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

Data Science Methodology- A complete Overview

The people who work in Data Science and are busy finding the answers for different questions every day comes across the Data Science Methodology. Data Science Methodology indicates the routine for finding solutions to a specific problem. This is a cyclic process that undergoes a critic behaviour guiding business analysts and data scientists to act accordingly. Business Understanding: Before solving any problem in the Business domain it needs to be understood properly. Business understanding forms a concrete base, which further leads to easy resolution of queries. We should have the clarity of what is the exact problem we are going to solve. Analytic Understanding: Based on the above business understanding one should decide the analytical approach to follow. The approaches can be of 4 types: Descriptive approach (current status and information provided), Diagnostic approach(a.k.a statistical analysis, what is happening and why it is happening), Predictive approach(it forecasts on...
Post a Comment
Read more

How to deal with missing values in data cleaning

The data you inherit for analysis will come from multiple sources and would have been pulled adhoc. So this data will not be immediately ready for you to run any kind of model on. One of the most common issues you will have to deal with is missing values in the dataset. There are many reasons why values might be missing - intentional, user did not fill up, online forms broken, accidentally deleted, legacy issues etc.  Either way you will need to fix this problem. There are 3 ways to do this - either you will ignore the missing values, delete the missing value rows or fill the missing values with an approximation. Its easiest to just drop the missing observations but you need to very careful before you do that, because the absence of a value might actually be conveying some information about the data pattern. If you decide to drop missing values : df_no_missing = df.dropna() will drop any rows with any value missing. Even if some values are available in a row it will still get dropp...
1 comment
Read more

CondaValueError: Value error: invalid package specification

Recently I was trying to create Conda Environment and wanted to install Tensorflow but i have faced some issue , so i have done some research and done trouble shooting related to that . Here am going to share how to trouble shoot if you are getting Conda Value error while creating Conda environment and install tensorflow . Open Anaconda Prompt (as administrator if it was installed for all users) Run  conda update conda Run the installer again Make sure all pkg are updated: Launch the console from Anaconda Navigator and conda create -n mypython python=3.6.8 After Installing Conda environment please active the conda now :  conda activate mypython once conda environment has been activated kindly install tensorflow 2.0 by using this command pip install tensorflow==2.0.0 once Tensorflow has been successfully install kindly run the command :  pip show tensorflow Try to Run Comman PIP Install Jupyter lab and after ins...
1 comment
Read more