A guide to making a career in Data science
Irrespective of one’s professional vocation or educational background AI, ML, Data Science and Data Analytics are terms we are all privy to. However, due to their sophisticated interrelation with one another these terms are used interchangeably. Which is unreasonable, in this post we would be going into details about how these terms are different yet interlinked to each other. But first let’s have an understanding of what these words stand for individually.
The primary objective of data science is getting new results from data. In practical terms, everything connected with data selection, preparation and analysis pertains to data science.
Data science enables us to extrapolate relevant information from huge bulks of data. Almost every organization generates tons of data through various operations and reports. Data science uses that data that would have otherwise gone to waste and exploits it in order to generate information to make useful decisions and accurate predictions.
According to a report by ‘The Economist’ in 2017 data surpassed oil in value; data is now the most valuable asset on the planet. We’re all aware that every tech company is collecting immense amounts of data. Because the more data you have at your disposal, the more business insights you can produce. With the help of data science you can unravel patterns in data that you didn’t even know existed. Due to such vast potential data science has become one of the fastest growing stream in the tech world.
Usage of data Science
- Predictive analytics (prediction of events)
- Recommendation systems (like aforementioned companies)
- Social research
- Tactical Optimization
Popular companies that use Data Science:
What precisely is AI? AI is explained as any task performed by a program or a machine that, if a human performed the same task, we would say the human would have to apply intelligence.
Artificial intelligence is basically the potential of machines to comprehend data, learn from data, identify relevant patterns hidden in the data, to make inference from the data, and eventually make decisions based on the knowledge. Now this may sound surprisingly similar to the process to human learning. But if you take into consideration the immense computational power of our computers, it gives them the ability to process and learn from huge bulks of data which humans can’t. which brings us to our next point , you need to make sure that you have adequate data for the AI to learn from. If you use small data lake in order to train your model, the accuracy of your model would be extremely low. So the larger data lake you have, better is the training of your model, and more accurate would be your result. With respect to the type and size of your data you can use various algorithms. And this is where Deep learning and Machine learning turn up.
Applications of AI –
- Cognitive Robotics (e.g- the robot Sophia)
- Game playing (e.g- Alpha go)
- Natural language processing
- Optimization etc.
Machine learning is basically a subset of AI. In fact machine learning can also be considered as an implementation of AI. As the name suggests machine learning is used in operations where a machine needs to learn from immense bulks of data that we have available to us. But the question is how does a machine learn?
Contrary to writing explicit codes, you feed in data to an inclusive algorithm and it draws logic based on that data. There are various ways to make a machine learn. The said machine learning methods are called Supervised learning, Unsupervised learning, and Reinforcement learning. In these methods a machine is told what the traits of independent variables are and what the dependent variables are so that the machine discovers the association among the dependent and non dependent variables in the data.
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Ashpreet Kaur - Jul 2, 2021
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