally, truly understanding the needs of an organization or your customer is Step 1 to create value. 

What is the definition of Value creation for a business? Data in itself is a liability, and Data Scientists can’t inherently provide value without understanding the problem. Communication between the business owners, stakeholders and data scientists is extremely important to engineer value and come up with effective solutions. Trying to solve a data problem in isolation of the business will never be valuable. Data is HARD! The field of Data Management requires significant improvement in the quality of Data used and spend less time fine tuning the algorithms. Improving Data and making more it more transparent will result in greater efficiency of AI/ML models. Data Scientists need to approach the problem with a value creation and realisation perspective rather than looking at it from a Data perspective to find the out what Data is truly Gold.

Have you ever wondered what happens to the huge amounts of your private Data collected by social media websites and smart technology in your houses? Data is not only used for personalised advertisements but is also sold to other companies. Although, there could be some benefits of companies collecting data, the disadvantages and security issues significantly outweigh them. The amount Data produced and collected in the past 2 years was greater than the vast majority of all the data existing before it. With the increasing amounts of Data, just the knowledge workers and Data professionals wont be enough to deal with it. Citizens should be given the ability to work and perform basic actions on Data.  Today, Data Professionals and Scientists are not trained to produce results efficiently and timely for the analysis to even be useful for the company. Learn about why we need to engage citizen data scientists and how Data Literacy can be useful for academics and professional workers as well with Peter Aiken, co-author of the book 'Data Literacy'.

Your household technology collects more data about you than you can imagine, and you have no idea what happens with that data. Most of the time, you unknowingly grant access to your private data. Data literacy is essential to recognize who has access to your personal information and what they can do with it. Legislation of Data can become a reality through awareness and societal pressure. Data Literacy solves the privacy issues of the general people, significantly increases the efficiency of knowledge workers, and allows data professionals to make advancements in multiple fields. Tune in to learn about the consequences of a Data Illiterate society and the steps you should be taking to protect your data with Peter Aikens, co-author of the book 'Data Literacy.'

Data Analytics in the past has not been able as successful as it can and it is not due to the lack of Data Science expertise. Due to the latency between re-calculations with human data scientists is extremely high. Consequently, the analysis takes an unnecessary amount of time rendering the results useless for the business to use. Mark, the CEO and Chairman of Proof Analytics, discovered the fundamental problem of latency about re-calculations involving a classic automation play. This episode discussed the need for automation in data analytics and the exciting, more efficient, applications of it. 

Kishen is pursuing his PhD in Economics at the University of Cambridge and his research is mainly based on Empirical Macroeconomics. He is involved in numerous innovative projects which require working with a lot of Data. In this episode we discuss the the exciting and unique research going on at University of Cambridge, the role data plays in economics research, and the quality of Data required to produce high level research outputs. Analysis of Data for rigorous results often comes with numerous problems that require clever solutions. 

The field of Economics has evolved from purely theoretical studies since the time of Adam Smith due to increased importance of mathematical theory and data thanks to economists like Paul Samuelson. In this episode we discuss the journey of modern economics through the contribution of influential economists and the use of mathematical models in Econometrics.  Kishen is pursuing his PhD in Economics at the University of Cambridge and his research is mainly based on Empirical Macroeconomics. He is involved in numerous innovative projects which require working with a lot of Data. The quality of research depends on Data used and the standards for good research are extremely high due to the strong foundation in Mathematics and Statistics in Econometrics.  Join us as we learn about the exciting research going on at University of Cambridge!

The job of a Data Scientist requires more than just running models on data sets and analysing it. There are many more factors that come into play when you are solving a problem using data such as understanding the business and stakeholder requirements, credibility of data, and recursively adapting the models. Aakarsh Mohan, Data Scientist at Better.com, talks about the technical and soft skills required to excel as a data scientist, approaching and understanding business problems, tackling issues of working with data and much more!

A highly effective Data Scientist has numerous qualities that help them stand out. Data is a tool used to solve problems. To be a good Data Scientist, you require a quantitative background; but to be a Great Data Scientist, you need a lot more! Learn from Tom's experiences and industry knowldge of over 30 years.Getting a feel for the data and truly understanding the business problem is just as important as building complex models and algorithms. Additionally, presenting the findings from Data in an understandable and relevant matter is essential for productivity.

Being a Founder comes with many responsibilities. You need to continue innovating and adapting to the ever-changing needs of the customer. Building relationships, networking, and hiring the right talent for your Startup can be integral to its Success. To be an entrepreneur, you need to be shameless and learn as much as possible from others. This episode lets you be a part of the journey and learn from a successful founder’s experience growing and managing a Startup.

The recent rise of access to Data has changed the business approach in numerous industries. Google is one of the biggest examples of companies that are built on Data. This episode covers the countless possibilities of Data Science in Entrepreneurship. Making the most out of the Data collected is an integral aspect for increasing efficiency and reducing storage costs. Fields such as Machine Learning and Artificial Intelligence are transforming our use of data. These tools help businesses make accurate predictions and better decisions, contributing majorly to their success.

Entrepreneurship is risky, gruelling, and requires a lot of hard work but the rewards are definitely worth the journey. Do you know what it takes to succeed in a generation where technology and user experience dominate the market?Answering all your questions ranging from "Who is an Entrepreneur?" and "What qualities set successful Entrepreneurs apart?" to covering relevant topics like - Attracting Top Talent to your business, the Startup Culture, and Dealing with Failure.

Today we discuss the emerging fields of Big Data, Data Optimisation and Artificial Intelligence with examples of industry-leaders like Amazon, Netflix and Google. With easy access to Data, AI's ability to work with Data analytics is the primary reason for the emergence of the seemingly inseparable Artificial Intelligence and Big Data systems. AI machine learning and deep learning are pulling from every data input and using those inputs to make predictions. Learn what the future beholds for us with Professor Shalabh, head of Data Science and Statistics at the Indian Institute of Technology Kanpur (IIT K).

How did Data Science develop as a subject? What is the historical importance of working with Data? Professor Shalabh from the Indian Institute of Technology answers these questions intuitive analogies and examples of Tech-Giants like Amazon and Google.Statistics, and the use of statistical models, are deeply rooted within the field of Data Science. Data Science started with statistics, and has evolved to include concepts/practices such as Artificial Intelligence, Machine Learning, and the Internet of Things, to name a few.

Data Science and start-ups: The 2 fields that are revolutionising the 21st century.The need for Data Scientists today is sky-rocketing due to the relevance of Data in transforming businesses. But what is Data? Who is a Data Scientist? If you have ever asked yourself these questions, then you are about to begin an exciting journey!Welcome to The Datapreneur Podcast - the most exciting and informational platform to provide you with knowledge about the countless possibilities of Data Science in Entrepreneurship.