Harvard’s 9 Free Courses to Master Data Science Skills

Harvard offers nine free courses to help you become an expert in data science.

Data science has become one of the most in-demand skills in the job market of today. In a variety of industries, from finance to healthcare and beyond, the capacity to glean insightful information from massive amounts of data has become essential. One of the most prominent universities in the world, Harvard University, has acknowledged the value of data science and provides a number of free courses that might assist you in mastering this subject. In this article, we'll examine the nine free Harvard courses that can provide you the expertise you need to succeed in data science.

Programming

Learn to code as the initial step in your data science studies. Your favorite method can be used to complete this.Ideal programming languages are Python or R.

Harvard University provides Data Science: R Basics, an introductory R course created specifically for data science students, if you're interested in learning more.

You will learn about R concepts including variables, vector arithmetic, data types, and indexing in this course. Additionally, you will discover how to create charts to display data and how to alter data using programs like dplyr.


Take Harvard's free CS50 Introduction to Programming with Python course if Python is your preferred language. This course will cover a variety of concepts, including functions, variables, arguments, data types, conditional expressions, loops, methods, and objects.


The aforementioned programs can be completed at your own leisure. On the other hand, the Python course is more in-depth than the R program andis more time-consuming to complete. Additionally, R is used to teach the other courses in this roadmap, so knowing it can be beneficial if you want to follow up rapidly.

Visualization of data

One of the most effective methods for explaining your data results to someone else is visualization.

The Harvard Data Visualization program will teach you how to express data-driven insights as well as how to construct visuals in R using the ggplot2 tool.

Probability

You will learn crucial probability concepts in this course, which are essential for running statistical analyses on data. Among the topics discussed are random variables, Monte Carlo simulations, independence, expected values, standard errors, and the Central Limit Theorem.

The aforementioned subjects will be instructed througha case study, allowing you to apply what you've learned to data from the real world.

Statistics

After learning about probability, you can enroll in this course to learn the fundamentals of statistical inference and modeling.

In addition to introducing you to the fundamentals of Bayesian statistics and predictive modeling, this program will show you how to create population estimates and margins of error.

Tools for Productivity

The study of data science has nothing to do with this elective project management course. Instead, you'll discover how to use GitHub for version control, Unix/Linux for file management, and R for report creation.

You'll save a ton of time and be better able to manage complete data science projects if you can do the following.

Pre-processing of DataData Wrangling, the course that comes after it on this list, will teach you how to organize data and convert it into a form that machine learning models can easily understand.

The topics of data import into R, handling string data, data cleaning, HTML parsing, interacting with date-time objects, and text mining are all covered.

10 Exceptional Opportunities for Data Science Interns Copy

 

Internships in data science: 10 extraordinary paths to success

The subject of data science, which combines statistics, computer science, and domain knowledge, is one that is quickly developing. The need for qualified data scientists is still rising as businesses increasingly see the benefits of data-driven decision-making. An internship can be the ideal first step if you're a data science enthusiast seeking for opportunities to launch your career. This post will examine 10 outstanding opportunities for data science interns, each of which provides a distinctive learning environment and an opportunity to have an important effect.

Programs for Interns at Tech Giants

Numerous tech behemoths, including Google, Facebook, and Amazon, provide data science internship opportunities. These businesses have complex infrastructure and large datasets, giving interns access to cutting-edge tools and real-world data issues. They also provide coaching from seasoned professionals, making it a great place for budding data scientists to start.

Startups

For those who love data science, working as an intern at a startup might be a fascinating experience. Startups frequently have a restricted budget and must get the most out of their data. As a result, interns have the opportunity to work on a variety of projects, from data gathering and cleaning to the development of predictive models. The startup environment encourages invention and creativity, which enables interns to assume important responsibilities.

Organizations in the Public and Private Sectors

In order to solve social concerns and make informed decisions, government agencies and charitable organizations are increasingly relying on data. Data science interns in these fields might focus on initiatives supporting social justice, environmental preservation, public health, or education. These internships give you the chance to develop important data skills while having a real impact on society.

Institutions of finance

Data science is used by banks, investment companies, and insurance organizations to spot fraud, control risk, and maximize investments. Gaining practical expertise with big data, machine learning, and financial modeling through an internship with a financial institution. For individuals with an interest in the confluence of finance and data, it's a fantastic option.

Biotech and healthcare

Data science is essential to research, diagnosis, and patient care in the data-rich environments of the biotech and healthcare sectors. Working on projects involving drug research, electronic health record analysis, or medical imaging analysis, interns in these domains can advance healthcare.

E-commerce

Large volumes of data on consumer behavior and sales are generated by online merchants like Amazon, eBay, and Alibaba. Projects involving recommendation systems, price optimization, and consumer segmentation are all things that data science interns in e-commerce can work on. For individuals who are interested in the relationship between data and marketing, these experiences can be priceless.

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New Centre to tackle intersection between global environmental change and NCDs

 

A £10m grant has established the NIHR Global Health Research Centre on Non-Communicable Diseases and Environmental Change.

The Centre, a partnership led by Imperial College London and The George Institute for Global Health India, will work to tackle the dual challenge of a rapidly growing burden of non-communicable diseases (NCDs) and global environmental change in low- and middle-income countries (LMICs).

LMICs face unique challenges in delivering equitable, high-quality primary care services for the prevention and treatment of NCDs such as diabetes, kidney disease, hypertension and cardiovascular disease, and mental health. This is especially true among marginalised populations that are affected by - or have migrated due to - environmental risks and exposures including air pollution, flooding, and heatwaves. The Centre will focus on populations in Bangladesh, Indonesia and India which are amongst the most vulnerable to the impact of climate change on health.

Despite an increased recognition of a need for action there is limited evidence on cost-effective interventions to address major challenges emerging at the intersection of NCDs and environmental change in LMICs.

Professor Vivekanand Jha, co-lead of the Centre and Executive Director of The George Institute for Global Health, India remarked: “LMICs face dual, intertwined challenges of a rapidly growing burden of NCDs and the existential threat of global environmental change. Our Centre will focus on three major challenges at the interface of NCDs and environmental change - air pollution, water salinity and food systems and generate actionable evidence for improving health outcomes and reducing inequities in a cost-effective manner”,

The National Institute for Health and Care Research (NIHR) Global Health Research Centre for NCDs and Environmental Change includes an interdisciplinary group of academics from the International Centre for Diarrhoeal Research (Bangladesh), Sri Ramachandra Institute of Higher Education & Research (India), and University of Brawijaya (Indonesia), who will work to address specific health concerns related to environmental change.

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The average data scientist earns almost $100,000 a year — and the barriers to entry for candidates are being broken down

Companies are facing a talent shortage of experienced data scientists due to evolving technology and inflated salaries.

The need for data specialists has moved beyond the traditional tech roles. The number of jobs requiring data science skills is projected to grow by 27.9% by 2026, according to the US Bureau of Labor Statistics.

Matthew Forshaw, senior advisor for skills at The Alan Turning Institute, said his research into data skills in the UK found there was a growing demand for professionals in the finance, insurance, and manufacturing sectors.

The increased demand is putting a recruiting squeeze on the entire sector, with companies reporting they are struggling to find experienced candidates.


It's a new discipline, it's been changing quite a lot," Libby Kinsey, head of data science at Ocado, a UK-based company that licenses grocery technology, told Insider. "So it's just quite hard to find the right people with the right skills."

"I would say the shortage is a lot on the leadership side and very senior people," Claire Lebarz, head of guest data science at Airbnb, said. "And the salary war we're seeing doesn't help at all."

Data scientists in the US make an average base salary of $97,000 a year, according to Payscale. But senior data scientists at top companies can make more than double this average. Data scientists at Facebook's parent company, Meta, for example, can make up to $260,000 in base salary a year, according to disclosed foreign labor data.

Experienced data scientists and industry experts told Insider what they are looking for in new recruits.

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Data Science vs. Decision Science: What’s the Difference?

Data scientists and decision scientists do very different, though equally important, work. Here’s how to tell the difference.



At Instagram, we had many different job roles that analyzed data. A few of the data job titles included: data scientist, analyst, researcher and growth marketing.

There’s often a lot of confusion between the roles of data scientist vs. decision scientist.

We had both at Instagram and they fulfilled different needs, so I thought I’d explain the main differences I see from my personal experience in the decision science role, working closely with my data science colleagues.

DATA SCIENCE VS. DECISION SCIENCE


The data scientist focuses on finding insights and relationships via statistics. The decision scientist is looking to find insights as they relate to the decision at-hand. Example decisions might include: age groups on which to focus, the most optimal way to spend a yearly budget or how to measure a non-traditional media mix. For decision scientists, the business problem comes first; analysis follows and is dependent on the question or business decision that needs to be made.

DATA SCIENTISTS

Data is the Tool for Improving and Developing New Products Based on Robust Statistical Methods

Data scientists are looking to understand, interpret and analyze with the goal of building better products. Therefore, data quality, statistical rigor and measurement perfection are often their trademarks.

For data scientists, the analysis, statistical rigor and understanding comes first. Business challenges come second. Data scientists think about data in terms of data patterns, data processing, algorithms and statistics. Often, data scientists are conducting deep analysis and experimental statistics. They are obsessed with finding causal relationships.

Data scientists are deeply focused on data quality as it relates to their product area because better data quality results in more thorough statistical analysis.

Data scientists frame data analysis in terms of algorithms, machine learning, statistics and experimentation. They are looking to bring order to big data to find insights and learnings as they relate to their product or focus area. Theyhave a statistics lens to everything they do.
Data scientists’ north star goal: Use high-quality data and robust statistics to support product development.


DECISION SCIENTISTS

Data is the Tool to Make Decisions

Decision scientists frame data analysis in terms of the decision-making process. They are looking at the various ways of analyzing data as it relates to a specific business question posed by their stakeholder(s).

Other names for this role may include: analytics, analyst and applied analytics.

The data scientist focuses on finding insights and relationships via statistics. The decision scientist is looking to find insights as they relate to the decision at-hand. Example decisions might include: age groups on which to focus, the most optimal way to spend a yearly budget or how to measure a non-traditional media mix. For decision scientists, the business problem comes first; analysis follows and is dependent on the question or business decision that needs to be made.

The decision scientist therefore needs to take a 360-degree view of the business challenge. They need to consider the type of analysis, visualization methods and behavioral understanding that can help a stakeholder make a specific decision.

In other words, decision scientists need to make insights usable. They need to be able to work with a variety of data sources and inputs — each selected based on its ability to help answer the business question. This means a decision scientist needs to have a strong business acumen as well as a robust analytical mind. You cannot have one without the other in a decision science role.

Sometimes, measurement won’t be perfect. Business tactics aren’t always neat and tidy. For example, there is almost no clean way to create a test and control for viral or celebrity marketing, but these are both legitimate marketing approaches and the decision scientist needs to be okay with that. Businesses shouldn’t take an action so that it can be measured, but because it is the right thing to do; measurement comes next.

Sometimes a clean, causal experiment is possible and sometimes it isn’t. Decision scientists need to have a keen sense of when it’s appropriate to move forward with a decision based on correlations and when they need to push for a clean experiment. It all comes back to the business context and the decision at-hand.
Decision scientists’ north star goal: use data and statistics to support business decision making, budgeting and marketing spend.
Why Decision Science Matters

Data Science vs. Decision Science: In the Real World

In my own experience at Instagram, each data scientist was dedicated to one specific product or product feature. They spend a lot of time ensuring the data logging is accurate for that product area by running statistical analysis on trends and using complex visuals to display their type of analysis. They have a deep knowledge of their product, but not the ecosystem.

If the product changes or we launch new features attached to their product, the data scientist is responsible for both logging the new data and measuring the uptake of the new features.

On the flip side, I was in the decision science job group. My team and I supported the marketing group and the marketing leadership in helping them make decisions about marketing budgets and priorities.

I relied heavily on the tables, logging and analysis from my data science colleagues as the basis for our marketing activities. I then augmented their work with my own analysis to help our marketing leadership make decisions on where and when to spend marketing budget.

My visuals were designed for consumption and business action, and therefore had a different goal than the data scientists’ goal of using visuals to display complex analysis.

Because data scientists focus on one product area only, my analysis tended to look at relationships across products and the impact of demographics on product behavior at the company level.

My decision science team is the only team that looks at the full ecosystem on a regular basis because marketing decisions revolve around wanting to understand how one behavior interacts with another.

As you can hopefully see, there are some subtle but important differences here.

The decision scientist sits hip-to-hip with decision makers and management to help them make the best decisions for the business. Decision scientists are equal parts business leader and data analyst.

The data scientist sits hip-to-hip with data and statistical rigor. Data scientists are relentless about quality and deep analyses that drive products to scale and develop based on usage data.

Each role is necessary and critically important.

Decisions need to be made quickly to keep the business moving forward based on what is knowable now. This is the job of the decision scientist.

The business also needs to grow, scale and build better products. Deep product knowledge, a high standard of data quality and statistical rigor help ensure they’re pulling out the best insights so product leaders understand their domains. This is the job of the data scientist.

A business needs to both move forward with decision making while also improving its products for the longer term, so the decision scientist and the data scientist both contribute to the greater health of the company.

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TOP 10 US-BORN COMPANIES PAYING A FORTUNE FOR DATA SCIENTISTS IN INDIA

 

Developing countries are buzzing with new ways of reinventing themselves and in the process, are including diverse opportunities

Pandemic has seen the emergence of new cultures and work habits spawning across the globe. While Indians are struggling with keeping up with the pace of digitalization and making it inclusive, developing countries are buzzing with new ways of reinventing themselves and in the process, are including opportunities for the people from other countries yearning to realize their dollar dreams. A report was released by Glassdoor in February, highlighting the 50 best jobs in America in 2022. Among all the jobs, positions in technology grabbed the most rankings with data science jobs in the third-place only after statisticians and information security officers. So, if you are a data scientist or want to be one, dreaming of the American dreamland, only your ignorance should be the limit. These top 10 US-born companies hiring data scientists can reward you with attractive pay.


Numerator:

Salary: $1,22,000 to $1,34,000 p.a.

A data science company based in the US, known for its trendsetting methods in reinventing the market research with first-party data, it is able to provide companies with real-time insights. Their USP lies in suggesting the kind of promotion to get the maximum turnout in terms of consumer behaviour. Working at numerator is enriching with good team support. The overall Glassdoor rating of 4.1 out of 5.


Spins:

Salary: $74,000 to $1,29,000 p.a.

It is a wellness-based data processing company, that monitors market trends specifically in the natural products sector. Apart from providing consumer insights to retail outlets, it also helps in weighing their performance, consumer engagement, and finding new market opportunities. Want to have work-life balance, and a wee bit of flexibility, then choose Spins. With an overall rating of 3.6, it is a great place to work if you are looking for a good team and uncomplicated work culture.
SAS Institute:

Salary: $1,01,347 p.a.

An independent vendor in business analytics market, it leverages innovation as its deriving force. It has clients currently being served at more than 70,000 sites and the number is only growing. It provides companies instant access to data analytics through its ever-evolving methodologies in market research, which they have named ‘The Power to Know’. Glassdoor rates carry an overall rating of 3.9 for its engaging work environment and amazing team.
MU Sigma:

Salary: $56,000 to $65,000 p.a.

MU Sigma calling its methods ‘Art of Problem Solving’ itself shows its vision toward designing innovative solutions for its clients. They achieve it essentially through data mining and machine learning consulting not just to step up the competition but to use data analytics in a unique way. It is considered one of the amazing places to work and at the same time grow and a great place to kick-start a data science career. Its overall Glassdoor ratings stand at 3.2.
Cloudera:

Salary: $1,43,000 to $1,54,000 p.a.

Specialized in cloud-native services, provides enterprise data cloud for the entire data-cloud lifecycle – ingesting data and experimentation, data warehousing, and using machine learning to build and deploy models. Working with Cloudera gives employees a great deal of positive energy for the importance they place on people and provide space for employees to grow. The ‘Unplug Days’ is the best thing you can look forward to at Cloudera. It carries an overall rating of 3.5 on Glassdoor.
Splunk

Salary: $81,000 to $1,68,000 p.a.

#TurnDataIntoDoing is what they stand by, to deliver hybrid data solutions. Primarily, a data service provider in the IT security domain also specializes in enterprise observability, unified security, and custom applications, all while helping its clients to derive insights from context-specific data. As a company with an overall rating of 4.1 on Glassdoor, a four-day workweek and people-oriented cooperative work culture, and the prospect of forays outside its conventional domains, definitely it makes for an intelligent choice for a data science job aspirants.
Biz2credit

Salary: $4,01,000 p.a.

A fintech company is fast transforming itself into a SaaS digital lending platform, and plans to leverage an AI-powered digital banking platform to automate business lending. They ensure companies provide a user-friendly experience for small businesses, using cloud-based technology and help them expand into new markets. Want to be treated as a person rather than an employee, this is the place you need to look for. With a great office environment and a collaborative team, you will get to learn every single day. It has an overall Glassdoor rating of 5.
Unified:

Salary: $1,33,000 to $1,43,000 p.a.

A paid social advertising solutions development company, which integrates expert services with advanced technologies to deliver at scale solutions, for companies to reap maximum ROIs. Businesses, which utilize their services can expect value-based insights that help run their businesses sustainably and profitably. A great company for a beginner, for them to learn a lot from the training programs. If you are fond of fun activities and paid vacations, maybe Unified is the right place! For this company, Glassdoor ratings stand at 3.9
Orbital Insight:

Salary: $1,32,000 to 1,44,000 p.a.

A geospatial analytics company collects data from satellite images to provide insights into environment-dependent activities and businesses. It can sift through millions of images, combined with AI and generate usable insights in no time. With awesome technology, good use cases, and visionary leadership, it makes for an ideal job destination. The overall Glassdoor ratings for Orbital Insight add up to 5.
Devo:

Salary: $79,000 to $1,00,00 p.a.

Touted as the only cloud-native logging service provider, it is the leader in providing clients with security analytics. It employs uncompromised data collection methods for the decision-makers to take bold actions. Though Glassdoor ratings stand at 2, most employees find it an interesting place to work because of its innovative products and the diversity of the company.

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From Imperial College London to IIM Kozhikode, Top Institutes Offering Online Data Science, Machine Learning Courses

 

EXECUTIVE POST GRADUATE PROGRAMME IN DATA SCIENCE FROM IIIT BANGALORE



Candidates can enroll in a completely online postgraduate programme in data science from IIIT Bangalore. Candidates must have a bachelor’s degree with minimum 50 per cent marks to be eligible to apply for the course. It offers five unique specialisations including deep learning, business analytics, data engineering, natural language processing, and business intelligence/data analytics. The programme is for 12 months and will start from April 30. One can apply online through upGrad.

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DATA SCIENCE ACTS AS A TRIGGER TO BOOST BUSINESS GROWTH

 Data science and business expansion

Data is important, as is the science involved in translating it. Millions of bytes of information are created each day, and its value has recently surpassed that of oil. A data scientist's role is and will continue to be critical for businesses in a variety of industries.

Data science is important for businesses because it has revealed amazing arrangements and intelligent decisions in a variety of industry verticals. The incredible approach of using intelligent machines to stir massive amounts of data to comprehend and investigate behaviour and examples is absolutely amazing. To that end, data has received all of the attention. Big Data can help you "predict" your requirements ahead of time, resulting in better client support outcomes.

There are numerous ways by which Data Science is assisting businesses with running in a superior manner:

1. Business Intelligence for Making Smarter Decisions – With the gigantic expansion in the volume of information, business needs data scientists to investigate and get significant experiences from the information. Data has delivered Business Intelligence to join a wide scope of business tasks. The significant experiences will help the data science organizations to examine data at an enormous scope and gain important dynamic methodologies.

2. Improving Products – Industries expect data to foster items that suit the prerequisites of clients and furnish them with ensured fulfillment. The cycle includes the examination of client audits to track down the best fit for the items. This investigation is done with the high-level logical devices of Data. Industries use the current market patterns to devise an item for the general population.

3. Overseeing Businesses Efficiently – Businesses today are data-rich. They have plenty of information that permits them to acquire experiences through a legitimate investigation of the information. Data Science stages uncover the secret examples that are available inside the data and help to make significant investigation and forecast of occasions. Huge scope organizations and few new companies can profit from data science.

4. Mechanizing Recruitment Processes – Data Science plays a vital influence in carrying computerization to a few enterprises. Data advancements like picture acknowledgment can change over the visual data from the resume into a computerized design. It then processes the data utilizing different scientific calculations like grouping and arrangement to produce the right contender, to get everything taken care of.

Data Science and Artificial Intelligence are significantly affecting business and are quickly becoming basic for separation and sometimes for endurance. Data can increase the value of any business that can utilize its data well. From measurements and experiences across work processes and employing new competitors, to assisting ranking staff with settling on better-informed choices, data science is significant to any organization in any industry.

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10 PLATFORMS TO GET DATASETS FOR DATA SCIENCE PROJECTS IN 2022

These platforms provide large volumes of information that can be used in data science projects in 2022.

Data science can be interpreted as different things for different roles. Technically the technology revolves around extracting knowledge and insights from data and information generated through various data science tools and applications. Its rising use has made several professionals and aspiring data science professionals create and participate in projects, assignments, including data visualization, data cleaning, and data science projects, along with several machine learning projects. Practicing these projects and assignments can help professionals ace their skills and excel in their careers. In this article, we have listed 10 platforms from where professionals can get datasets for their data science projects in 2022.

• Kaggle: Kaggle is a platform where professionals can learn, practice, and sharpen their data analytics and data science. The platform provides tons of data that are public and allows the users of the platform to share code so that they can learn the best practices within the data space.

• FiveThirtyEight: FiveThirtyEight is an interactive news and sports platform that has some incredible information for data visualization projects. The platform makes a lot of their data available to the public, which means they can download and use the information according to their own convenience.

• Google Dataset Search: Google Dataset Search is one of the most comprehensive dataset search engines that are available. It claims to hold more than 25 million online datasets and assists scientists and researchers in better locating datasets. It is armed with a function, which can sort data types, update dates, and so much more.

• Data.gov: Data.gov allows its users to download and explore data from multiple US government agencies. The information can range from government budgets to climate data. It is documented quite evidently so that it becomes easier for the users to navigate them.

• AWS Public Datasets: AWS Public Datasets allow the users to download the data and work with it on their individual devices. They can analyse the data in the cloud using EC2 and Hadoop via EMR. Amazon has a page that lists all the datasets for its users and also gives free access to all the new accounts.
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• UCI Machine Learning Repository: UCI Machine Learning Repository is one of the oldest sources of datasets on the internet. The datasets are generally contributed by the users, and thus have varying levels of documentation and cleanliness. Users can download UCI Machine Learning Repository without any registration.

• Quandl: Quandl is a repository of economic and financial data. Most of this information is free, but some require purchasing. The platform is extremely useful for building models to predict economic indicators and stock prices.

• data.world: data.world describes itself as the social network for data professionals. It is a platform where they can search for copy, analyze, and download datasets. In addition to this, they can upload their data and use it to collaborate with others.

• Buzzfeed News: Buzzfeed provides datasets, analysis, libraries, tools, and guides that are used in the articles available on GitHub. It is a quite popular platform and is used by millions of data professionals.

• Academic Torrents: Academic Torrents is a new site that is geared around sharing the datasets from specific scientific papers. It is new to the dataset platform market and allows its users to browse data directly on the site.

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NFL Taps Data Science Community To Help Track Head Impacts

NFL taps data science community to help track head impacts


The NFL is continuing to crowdsource new ways to track head and helmet impacts during games from data scientists and for the second straight year the winner of its artificial intelligence competition comes from outside the United States.

The NFL and Amazon Web Services awarded $100,000 in prizes for this year’s competition with the top prize of $50,000 going to Kippei Matsuda from Osaka, Japan, the league announced Friday.

The task for Matsuda and the rest of the data scientists who took part was to use artificial intelligence to create models that would detect helmet impacts from NFL game footage and identify the specific players involved in those impacts.


NFL executive vice president Jeff Miller, who oversees health and safety, said the league started manually tracking helmet impacts for a small number of games a few years ago.

The tedious task of tracking every helmet collision, especially along the line of scrimmage, made it difficult to do more than just a small sampling of games as the league tried to gather more data on head impacts.

By sharing game film and information with the data science community, the league is hoping to continue developing better systems that can track those impacts more efficiently. The league estimates Matsuda’s winning system could detect and track helmet impacts with greater accuracy and 83 times faster than a person working manually.

“There were certainly any number of domestic participants too, but the data science community is large and looking for solutions in places or with communities you wouldn’t normally talk to may end up being a pretty fruitful exercise,” Miller said. “So I think we’ve proven that this model of working with the global data science community is helpful to us and will continue to be and we’ll continue to engage in.”

The first year of the competition in 2020 focused on models that detected all helmet impacts from NFL game footage. That competition was won by Dmytro Poplavskiy from Brisbane, Australia , which included nearly 7,800 submissions from 55 countries.

This year’s competition was focused more on specific player impacts and included 825 teams and 1,028 competitors from 65 countries, and a total of 12,600 submissions.

“This was the most exciting competition I’ve ever experienced,” Matsuda said in a statement. “It’s a very common task for computer vision to detect 2D images, but this challenge required us to consider higher dimensional data such as the 3D location of players on the field. NFL videos are also fun to watch, which is very important since we need to see the data again and again during competition. I would be honored if my AI can help improve the safety of NFL players.”

Miller said the goal of the league is to create a “digital athlete” that can become a virtual representation of the actions, movements and impacts an NFL player experiences on the field during a game and can be used to help predict and hopefully prevent injury in the future.

“That is novel for us and obviously has great importance in how we think about making the game safer for the athletes,” Miller said “It will have an effect on training and coaching, certainly. It will have an effect in rules without a doubt. It will definitely have an impact in terms of equipment, and benefits that we can see from equipment because now for the first time we’ll have a pretty good appreciation for every time somebody hits their head during the course of an NFL game, and therefore, we will look for ways to prevent many of those.”

Priya Ponnapalli, senior manager with Amazon’s Machine Learning Solutions Lab said the potential for machine learning to analyze past data but also make forward-looking projections will be helpful in the future in helping create a digital version of players at all positions and analyze the types of hits they take.

“Machine learning is a very intuitive process and you get to a certain level of performance, and in this case we’ve got some pretty accurate and comprehensive models,” Ponnapalli said. “And as we collect more data, these models are going to get better and better.”

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Identifying And Retaining Good Talent Is Crucial In Data Analytics: Vineet Shukla, Mahindra Group

 

Mahindra Group recently appointed Vineet Shukla as their Head of Data. Shukla has close to two decades of experience, of which he has spent a major chunk dabbling with data, AI and machine learning. He started his journey as a software engineer before doing an MBA from IIM Bangalore in business analytics. Before Mahindra Group, Shukla worked as the senior director for data science and machine learning at UnitedHealth Group, where he led the team and built an AI/ML practice from the ground up.

Analytics India Magazine caught up with Shukla for a detailed interview.

Edited excerpts:
AIM: You started your career as a software engineer. Why did you choose to transition to the field of machine learning and data science?
Vineet Shukla: Even when I was a software engineer, I used to work on tweaking algorithms. Owing to my deep interest in mathematics, I could make a smooth transition to data science. In this field, I got the opportunity to conceptualise and design a few ‘big data’ solutions that have helped organisations leverage their potential information assets to gain insights, leading to more efficient, effective, and competitive business decisions. I have collaborated with many key stakeholders (globally) in my career and built analytical solutions on big data, using state of the art tools and technologies (e.g. BigQuery, MapReduce, Hadoop/Hive, R, NLP, Tableau, etc.)

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Scaler Academy launches new course in data science and machine learning

 Scaler Academy launches new course in data science and machine learning

Ed-tech startup Scaler Academy today said that it has launched a new program for engineers in data science and machine learning (ML).

The company said that the program had been designed based on a survey conducted by the company with around 100 data scientists working in leading tech and product firms worldwide.

The course will have a foundation of data structures and algorithms, followed by mathematics, data mining, statistical analysis, data science, machine learning, 

deep

 learning and big data.

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TOP 10 DATA SCIENCE JOBS TO APPLY IN SEPTEMBER 2021

 

Looking for a job in the field of data science? Check out these new openings

Presently, the data science course is one of the top courses that assist you to land trending job areas globally. If you are pursuing a data science course or you are already a data scientist then, without a doubt, it is the best profession to pursue your career in the present developing world.

Each organization has its necessities with regards to data science; nonetheless, various jobs are directly or indirectly, related to data science, these jobs are data scientists, data engineers, data architects, machine learning engineers, big data engineers, and artificial intelligence experts.

Here are the top 10 data science jobs to apply for in September 2021:

 1. Senior Manager – Data Science at Bain & Company

Location: Bangalore

2. Senior Manager – Data Science at The Smart Cube

Location: Noida/Gurgaon

3. Data Scientist – Banking/Insurance at Aureus Analytics

Location: Mumbai

4. Data Scientist – Product Owner at Dell

Location: Bangalore

5. Data Scientist – Advanced Machine Learning at Thoucentric

Location: Bangalore

6. Data Scientist – Advanced Analytics at Eclerx

Location: Pune

7. Data Scientist at Analytos

Location: Kolkata

8. Data Scientist at Jumio Corporation

Location: Jaipur, Rajasthan

9. Data Scientist – C3 Developer at Shell

Location: Chennai/Bangalore

10. Data Scientist at IBM

Location: Bangalore

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TOP 10 AFFORDABLE COUNTRIES TO STUDY DATA SCIENCE

 

You can’t miss this list of countries if you are data science passionate and want to enhance your skills.

Data science is the trendiest and the most in-demand course to follow up in 2021. With millions of qubits of data produced every day, it is really important to cater to the number of data scientists and analysts to analyze, interpret, organize and distribute data. But more often than not in countries like the USA and UK, pursuing data science becomes really difficult to afford. So here we at Analytics Insight churned up the whole world’s universities and colleges and countries to bring to you the top 10 affordable countries to study data science in the world.

1. India

(The average cost of the course would be around 20,000 – 5,00,000 INR)

2. Norway

(Study Costs in Norway

Tuition Fees: FREE

Living Expenses: NOK 116,369 (appx. INR 9 – 10 Lacs) per year.)

3. Germany

(Study Costs in Germany

Tuition Fees: EUR 125 – EUR 1,500 per semester | INR 20,000 – 2 Lacs per year

Living Expenses: EUR 650 – 800 per month | appx. INR 6.5 – 8 Lacs per year.)

4. Switzerland

(Tuition Fees: CHF 500 – 2,000 per semester | INR 1.2 Lacs – 4 Lacs per year

Living Expenses: CHF 1,400 – 1,800 per month | INR 10 – 14 Lacs per year.)

4. Denmark

(Tuition Fees: DKK 45,000 – DKK 120,000 per year | INR 4.5 Lacs to 12 Lacs per year

Living Expenses: DKK 6,000 – DKK 10,000 per month | INR 7.5 Lacs – INR 11 Lacs per year.)

5. Belgium

(Tuition Fees: EUR 900 – EUR 4,000 per year | INR 70,000 – INR 3.5 Lacs per year

Living Expenses: EUR 700 – EUR 1,200 per month | INR 7 – 11 Lacs per year.)

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Amazing Applications / Uses of Data Science Today

 

Introduction

One of the questions people ask me commonly is:

Is Big Data /  Data Science really a buzz or a once-in-a-lifetime opportunity?

Different people have different answers and viewpoints to the question above. 

I don’t want to get into this debate here. I am rather taking a safer approach here. 

I would tell you a few applications which are already impacting a layman’s life. 

You can read them for yourself and decide whether this is a buzz or an opportunity.

we discussed one by one 

 1.Image Recognition

(Using data science, companies have become intelligent enough to push & sell products as per customers purchasing power & interest. Here’s how they are ruling our hearts and minds)

You upload your image with friends on Facebook and you start getting suggestions to tag your friends. This automatic tag suggestion feature uses a face recognition algorithm. Similarly, 

while using WhatsApp web, you scan a barcode in your web browser using your mobile phone. In addition, 

Google provides you the option to search for images by uploading them. It uses image recognition and provides related search results. 

To know more about image recognition.

check out this amazing (1:31) mins video: 

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Why is Data Science Important in 2021?

 

When we talk about data science, it is not about making complicated models, exquisite visualizations,   or writing code. Data science is about using data to create as much impact as possible for a company. 

Now, the impact can be in the form of multiple things like insights, data products or product recommendations for a company. Data science is used across various industries already. With the advancements in predictive modeling, data scientists can help predict the outcomes of a particular disease given the historical data of the patients. 

With data science, financial organizations can manage their resources and make smarter decisions through fraud detection.

Stages involved in Data Science?

1.Defining the Problem

2.Obtaining the Data      

3.Scrubbing/Cleaning the Data

4.Exploratory Data Analytics

5.Data Modeling

6.Data Visualisation

This section discussed with following aspects 

 

A)  ? Why is Data Science important for businesses

       ? What makes a data science job so desirable

       ? What is the future scope for Data Science

       ? Why is Data Science Important in 2021

B) examples of Data Science- centric Industries

Finally, How do I Become A Data Scientist?

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11 Facts about Data Science that you must know

 

 Statistics, Machine Learning, Data Science, or Analytics – whatever you call it, this discipline is on rising in last quarter of century primarily owing to increasing data collection abilities and an exponential increase in computational power. The field is drawing from the pool of engineers, mathematicians, computer scientists, and statisticians, and increasingly, is demanding a multi-faceted approach for successful execution. 

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Data Science vs. Data Analytics vs. Machine Learning: Expert Talk

Data science is a concept used to tackle big data and includes preparation, and analysis. A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets.

A data analyst is usually the person who can do basic descriptive statistics, visualize data, and communicate data points for conclusions. They must have a basic understanding of statistics, a perfect sense of databases, the ability to create new views, and the perception to visualize the data.

Machine learning can be defined as the practice of using algorithms to extract data, learn from it, and then forecast future trends for that topic. Traditional machine learning software is statistical analysis and predictive analysis that is used to spot patterns and catch hidden insights based on perceived data. 

Do you know more about this?

1. Data Science vs. Data Analytics

2. Data Science vs. Machine Learning

3. Enroll in Our PGP in Data Analytics, Data Science, AI and Machine Learning Today

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