Master's of Science in Data Science
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Villa Park, Illinois, United States
Special thanks to my mentors Dr. Darshan Ingle, Rajan Chettri, Sumit Shukla for the hands on sessions. Thanks to upGrad for this is wonderful learning platform.
United Arab Emirates
Learning experience was amazing and exciting because the course contents are more practical rather than just theoretical.
I chose upGrad because of its association with IIITB and LJMU and its online platform.
Thanks to upGrad, I was able to achieve my dream of completing my Masters in Data Science 20 years after completing my graduation.
The level of expertise, in-depth knowledge of the lectures, hands on practice session, the wider communication and interpersonal skills makes upGrad the number one choice.
APN Software Services Inc
upGrad's professional approach to education was encouraging as well as refreshing in these times. Never felt like I missed real physical classes.
Data Analyst, Ganit
upGrad has groomed me not only with the course content but also with additional efforts they you industry ready.
Data Analyst, Newmedia, London
upGrad makes you very disciplined and well organized. I would like to thank my team member and upGrad student mentor for constant support.
Data Scientist, Common Bank of Australia
upGrad is highly helpful as they align you and your preparation in the direction of your goal.
Head of Global Operations, Whiteklay
I will not deny that I loved the way upGrad has branded itself. It is now also the best Startup on LinkedIn in 2020!
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If there is one good thing that the COVID-19 situation has shown us more vividly, it’s to rely on data more. To get the most out of the generated data, organisations need to spend more on innovations, problem-solving approaches, and employees’ upskilling. Here are two reasons why you can't escape data science upskilling:
1. How Many Job Opportunities Will Be There for Data Science Experts?
- More than 2,50,000 e-commerce firms exist globally. Therefore, it is evident that these firms will require a large workforce of data analysts and data scientists to analyse enormous amounts of data generated every day.
- According to the latest survey conducted by Analytics Insight, in 2021, more than 3,037,810 new job openings will spring up. Startups and MNCs are posting job roles for data science experts globally and in the US. It vividly indicates that data is a big hot job openings aggregator.
2. Why Can’t You Escape Upskilling Yourself?
- Regardless of the skills, degree, or experience, there is always a path to pursue Data Science as a career option.
- As per the data science industry prediction 2021, the US and India are the top two countries to generate demand for more than 50,000 data scientists and over 300,000 data analysts job opportunities.
Skills required to prepare yourself as data analysts are Statistics, programming (using Python or R), Machine Learning, Multivariable Calculus, Data Wrangling, Data visualisation, Data Intuition, and Data Communication.
upGrad has an unparalleled collection of data science courses with varying prices and duration. Check it out today!
If you are someone with an analytical mindset and tech chops, data science asks for honing your business acumen, global awareness, critical thinking, and relationship skills. The following industries are the top 3 that infuse your passion for data science.
1. Music and Data Science Analytics
Data science has a significant contribution to the music industry. Data science analysis helps companies to analyze trends and predict the next big hit. If your calling is in the music industry, imagine joining companies like Spotify, Hungama, Saavn, Pandora etc.
As a data science analyst, you assess market trends to help your company release relevant music that your audience is going to enjoy. Well, if you are interested in movies, check out how data science is already transforming the movie industry.
2. Travel and Data Science Analytics
Watching National Geographic and TLC has inspired many millennials to consider a job in the travel industry. Here, data science can help you take a flight to land a job in companies like Tripadvisor, Expedia, Trivago, and the coolest AirBnB.
You know working for these companies is a rewarding experience, but to attract a reward like that you have to be the person helping your travel company forecast bookings through artificial intelligence, machine learning, and predictive analytics.
3. Food and Data Science
Data science controls AI-enabled restaurant-management software. Consider Zomato or Swiggy.
Think about how the surge price increases or decreases, how they market your favourite dish from your favourite or similar restaurant through geo-location, control food product availability, pricing, and changing trends in customer and general market preferences.
You just need the theoretical and practical skills to ace in your domain with data science analytics. Wait no more, drill the rig here. You choose data science because the power of data rewards your cognitive muscles for the betterment of your future and that of the world.
Over the past few years, the R programming language has gained significant traction in the Data Science and Machine Learning communities. This is mainly because it is a multi-purpose language that can be used for statistical analysis, data visualization, data manipulation, predictive modelling, forecast analysis, and much more. Here, we’re going to focus on the first part of landing a job in the domain – the R interview. Here are the 2 most commonly asked questions in R interviews!
1. What is R?
- R is a programming language and environment specifically designed for statistical computing and graphics. It comes with an extensive catalogue of statistical and graphical methods including linear regression, classification, clustering, time-series analysis, statistical inference, and ML algorithms, to name a few.
2. Name the different data structures in R.
R has four primary data structures:
- Vector: It is a sequence of data elements belonging to the same type. Members within a Vector are known as components.
- List: It is an R object that can contain elements of different types, including numbers, strings, vectors, or another list.
- Matrix: It is a two-dimensional data structure that can bind vectors of the same length. The elements within a Matrix must be of the same type – numeric, or character, or logical, or complex.
- Dataframe: It is a more generic version of a matrix, that is it can contain elements of different data types. A Data Frame combines the characteristics of Matrices and Lists like a rectangular list, and its columns usually have different data types.
If you are interested to know more about data science, check out upGrad’s data science programs. Happy learning!