Internet of Things (IoT) is the next generation of technology which can provide the core tools to automate data collection and generate insights. A recent report suggests that India IoT adoption is still at a niche stage in India.

Various challenges such as lack of clarity on what to priorities and how to attract the right talent and skills are affecting the IoT adoption. Technical challenges of connectivity, compatibility, interoperability, and cybersecurity are the other factors holding back the IoT adoption. The research report is by Deloitte India in collaboration with Confederation of Indian Industry (CII).

These challenges have created more hurdles in realising the potential of this technology. The report titled ‘Internet of Things (IoT): The rise of the connected world’ suggests that organisations should invest in IoT during the economic downturn and be ready to operate.

The studies suggest that the companies that proactively invest in key technologies during downturns are better positioned to leapfrog competitor. According to Deloitte, the big four technologies such as Deloitte, Big Data, Analytics, IoT, and Artificial Intelligence (AI) can provide the bedrock to connect organisations, generate data, and drive more intelligent operations.

Indian businesses and the government will be focused on conserving cash within India post-COVID-19. Deloitte report projects de-growth in 2020, possibly going into the first half of 2021. A recent IoT industry spending report reveals that Asia/Pacific accounted for most of the IoT spending in 2019. Indian market alone spent $20.6 billion on IoT.

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Mastering Data Science is all about practising your skills on a regular basis. While online courses cover almost all the concepts of Data Science, it is still important for data scientists to have professional project experience. Here are top professional projects that every data scientist should have worked on.

1. Customer Segmentation
This is a form of data science where unsupervised and clustering modelling technique is used to develop groups or segments of the human population or observations in data. The idea is to create groups that are separate but these groups themselves are called related features. – Between groups sum of squares (BGSS) – Within-group sum of squares (WGSS)

2. Text Classification
Text classification is a part of Natural Language Processing (NLP). It has more to do with utilising techniques to ingest text data. You can think of text classification as a technique to ingest text data.

3. Sentiment Analysis
This is also under the umbrella of NLP. Sentiment analysis is a way to assign sentiment scores to form the text. It is beneficial to use this technique when you have plenty of text data and want to digest it to figure out levels of good or bad sentiment. If you have a rating system in place, it may seem redundant but people often leave reviews with text that don’t match the numerical scores. Another benefit of sentiment analysis is that you can flag certain keywords.

4. Time Series Forecasting
This can be applied to several parts of various industries sectors. In most cases, time series forecasting can be used ultimately to allocate funds or resources. If you have a sales team, they will benefit from the forecast. The time series forecasting results in the allocation of resources and awareness of future sales. You can use algorithms such as ARIMA and LSTM to allocate the resources.

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