If you belong to the world of Data, hackathon is not a new word to you. Several organizations host hackathons online but how do you pick the right one for yourself? Especially if you are a beginner?

What you do in a hackathon is only an easier version of what your job as a data scientist would require. From personal experience, Kaggle community is a boon to budding aspirants! It does wonders in enhancing one’s skillset by providing a competitive exposure.

The dataset on Kaggle and other platforms is created for the purpose of competitions and giving the participants a taste of work that data scientists are expected to do. However, real world data is much messier than what you would work with on these platforms. Nevertheless, it’s a great way to polish and upgrade your skills.

Read more at: https://analyticsindiamag.com/how-much-is-kaggle-relevant-for-real-life-data-science/

If you’re not tracking VR analytics, how do you know what works and what doesn’t? How do you prove ROI?

Utilizing quantitative and qualitative data can put you ahead of your competitors.  After all, without analyzing your data within VR is just guess work.

Doing this gives businesses the ability to track users in a 3D space instead of 2D screens. Traditional 2D tracking metrics such as clicks, swipes, scrolls or taps are certainly not the best ways to capture the depth of data available in these 3D environments.

VR specific metrics include Eye tracking to see what draws their attention, user interaction with specific objects, tracking 3D spatial data and biometrics to measure the emotional state of users to name a few.

These metrics are used by businesses to develop better products, train employees effectively and more efficiently and to understand customer buying behaviour.

Read more at: https://www.tobiipro.com/blog/why-vr-analytics-eye-tracking/

Each one of us have experienced Artificial Intelligence (AI) in our daily lives- from customized Netflix recommendations to personalized Spotify playlists to voice assistants like Alexa – all of these show how integral AI-enabled systems have become a part of our lives.

On the business front, most organizations are heavily investing in AI/ML capabilities. Whether it is automation of critical business processes, building an omni-channel supply chain or empowering customer-facing teams with chatbots, AI based systems significantly reduce manual work and costs for businesses leading to higher profitability.

However, Machine-learning systems are only as good as the data the are trained upon. Many AI experts believe that AI should be trained not only on simple worst-case scenarios but also on historical events like the Great Depression of 1930s, the 2007-08 financial crisis and the current COVID-19 pandemic.

Today, as humans rely on AI, they cannot leave AI to function by itself without human oversight because machines do not possess a moral or social compass. AI is as good as the data it is trained upon, which, may reflect the bias and though process of its creators.

Read more at: https://www.lionbridge.com/blog/3-reasons-why-ai-needs-humans/

No more will you hear about Chatbots being the next big thing. They’re already here and here to stay! Top domains where Chatbots are proving beneficial are:

1.      Ecommerce and Online Marketing: Messenger Chatbots have higher open rates and click through rates than Email, as a result of which many online marketers have begun using Chatbots as a way of getting website visitors’ information. Redirecting the customer to the correct sales channel, content gamification and relationship marketing are additional benefits it brings to this domain.

2.      Customer Service: The best use of technology right now is in automating the easy questions that get asked over and over again with a live agent takeover whenever the bot cannot answer a question. When the bot is stumped, it automatically sends the questions to a live agent, listens to the answer and then learn how to answer such questions in future.

3.      Travel, Tourism and Hospitality: Bots in this space are being successful on a number of critical fronts- they increase revenue, increase customer satisfaction, increase engagement and brand loyalty and lower costs via automation.

4.      Banking, Financial Services and Fintech: First and foremost, bots can help warn you about issues and dangers with your bank account. Bots can give you suggestions on what to do with your money- it can give you a cost breakdown of where you are spending or how can you move money around in order to save more money. Banks are also using chatbots internally to help automate tasks.

5.      HR and Recruiting: Chatbots can engage applicants and pre-screen them and make sure they’re qualified by asking a few questions. They also help in easing the process of on boarding new employees.

What other uses could be coming next? Read more at:

By definition, Data Science is a multi-disciplinary field that uses scientific methods, algorithms and systems to extract knowledge from structured and unstructured data. It processes enormous volume of information to draw meaningful conclusion and help businesses grow and expand.

Some of the biggest advantages analyzing data can give to your brand include improving efficiency, cut costs, boost sales, better recruitment, identifying opportunities and targeting the right set of audience to name a few.

Focusing on the practical ideas, four ways you can use big data to raise brand awareness include:

1.      Personalization: Analyzing consumer related information helps in understanding their preferences on an individual level. You can customize offers so as to fit each user individually.

2.      Choose the most relevant marketing channels: Brand awareness largely depends on marketing strategy and the channels you choose to promote business. For instance, Instagram marketing may help you attract younger users while marketing on LinkedIn gathers business professionals.

3.      Create better content: Data analytics enables you to learn about the buying persona such as education, relationship status, professional status, personal interests, demographics, leisure time activities, etc.

4.      Quality reporting: Using data science you can figure out the strengths and weaknesses of the brand, website traffic, social media performance and many other features.

Have you ever thought about incorporating data science into your business strategy?

Details at: https://www.business.com/articles/drive-business-growth-with-analytics/

Internet of Things is described as a digitally connected universe of everyday devices which are embedded with internet connectivity, sensors and other hardware which allow communication through web. From health tracking Fitbits to Smart blackboards, IoT has made everything around us smart. On a smaller scale, it would be switching on a TV using your phone and on a larger scale planning smart cities with sensors all over.

Why is IoT so important?

The sensors installed are capable of sending information and/or receiving information and acting upon it. These are beneficial as they help improve and innovate lives of customers, businesses and society at large. Businesses have invested extensively in R&D to innovate and develop out-of-the -box products.

Read more at:  https://www.zdnet.com/article/what-is-the-internet-of-things-everything-you-need-to-know-about-the-iot-right-now/

In literal sense, a forest is an area full of trees. Likewise, in technical sense, a Random Forest is essentially a collection of Decision Trees. Although both are classification algorithms which are supervised in nature, which one is better to use?

A Decision Tree is built on an entire data set, using all the features/variables while a Random forest randomly (as the name suggests) selects observations/rows and specific features/variables to build several decision trees and then average the results. Each tree “votes” or chooses the  class and the one receiving the most votes by majority is the “winner” or the predicted class.

A Decision tree is comparatively easier to interpret and visualize, works well on large datasets and can handle categorical as well as numerical data. However, choosing a comfortable algorithm for optimal choice at each node and decision trees are also vulnerable to over fitting.

Random Forests come to our rescue in such situations. Since they select samples and the results are aggregated and averaged, they are more robust than decision trees. Random Forests are a strong modelling technique than Decision Trees.

Read more at: https://www.analyticsvidhya.com/blog/2020/05/decision-tree-vs-random-forest-algorithm/