Article | June 9, 2021
In recent years, we have seen more industries adopt data analytics as they realize how important it is. Even the hotel industry is not left behind in this.
This is because the hospitality industry is data-rich. And the key to maintaining a competitive advantage has come down to ‘how hotels manage and analyze this data’.
With the changes taking place in the hospitality industry, data analysis can help you gain meaningful insights that can redefine the way hotels conduct business.
Article | December 10, 2020
Saurav Singla is a Senior Data Scientist, a Machine Learning Expert, an Author, a Technical Writer, a Data Science Course Creator and Instructor, a Mentor, a Speaker.
While Media 7 has followed Saurav Singla’s story closely, this chat with Saurav was about analytics, his journey as a data scientist, and what he brings to the table with his 15 years of extensive statistical modeling, machine learning, natural language processing, deep learning, and data analytics across Consumer Durable, Retail, Finance, Energy, Human Resource and Healthcare sectors. He has grown multiple businesses in the past and is still a researcher at heart.
In the past, Analytics and Predictive Modeling is predominant in few industries but in current times becoming an eminent part of emerging fields such as health, human resource management, pharma, IoT, and other smart solutions as well.
Saurav had worked in data science since 2003. Over the years, he realized that all the people they had hired — whether they are from business or engineering backgrounds — needed extensive training to be able to perform analytics on real-world business datasets.
He got an opportunity to move to Australia in the year 2003. He joined a retail company Harvey Norman in Australia, working out of their Melbourne office for four years.
After moving back to India, in 2008, he joined one of the verticals of Siemens — one of the few companies in India then using analytics services in-house for eight years.
He is a very passionate believer that the use of data and analytics will dramatically change not only corporations but also our societies. Building and expanding the application of analytics for supply chain, logistics, sales, marketing, finance at Siemens was a very fulfilling and enjoyable experience for him.
Siemens was a tremendously rewarding and enjoyable experience for him. He grew the team from zero to fifteen while he was the data scientist leader. He believes those eight years taught him how to think big, scale organizations using data science.
He has demonstrated success in developing and seamlessly executing plans in complex organizational structures. He has also been recognized for maximizing performance by implementing appropriate project management tools through analysis of details to ensure quality control and understanding of emerging technology.
In the year 2016, he started getting a serious inner push to start thinking about joining a consulting and shifted to a company based out in Delhi NCR.
During his ten-month path with them, he improved the way clients and businesses implement and exploit machine learning in their consumer commitments. As part of that vision, he developed class-defining applications that eliminate tension technologies, processes, and humans. Another main aspect of his plan was to ensure that it was affected in very fast agile cycles. Towards that he was actively innovating on operating and engagement models.
In the year 2017, he moved to London and joined a digital technology company, and assisted in building artificial intelligence and machine learning products for their clients. He aimed to solve problems and transform the costs using technology and machine learning. He was associated with them for 2 years.
At the beginning of the year 2018, he joined Mindrops. He developed advanced machine learning technologies and processes to solve client problems. Mentored the Data Science function and guide them in the development of the solution. He built robust clients Data Science capabilities which can be scalable across multiple business use cases.
Outside work, Saurav associated with Mentoring Club and Revive. He volunteers in his spare time for helping, coaching, and mentoring young people in taking up careers in the data science domain, data practitioners to build high-performing teams and grow the industry. He assists data science enthusiasts to stay motivated and guide them along their career path. He helps fill the knowledge gap and help aspirants understand the core of the industry. He helps aspirants analyze their progress and help them upskill accordingly. He also helps them connect with potential job opportunities with their industry-leading network.
Additionally, in the year 2018, he joined as a mentor in the Transaction Behavioral Intelligence company that accelerates business growth for banks with the use of Artificial Intelligence and Machine Learning enabled products. He is guiding their machine learning engineers with their projects. He is enhancing the capabilities of their AI-driven recommendation engine product.
Saurav is teaching the learners to grasp data science knowledge more engaging way by providing courses on the Udemy marketplace. He has created two courses on Udemy, with over twenty thousand students enrolled in it. He regularly speaks at meetups on data science topics and writes articles on data science topics in major publications such as AI Time Journal, Towards Data Science, Data Science Central, Kdnuggets, Data-Driven Investor, HackerNoon, and Infotech Report. He actively contributes academic research papers in machine learning, deep learning, natural language processing, statistics and artificial intelligence.
His book on Machine Learning for Finance was published by BPB Publications which is Asia's largest publisher of Computer and IT Books. This is possibly one of the biggest milestones of his career.
Saurav turned his passion to make knowledge available for society. Saurav believes sharing knowledge is cool, and he wishes everyone should have that passion for knowledge sharing. That would be his success.
Article | July 13, 2021
We are living in the age of Big Data, and data has become the heart and the most valuable asset for businesses across industry verticals. In the hyper-competitive market that exists today, data acts as a major contributor to achieving business intelligence and brand equity. Thus, effective data management is the key to accelerating the success of businesses. For effective data management to take place, organizations must ensure that the data that is used is accurate and reliable. With the advent of AI, businesses can now leverage machine learning to predict outcomes using historical data. This is called predictive analytics. With predictive analytics, organizations can predict anything from customer turnover to forecasting equipment maintenance. Moreover, the data that is acquired through predictive analytics is of high quality and very accurate. Let us take a look at how AI enables accurate data prediction and helps businesses to equip themselves for the digital future.
Article | February 23, 2020
Does the success of companies like Google depend on that of the algorithms or that of data? Today’s fascination with artificial intelligence (AI) reflects both our appetite for data and our excitement about the new opportunities in machine learning. Amalio Telenti, Chief Data Scientist and Head of Computational Biology at Vir Biotechnology Inc. argue that newcomers to the field of data science are blinded by the shiny object of magical algorithms and that they forget the critical infrastructures that are needed to create and to manage data in the first place.Data management and infrastructures are the little ugly duckling of data science but they are necessary for a successful program and therefore need to be built with purpose. This requires careful consideration of strategies for data capture, storage of raw and processed data and instruments for retrieval. Beyond the virtues of analysis, there are also the benefits of facilitated retrieval. While there are many solutions for visualization of corporate or industrial data, there is still a need for flexible retrieval tools in the form of search engines that query the diverse sources and forms of data and information that are generated at a given company or institution.