Introduction to Data Science
Data Science makes use of several statistical procedures. These procedures range from data transformations, data modeling, statistical operations (descriptive and inferential statistics) and machine learning modeling. Statistics is the primary asset of every Data Scientist.
In order to gain predictive responses from the models, it is an essential requirement to understand the underlying patterns of the data model. Furthermore, optimization techniques can be utilized to meet the business requirements of the user.
What do Data Scientists do?
Using various statistical tools, a Data Scientist has to develop models. With the help of these models, they help their clients in the decision-making process. Furthermore, these models support demand generation initiatives.
Analytic objectives and approaches are planned and defined by the Data Scientists who collaborate with the internal consulting team.
Data Scientists also formulate work plans to provide support – programming as well as analytical to internal consulting. There is also a provision of statistical procedures that utilize Microsoft Office and SAS suite.
It is also mandatory for the aspiring data scientists to possess strong communication skills which is the most sought non-technical skill required by many jobs. Furthermore, based on the domain of expertise of the company, the specific requirements for the job will vary accordingly.
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:
- Data Integration.
- Distributed Architecture.
- Automating Machine learning.
- Data Visualisation.
- Dashboards and BI.
- Data Engineering.
- Deployment in production mode
- Automated, data-driven decisions.
i. 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.
ii. 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 –
- 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.
- 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.
- 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.
How is Machine Learning the Driving Force behind the Future of Data Science?
Data Science is expanding due to the immense contributions made by machine learning. It has improved the data science scenario in the following ways –
1. Advanced Personalisations
Billions of users around the world are using smartphones, watches as well as other electronic devices. Customers generate such a colossal amount of data creating a huge potential for the industry to have a better understanding.
Therefore, companies are able to maximize value for themselves as well as improve the understanding of their user-base thoroughly.
2. Giving Advanced Search Engine Results to the User
Machine Learning algorithms are capable of making search results much more appealing to the user. Using Google’s advanced machine learning algorithms, we can get new content based on previous search history.
These results are predicted to grow much better in the future owing to immense researches that are ongoing in the field of machine learning.
3. Code Free Environments
With the help of Machine Learning Tools, softwares are evolving at a rate such that a Ph.D. is no longer required for understanding the depth of these operations.
This is a result of a constant evolution wherein functions like pytorch and TensorFlow can be utilized to perform rapid prototyping of data science solutions.
4. Quantum Computing
The potential for quantum computing and data science is huge in the future. Machine Learning can also process the information much faster with its accelerated learning and advanced capabilities.
Based on this, the time required for solving complex problems is significantly reduced. This will boost the health-care industry massively.