Skip to main content

Future of Data Science

It is rightly said that Data Scientists would be shaping the future of the businesses in the years to come.

And trust me they are already on their path to do so.

Over the years, data is constantly being generated and collected as well. Now, the field of data sciences has put this humongous pile of data to good use.

Now, data can be collected, processed, analyzed and converted into a highly useful piece of information that would benefit the businesses with better and well-informed decision-making capability.

"Data is a Precious Thing and will Last Longer than the Systems themselves."

Also, Vinod Khosla, an American Billionaire Businessman and Co-founder of Sun Microsystems declared –

"In the next 10 years, Data Science and Software will do more for Medicines than all of the Biological Sciences together."

By the above two statements, it is clear that data proliferation will never end and because of that, the use of data related technologies like Data Science and Big Data is increasing day by day. Different sectors are using Data Science for their growth and benefits. All these points are enough to explain that the future of Data Science is bright. Below are some more predictions, stats, and facts that will tell you everything about the future of Data Science and Data Scientists.

Future of Data Science

Data Science is a colossal pool of multiple data operations. These data operations also involve machine learning and statistics. Machine Learning algorithms are very much dependent on data. This data is fed to our model in the form of training set and test set which is eventually used for fine-tuning our model with various algorithmic parameters. By all means, advancement in Machine Learning is the key contributor towards the future of data science. In particular, Data Science also covers:

  1. Data Integration.
  2. Distributed Architecture.
  3. Automating Machine learning.
  4. Data Visualisation.
  5. Dashboards and BI.
  6. Data Engineering.
  7. Deployment in production mode
  8. Automated, data-driven decisions

  • Data Science currently does not have a fixed definition due to its vast number of data operations. These data operations will only increase in the future. However, the definition of data science will become more specific and constrained as it will only incorporate essential areas that define the core data science.
  • In the near future, Data Scientists will have the ability to take on areas that are business-critical as well as several complex challenges. This will facilitate the businesses to make exponential leaps in the future. Companies in the present are facing a huge shortage of data scientists. However, this is set to change in the future.
  • In India alone, there will be an acute shortage of data science professionals until 2020. The main reason for this shortage is India is because of the varied set of skills required for data science operations. There are very few existing curricula that address the requirements of data scientists and train them. However, this is gradually changing with the introduction of Data Science degrees and bootcamps that can transform a professional from a quantitative background or a software background into a fully-fledged data scientist.

Data Science Future Career Predictions

According to IBM, there is a predicted increase in the data science job openings by 364,000 to 2,720,000.

We can summarize the trends leading to the future of data science in the following three points –

  1. The increase of complex data science algorithms will be subsumed in packages in a magnitude making them quite easier to deploy. For example, a simple machine learning algorithms like decision trees which required huge resources in the past can now be easily deployed.
  2.  Large Scale Enterprises are rapidly adopting machine learning for driving their business in several ways. Automation of several tasks is one of the key future goals of the industries. As a result, they are able to prevent losses from taking place.
  3. As discussed above, the prevalence of academic programs and data literacy initiatives are allowing students to get exposed to data related disciplines. This is imparting a competitive edge to the students in order to help them stay ahead of the curve
Happy Learning..!!

Labels:

Comments

  1. Fin31 August 2021 at 03:49

    I think this is a really good article. You make this information interesting and engaging. You give readers a lot to think about and I appreciate that kind of writing. Technology analysis

    ReplyDelete

Post a Comment

Popular posts from this blog

Important Python Libraries for Data Science

Python is the most widely used programming language today. When it comes to solving data science tasks and challenges, Python never ceases to surprise its users. Most data scientists are already leveraging the power of Python programming every day. Python is an easy-to-learn, easy-to-debug, widely used, object-oriented, open-source, high-performance language, and there are many more benefits to Python programming.People in Data Science definitely know about the Python libraries that can be used in Data Science but when asked in an interview to name them or state its function, we often fumble up or probably not remember more than 5 libraries. Important Python Libraries for Data Science: Pandas NumPy SciPy Matplotlib TensorFlow Seaborn Scikit Learn Keras 1. Pandas Pandas (Python data analysis) is a must in the data science life cycle. It is the most popular and widely used Python library for data science, along with NumPy in matplotlib. With around 17,00 comments on GitH...
Post a Comment
Read more

R vs Python: Who is the Winner according to me...!!

As a data scientist, you probably want and need to learn Structured Query Language, or SQL. SQL is the de-facto language of relational databases, where most corporate information still resides. But that only gives you the ability to retrieve the data — not to clean it up or run models against it — and that’s where Python and R come in.R and Python both share similar features and are the most popular tools used by data scientists. Both are open-source and henceforth free yet Python is structured as a broadly useful programming language while R is created for statistical analysis. A little background on R R was created by Ross Ihaka and Robert Gentleman — two statisticians from the University of Auckland in New Zealand. It was initially released in 1995 and they launched a stable beta version in 2000. It’s an interpreted language (you don’t need to run it through a compiler before running the code) and has an extremely powerful suite of tools for statistical modeling and graphing...
Post a Comment
Read more

Machine Learning Interview Questions - Part 1

Q1. What is Machine Learning? Machine Learning  explores the study and construction of algorithms that can learn from and make predictions on data.  Closely related to computational statistics.  Used to devise complex models and algorithms that lend themselves to a prediction which in commercial use is known as predictive analytics. Given below, is an image representing the various domains Machine Learning lends itself to. Q2. What is Supervised Learning? Supervised learning  is the machine learning task of inferring a function from labeled training data. The training data consist of a set of training examples. Algorithms: Support Vector Machines, Regression, Naive Bayes, Decision Trees, K-nearest Neighbor Algorithm and Neural Networks E.g. If you built a fruit classifier, the labels will be “this is an orange, this is an apple and this is a banana”, based on showing the classifier examples of apples, oranges and bananas. Q3. What is Unsu...
Post a Comment
Read more