1. Prioritize Continuous Learning

Take the time to try new skills you want to memorize in 2024 and choose one or more online courses that give you those skills. Continuing education is critical for information scientists and analysts, as it helps them remain up-to-date in an always-evolving field. New engineering and best practices are constantly being developed. Some places to start your research would be popular run suppliers such as Coursera DataCamp and edX.

2. Reconnect With Office Mates

Interactions with your co-workers whether plotted in coming along or at work, are crucial. Purposeful formal and informal communication and conversation are important. Plan these untimely conversation pieces on the website to reduce employee productivity in organizations with new back-to-the-office (RTO) policies. It’s important to use the time you pass in the business office to establish substantial relationships with your co-doers.

3. Refresh Your Resume

Despite this general advice, maintaining your CV and LinkedIn profile is always skilful practice and should be done in every new class. With recent tidings around layoffs and worldwide financial uncertainty, now is the time to get prepared if you begin looking for a job following class planned or unplanned. By regularly reviewing and updating your CV and online visibility, you can ensure that they accurately reflect your current skills, experience, and goals. Preferably in the class of 2024 so if you want to market yourself, be ready to answer immediately!

4. Assess Your Working Style

Recently, respective road companies have proclaimed that they are acquiring out of RTO politics. Many of these are scheduled to be enforced by early 2024. Returning to an ordinary agency means being in power for two to four years a week, but this depends on other factors such as team dynamics or the degree of seniority. This is the perfect tense to pass judgment on how you are doing. The New Year’s Senate will do it for him. Some teachers boom in a whole remote area, while others want to get back up to solve. You don’t have to answer everything, but you no longer want to work at a layer that doesn’t suit your style. Determine where you fall on the spectrum. If your stream act surroundings do not conform to your style, begin the process of finding a suitable job.

5. Create Your Opportunities

Don’t hold back for chances to come to make them! In the fast-paced world of information science and analytics professionals, wealthy people have time to pursue and attain career goals. In the New Year, you will confirm your plans and clearly define your career goals for the following 1, 3, and 5 years. Whether your ends include a change in the organization or a promotion within the same company, it is important to decide whether to implement your be-after and aim steps to accomplish your goals. It is likewise significant to know that strategies change over time. With all the bustle and hustle these years, even what you thought 12 or 18 calendar months ago may look out of date.

Strategy for making resolutions:

• Remind myself: I tape my list of New Year’s resolutions to my desk so I can refer to them several times a day
• Apply this to my work: I will apply many of the recommendations to my current work and future projects.
• Implementation Goals: I will write down the time and place (or project) to implement this resolution (if possible).

TAKE THIS PROMISE

• Improved folder and document naming methods
• More use Github.
• Construct simple answers
• I won’t begin a project until I am aware of its advantages and potential savings.
• Don’t let confirmation bias get the better of you!
• One new talent every year
• Put the MLOps idea into practice.
• Avoid deploying a model without doing a deep analysis.

Conclusion
Reflecting on New Year’s resolutions for data scientists and analytics professionals provides an avenue to enhance skills, refine strategies, and embrace change in the evolving landscape of our careers. Prioritizing continuous learning, strengthening workplace connections, and redefining career goals remain pivotal pursuits. The coming year beckons us to fortify our adaptability, learn new technologies, and re-evaluate our work styles to align with our professional aspirations. With deliberate strategies and implementation goals in place, 2024 presents an opportunity to embrace growth, drive impactful transformations, and navigate the dynamic realm of data science and analytics with agility and purpose. Cheers to a year filled with professional development and impactful accomplishments!

The post Seizing Success: New Year’s Resolutions for 2024 appeared first on School of Data Science and Business Intelligence.

The world needs more data scientists, including women, to harness the growing volume of data and unlock its potential across various industries. As mentioned earlier, data scientists play a crucial role in analyzing, interpreting, and communicating valuable insights from large datasets. By promoting gender diversity and encouraging more women to enter the field, we can ensure a broader range of perspectives and ideas, leading to more innovative and effective solutions. Additionally, having more women data scientists helps reduce biases in data-driven decision-making and contributes to a more equitable and inclusive workforce, ultimately benefiting the diverse world we live in.

Data science is a fantastic career choice for women, but it can be tricky to get started. The good news is that there are many reasons why women are making great strides in the field. Here’s what we know about why data science is a great career choice for women and how to get started.

A growing number of women are becoming data scientists and tech leaders, but they face some serious roadblocks along the way. Here are some of the major issues women face in a male-dominated industry, and strategies they can use to overcome their human resources, business, administration, information technology, marketing, advertising, and sales.

• Organize datasets and identify meaningful patterns

• Build algorithms and design experiments to extract data to benefit the wider organization
• Create clear reports that visualize and communicate data insights in a business
• Use machine learning tools and statistical techniques to produce actionable solutions to problems

• Use insights to locate business opportunities in various departments like marketing or HR

Mathematics and computer science are two of the strongest prerequisites for a successful career in data science. Fortunately, there is a growing number of communities for women who want to break into these areas. For example, Women Who Code sponsors women’s coding workshops and support groups throughout the country. At this point, there’s enough interest in starting these types of spaces that companies such as Google and Facebook have been forced to start their own initiatives to encourage women to pursue careers in data science.

Female working statistics will continue to rise with more women becoming interested in data science, but still, the number of men coming into the field is greater. The biggest barrier for women entering data science is the perception that this field requires technical expertise in math and computer programming. While there are many specializations and roles within data science, including those that run on hardware, basic statistics can be done using Excel or python

Data scientists work in a variety of industries, including accounting and finance, Women in data science are essential in a data-driven world. We cannot accept large gender gaps where women are underrepresented, especially in industries that promise to change the world in such monumental ways. Women in statistics and data science will help prevent statistical bias, offer unique perspectives, and enjoy the benefits of a high-paying career path with a positive job outlook.

Data scientists and statisticians are in high demand, but the number of women available for the job is limited. As more and more people are relying on data to make decisions, we need more women working in this field, as they can help prevent bias and offer unique perspectives. The work being done by women in statistics and data science is paramount to achieving any goal.

*** The Future for Women in Data Science ***

The future of women in data science depends on women who not only occupy roles in the industry but excel in executive roles and leadership positions in the data science and analytics space. Women in data science positions need education and confidence to pursue influential leadership positions.

The conversation about women in data science and the gender gap in technology has largely discussed the dangers of male-dominated workplace culture, the lack of mentorship provided to women, and the misconceptions about what it’s like to work in data, however, researchers are now examining how a lack of confidence is a major obstacle for women in statistics and data science.

1. Why Is Data Science a Fantastic Career Choice for Women?

The first and foremost reasons that come to mind when discussing Data Science as a positive career choice are the widely heard promises of high pay and job growth. Indeed, looking at the data on Glassdoor (put together by a Data Scientist no doubt) The national average salary for a Data Scientist is $1,21,314 in the United States.

Data science is a fast-growing field that allows you to work in an analytical role with data as your tool to get insights and solve problems. In this role, you have the privilege of making systems, technologies, and even products based on analysis. A career in data science will provide you with a wide range of opportunities, such as self-driven career growth, working with cutting-edge technology and tools, learning from the best people in different fields, and gaining skills that are applicable to other sectors.

Female data scientists are in huge demand. This is due to the vast number of qualifications women (and men) can gain through data science degrees. There’s also a distinct image of what you might expect a female data scientist to look like — either a chick wearing hoodies or nerdy glasses.

 if you want to start or progress your career. In this post, I’m going to explain why being a female data scientist is an ideal choice for you and why it can open up multiple opportunities for success.

2.  The field of data science needs you.

A new report on the gender balance in this multi-billion-dollar industry released by the Institute for Women and Technology (IWT) and McKinsey & Company shows that data science is making significant progress toward becoming more diverse, but a lack of women persists. More than half of all data scientists are men.  “This is not an anomaly, it’s a systemic problem,” said Diana Frank, senior vice president at McKinsey

Gender imbalance in data science is a growing problem. The field of data science needs more women and since you are here today, you can be part of the solution!

There are many reasons to become a data scientist, but the main one is that it’s a fantastic way to further your education and career in your field of interest. The skills that you learn while becoming a data scientist can help you break into any field possible. Data science proves itself as a highly lucrative career choice for women who dare to take it on and achieve success in this male-dominated industry.

3. Women’s Biggest Barriers and How to Overcome Them

You may also now be experiencing some degree of self-doubt, as is common for women in this situation. Indeed, this doubt remains one of the biggest barriers still preventing or hindering many women from entering the field. But it doesn’t have to be.

In a large industry, where representation is still lagging behind what researchers had expected based on the rapidly increasing number of women entering STEM and related fields, one could say that Women’s Biggest Barriers and How to Overcome Them is a must-read. In fact, many women still face a lot of roadblocks in becoming an ever more prominent force in software development and technology-related fields (engineering, programming). If you are one of them, it’s time to take a step back and look at this book as a platform for growth — not only yours but also for other women who might be interested in learning about coding or pursuing a data science degree.

 But the truth is – those are justifications of fear and thus complete nonsense. Our brains are very powerful at making excuses for us all — even when trying to stick with a new exercise routine or a healthier diet.

A lot of us think that we can’t enter or excel at certain careers because of our personal circumstances, including gender and age. But this thinking has no basis in facts or research — in fact, it’s holding women back from seeing their full potential. Our brains are very good at justifying our thoughts and feelings — so instead of letting these barriers get in the way every time we try something new, let’s use them as motivation to overcome doubt and embrace opportunities!

 There are many reasons why women find it difficult to enter the field of Data Science. A lot of barriers can be related to cultural issues, education, and a lack of awareness about the roles available in data science fields.

Women’s biggest barrier to becoming data scientists is the lack of role models in the field. There are so few women that it is hard for them to find role models and mentors, which leads to more discouragement. Women also face issues when their workplace cultures don’t consider them as experts and value their contributions as much as their male colleagues.

Data science is a field that’s quickly becoming a leading way to solve problems. Data scientists are responsible for evolving the way we do things – whether it’s understanding how people use technology and then figuring out how to make this technology better by learning from them or exploring new ways to analyze information in people’s lives, or even learning from data itself. For example, one of your jobs as a Data Scientist could be to figure out ways for a company like Facebook to encourage users to use more of its services. we’re using social media – known as the FB News Feed Algorithm update process – so that we can get the most engagement possible while reducing spammy comments. While there are many types of roles you might end up serving in your career as a data scientist, no matter what type of role you end up playing, there are two skills that every good data scientist should have: Analysing and Organizing Data.

Types of Data Science jobs:

1. Data Scientist
2. Data Architect
3. Data Administrator
4. Business Analyst
5. Data Analyst
6. Business Intelligence Manager
7. Data/Analytics Manager

CONCLUSION:

Our institute is dedicated to empowering women in data science by equipping them with the necessary skills and knowledge to excel in their careers. By preparing our students for potential challenges and teaching them how to overcome these obstacles, we believe they will gain valuable insights into the similarities and differences between various datasets.

 Our curriculum focuses on introducing tools and techniques tailored to the type of data our students will encounter, helping them build a strong foundation for future success.

We encourage all aspiring data scientists, regardless of any doubts or uncertainties, to embrace their potential and join our institute. You are undoubtedly intelligent and capable, and the skills you acquire here will prove to be invaluable in your data science journey!

The post “The Power of Female Data Scientists: Shaping a More Equitable Future in Tech” appeared first on School of Data Science and Business Intelligence.

 Do you believe if I say that artificial intelligence is 1 million  users in one day? It sounds crazy right guys am speaking about OPEN AI (chat GPT)

• Netflix – 3.4 years

• Facebook – 10 months

• Spotify – 3.5 months

• Instagram – 2.5 months

• Chat GPT – 5 days

In contrast, many users are now contrasting ChatCPT with Google and expressing their opinions about both services.

Here, we’ll demonstrate the differences between the two systems and how they set themselves apart from their respective services.

Google

Google is a popular search engine that can answer all of its users’ questions. It should be noted that Google is controlled by millions of writers who write around the clock to look up any information

1. Google offers several ways to find the best match.
2. Google has a huge database with all the latest information.
3. Google delivers in real time.
4. Google is the best at providing personalized results. 
5. Google is free.

OpenAI

OpenAI has been developed by OpenAI. AI (artificial intelligence) is a chatbot. This chatbot is a human chatting with another human. Humans can interact in the same way that this chatbot can. It would be best if you typed your question, it you and provide get an answer

1. ChatGPT sometimes lacks diversity in responses
2. The main disadvantage of ChatGPT is that it is limited by data.
3. ChatGPT has serious accuracy issues.
4. Code debugging is another great feature of ChatGPT that should not be overlooked.

It takes text requests and generates human-like responses to questions in few well known

Chatgpt works as Google intended, but the two platforms are fundamentally different. 

Chat Gpt is an AI bot that excels at natural language processing (NLP) and human-like responses.

Google is a search engine that retrieves information related to your query from billions of websites.

Google search engine is better in terms of quality and accuracy of answers. It has matured over time, with more accurate and localized results for countries.

Chat GPT is a new product that provides simple and clear answers in an easy-to-read format. It provides simple answers more concisely. Chat GPT knows nothing about a data model limited to 2021. This means that Chat GPT doesn’t know what’s happening in 2023.

Chat GPT cannot compare messages with other complete or recent messages because it does not have access to the Internet. So the knowledge about Chat GPT is limited. It is only accessible during training

Chat GPT is currently free to use, but Open AI will monetize it sooner or later.

“After all, creating responses to text requests requires a lot of resources. When Elon Musk asked the CEO of OpenAI, Alex Altman replied that each request costs about a penny.”

Google is a search engine and does not have the same language processing capabilities as ChatGPTA. I told you earlier that ChatGPTs main appeal is its human interaction. Google does not give you a single answer but gives you a different result and leaves it up to the user to answer as accurately as possible.if you’re new in any profession if you read a couple of various types of articles & blogs then you get the idea about it.

While googling “you’re boss of yourself” because is in your hands

 In contrast, ChatGPT provides comprehensive and clear answers to your questions in human language.

ChatGPT admits that it occasionally provides incorrect answers. This may seem like a minor error, but it means a lot. If you doubt the validity of ChatGPT, you should look elsewhere for awesome information. 

Google is also useful for certain types of questions, as it searches the web and returns a brief but concise answer right in line. For example, if you search “Apple stock ticker” or “Cheap flights to Aruba,” it will display a ticker chart with current price information, or a calendar with the most likely cheapest days to fly and a dialogue box that connects you to multiple web sites to shop for tickets on your chosen date. ChatGPT does not search the internet in real time and has only been trained on data up to 2021, making it completely useless for these types of queries.

Reasons why people stay away from Chat GPT

• No Source for the Result Text-Only Result
• Slow No Query Suggestions Factual Errors 
• User sometimes can’t search for products which aren’t connected to the internet at all

Thanks in part to its hegemony with Apple (which permits it to be the default search engine on iOS with hefty payments every year in a deal under scrutiny by the US antitrust), Google search has been winning across all platforms.

On the other hand, Google provides multiple results for each query and allows users to select relevant information.

However, Google uses sophisticated algorithms to index and rank web pages that provide accurate and relevant results

Google is also extremely dependable, owing to the company’s large operations budget and years of experience. ChatGPT is still in beta testing and occasionally goes down.The strength of ChatGPT comes in its awareness of human language and ability to produce a response based on millions of pages of text.

Google is a multinational technology company that specializes in Internet-related services and products. It provides search engine, online advertising technologies, cloud computing, software, and hardware solutions.

ChatGPT is an AI language model developed by OpenAI that uses deep learning to generate human-like text responses. It is capable of answering a wide range of questions and generating text for various applications, including conversation, summarization, and language translation.

In summary, Google is a technology giant that offers a range of products and services related to the internet, while ChatGPT is an AI language model that can generate human-like text for various use cases.

The post Google Vs ChatGPT appeared first on School of Data Science and Business Intelligence.

In 1868, the entrepreneur and philanthropist, Jamsetji Nusserwanji Tata founded the Tata Group as a private trading entity. Tata Group covers almost every industry domain where the nation needs to develop its services. Even though getting a profit in every industry, the vision of the Tata group is still the same. There was a time in 1890 when India faced severe drought, famine &  plague in different parts of the country. So the founder of the Tata group Jamshedji Tata started a worker’s welfare initiative, an Iron & steel company, hydroelectric power plant. In every tough situation for India, Tata Group has always been a helping hand for the Nation. 

Tata Groups has a  vision of a developed Nation for the citizens of India.

In 1902, the company expanded to incorporate Indian Hotels Company Limited (IHCL) for opening the Taj Mahal Palace 1903 which was the first luxury hotel in the nation. The first business of Tata Group was in 1907. Sir Dorab first establishes Tata Iron and Steel Company (now Tata Steel) in remote Sakchi, and builds a hospital for the village, years before the first ingot is rolled out and slowly. Tata Group covered many sectors of the business &  expanded swiftly and entered into various industry sectors including steel, electricity, education, consumer goods, aviation, chemicals, cosmetics, marketing, tea as well as software services, and many more………

Tata Group has been serving all over the world to all the limits & the majority of their services are the ones that were the need of the hour for the country back when the service commenced but as we know in today’s era Internet is the platform that has to be utilized accordingly for an organization to expand its root and become a recognizable entity across the globe and the giants (Amazon, Netflix, Flipkart) of today’s market know the power of the Internet and have made use of it to their maximum strength with the constant research on improving and utilizing the internet for their purpose.

All of the companies have their website or app on the internet to give their consumers 24/7 services. Modern apps or websites can be thought of as traditional supermarkets stores (One-stop solutions). So it won’t be imprecise to call the apps of Amazon, Flipkart, Netflix, and Myntra super apps. Think of TATA and one can name almost every sector from Hospitality, IT Services, to Automobiles, etc where TATA  is providing its service and now TATA is launching its super app named “Tata Neu” with a wide range of its services for the consumers to enjoy on the tip of their finger.

Everything You Need to know About Super App

A country or a region becomes super app ready when its large base of the population uses smartphones instead of desktops, and the ecosystem of apps customized to local needs has not evolved. India has over 450 million smartphone users, which means over 60 percent of Indians are accessing wireless connectivity.

Services that which super app provides to its consumer.

Interactive Social Platform, E-Commerce Services, Transportation, Food Delivery, Bill Payment, Financial Services & Health And Insurance Service.

Tata Neu, the Tata group’s all-in-one flagship super-app,
which was built to give forte experience to the Nation.

What is Tata Super App?

Tata super app will bring Tata’s various consumer businesses under one umbrella. Tata has a reach of over 200 million households in India. Soon you could order ‘everything’ using Tata’s super app.

Tata companies merged with Tata Neu.
Tata Neu connects with several brands across the Tata universe like never before Tata Neu will have in-house brands including Croma, Westside, AirAsia India, the Taj chain of luxury hotels, BigBasket & other Tata Brands will be live.

How does Tata Neu Works?

Tata Neu will be packed with features that will extend financial services, bill payment services, and E-Commerce services, including grocery, fashion, beauty, electronics, and more to its users. Plus, you can also book hotels and tickets for movies.

A super app creates an ecosystem where there is no need for them to use a variety of apps.

The main screen is divided into five pages — Home, Tata Pay, NeuPass, Offers, and Stories.

Challenges it may face once launched.

Tata Neu, a little late to the enter the e-commerce bandwagon in India, will face considerable competition from the likes of Reliance Industries Limited‘s Jio and Amazon – each of whom already have a strong user base and have started evolving into super apps in their own right.

Competitors of Tata Super App are Paytm, Amazon, Flipkart & WhatsApp.                  

Advantage it has because of Existing market tap of Tata.

Combining their traditional consumer-first approach with the modern ethos of technology, it is an all-new way to discover the wonderful world of Tata.

Now, Tata consumers can directly come to the Tata Neu app & get it in one place.

How it is different from other apps.

The all-in-one app will have a loyalty program for retaining customers. Each brand on Tata Neu is “connected by a common reward called NeuCoins, which can be earned across all brands online and at physical locations and can be used similarly as well.

Tata Group is well known for customer orientation now they came up with a loyalty program on Tata Neu App.

How much is Tata planning to invest in Tata Neu?

Tata Sons and Ltd. are looking to invest over $2 billion in Tata Neu.  As per reports, it is also planning to raise $5 billion by selling the stakes for the digital venture to external investors.

The Race To Build Super App In India.

Following the footsteps of WeChat, many apps in other countries have also taken notice.

Indonesia came up with Go-Jek. Successful Grab from Singapore and Vietnamese ZaloPay are beginning to grow big.

In the middle of all this, the idea of a super app in India is a win-win situation for all because India is a large trillion-dollar digital economy and a mobile-first market that witnesses an increasing penetration of digital services and usage.

The first successful super app developed was WeChat by Tencent.

WeChat is a Chinese multi-purpose messaging, social media, and mobile payment app that became the world’s largest standalone mobile app in 2018, with over 1 billion monthly active users.

Does India need a Super App?

In a developing country like India, a majority of the users have an affordable smartphone. These phones are restricted in terms of storage. Hence, the user doesn’t have the luxury to keep 20 utility apps installed for each task. A Super App can help bridge this restriction by clubbing all services in one app.

Statistic criteria
Smartphone users in Million Chart, As per a report India has 750 million are smartphone users & a consumer digitally economy worth $90Billions in 2020.

How many super apps will India see in the coming days?

There are numerous super apps in the making for the citizens of India. Tata Neu, Jio super app, Paytm super app, SBI super app, Flipkart super app, Google super app, Amazon super app, WhatsApp super app, Zee5 super app, are some of the super apps that will soon be launched!

Conclusion

Now the world has become familiar with digital aspects. No need to download a single app for one service just one click & you can enjoy the benefits of all services of the TATA Neu App.

Super Apps are the future. TATA Group came up with Tata Neu in the market which will bring dramatic competition & revolutionary change in the technology Industry. Looking to ease our life as an organization coming up with new technologies seems like a new beginning for super apps.

The post Big Shark Enter Into Super App appeared first on School of Data Science and Business Intelligence.

Data scientists work in a wide variety of industries. But what they have in common is that it is a profession to solve business problems with advanced data analysis technology. Big data analyzed by data scientists is held and collected by companies. With the evolution of technology, it has become possible to collect information that could not be learned before, and to manage a large amount of data that had been abandoned, so it became possible for each company to collect a huge amount of data.

The union cabinet chaired by Prime Minister Narendra Modi approved a memorandum of cooperation between India and Japan on January 6, 2021 that would facilitate movement of skilled professionals between both Asian countries.

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Keeping this in mind, a new EdTech has taken shape, teaching life-skills through Ramayana and other Indian epics in the form of The Ramayana School which marks a new paradigm in online education. Founder of The Ramayana School (TRS), Shantanu Gupta, believes a way to fill this gap is by learning our India’s ancient text and scriptures.

Shantanu, a home-schooling father, started decoding Ramayana for his son and later started offering it to the other kids. During the Covid pandemic it got further traction and by now thousands of children from more than 15 countries have done the two flag-ship courses of The Ramayana School. The Ramayana School team is working with more than 25 partners worldwide to reach out to families across the globe.

As per Gupta, Valmiki Ramayana is a vast treatise in itself, comprising of 6 Kandas (Parts), 645 Sargas (Chapters), and 24,000 shlokas. Each shloka has great learnings and the complete understanding of The Ramayana can enrich humanity, with timeless values. Hence, we invited Mr. Shantanu Gupta to conduct an online session for our students to give them an exposure to numerous life lessons of the Ramayana that can be applied in all realms of life.

Ramayana is a book of ideal behaviour. Ramayana as an epic is an eternal source of guidance to mankind about how to live life in a manner such that it benefits society and commits no acts that one lives to regret later.
Every character and every development in the story told in Ramayana is a valuable lesson for us all. Lord Rama’s life was one of ethics and ideals, he preferred hardships of the jungle to the luxuries of the palace just because there were promises to keep
Ultimately good won over evil, but gave us lessons in ethics, integrity and righteous behaviour.

The post Leadership Skills from Ramayana: Morals before Maths and Character before Coding appeared first on School of Data Science and Business Intelligence.

Communication is a two – way process, it cannot be completed without listening and understanding the other person’s thought process. Therefore, it is very important to make sure to listen, understand and finally convey your thoughts accordingly to ensure a smooth conversation.

Make sure to directly communicate with the concerned person as much as possible. Communicating through people often leads to your thoughts being conveyed in an unintended manner.

The post Top 5 tips to enhance communication skills appeared first on School of Data Science and Business Intelligence.

Analysts are now an integral part of the coaching staff. There are subdivisions to an analyst’s job – opposition analysis, set-piece analysis, own-team’s analysis, training session analysis etc. In India, a few ISL teams used to have two analysts when they started out but now it’s mostly all done by one person per team.

The coaches let the analysts know what they require and the analysts then put their brains to work on the footage they have collected. They use data analysis software (such as Sports code and ProZone) which help in exporting a report which is then sent to the coach’s desk.

The workflow varies from one analyst to another. But usually, if there are 5 days between two games, the analyst creates documents and reports ahead of the fixture on day one. On the second day, the coach makes use of the analysis from the last game.

SportsKPI, an Indian analytics agency, has had Indian national team, FC Goa, NorthEast United, Indian Arrows etc as clients. Several ISL clubs are thought to be using the expensive tool Sportscode. Data like statistics, player movements, patterns in training and its effectiveness can be picked up from the videos.

While the role of analysts has become important in India, it can be argued that Indian football still does not have enough resources to make full use of the technology available. There is a practical difficulty to pull off a Julian Nagelsmann, who has live data and video feed of training sessions available to him on a screen.  As it stands, not all Indian clubs, especially in the I-League, invest in the analysis of the game.

ISL has helped Indian football take a step in the right direction when it comes to football analysis. Foreign coaches at several clubs made analyst a mandatory position among the staff.

If you speak to football analysts at any club, they will tell you that it is a full-time job that takes a lot of hard work. But one that’s very rewarding if you love the game. Indian football clubs could soon go from having one analyst on their roster to employing a team of analysts to extract the full potential of what technology and excellent observational skills can do.

The post Have you thought what an analyst does in Indian football? appeared first on School of Data Science and Business Intelligence.

The three primary precepts of Adaptive AI are robustness, efficiency, and agility. Robustness is the capacity to accomplish high algorithmic precision. Efficiency is the capability to accomplish low resource usage. Agility manages the capacity to adjust operational conditions dependent on current needs.

While big data has been around for a while now, applications of the technology, until recently, failed to make the impact they were expected to upon their emergence. In fact, it was estimated that 85% of big data initiatives had failed to deliver their expected results. While the reasons for the underwhelming impact of big data initiatives are numerous, not having the right tools and people to clean, analyze, and solve problems.

In recent years, the use of AI to clean — and solve problems using — the enormous body of data collected is making applications of data analytics more effective. However, when it comes to making decisions, there is still a large dependence on humans. Among the numerous ways that AI is enabling the effective utilization of big data by minimizing the role of humans, one way is through the recently emerged concept of adaptive intelligence.

Adaptive intelligence, just like business analytics, helps businesses make better decisions. But, it makes the process of decision-making faster, more adapted to individual cases, and with minimal effort from the decision-maker.

While business analytics focuses on data analysis and delivery of insights, adaptive intelligence adds the focus on context and relevance. . This makes every instance of data analysis and decision-making faster and simpler for the stakeholders which culminates in the enterprise benefiting massively in terms of productivity, effective responsiveness, and, consequently, financial profitability.

The penetration of adaptive intelligence will only increase with time as AI becomes smarter and technologies like the Internet of Things (IoT) become widely within organizations and across industries. Using IoT’s sensory and actuating end-points, adaptive intelligence can become more responsive through greater accuracy in data analysis and greater control over outcomes. Industries like healthcare can greatly benefit from adaptive intelligence as information sharing, accurate data analysis, and prompt response to situations  are vital to its operations.

Adaptive intelligence-based health technology is already being developed and tested to the benefit of all stakeholders in the healthcare industry. Eventually, adaptive intelligence may completely eclipse and replace business analytics applications in all industries.

The post What is Adaptive Intelligence and how is it making a difference? appeared first on School of Data Science and Business Intelligence.

The software design made by an engineer is based on the requirements identified by Data Engineer or Data Scientist. So, it is safe to say that data Science and engineering in a way go hand-in-hand. The rapid growth of Big Data acts as an input source for data science, processing and analyzing that data is a huge task that has become mandatory for every

business to make an informed decision. An engineer builds applications and systems, are involved in all stages of this process from design to writing code, to testing and review but at the end of the day, they need Data Scientists to extract a hidden pattern out of it and build an analysis.

You can almost guarantee that every engineer will consistently come into contact with data, no matter the engineer’s focus. It’s vitally important that the average engineer is sufficiently competent at gathering good data and properly interpreting its meaning. If you are an engineer who wants to break the barrier and switch successfully to data science

you need to have an understanding about the statistical tools and techniques needed to extract insightful information, trends and patterns from data. Having basic computer skills and technical skills makes it is a lot easier for an Engineer to pursue Data Science when compared to professionals coming from a non-technical background.

Data Scientists with an engineering background enable them to interface with their engineering knowledge to ensure higher quality of data. They have an in-depth understanding about different data systems while diagnosing results of experiments and implementing the data products. Software Engineers and IT Engineers have prior

The Big Data industry is seeing an all-time high in career growth and job demand. The study by LinkedIn found that statistical analysis and data mining was ranked the second most in-demand skill sought after by employers who posted job advertisements.

We can now find Data science opportunities in pretty much all major industries such as – Finance, IT, Consulting, Marketing, telecommunications, Infrastructure and the list goes on.

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