With advancement of technology, one field that will be highly demanded in upcoming years is turning up to be Artificial Intelligence. It is bringing changes that is transforming the world. AI comes with its sub streams such as data mining, machine learning, neural networks etc. This field has already become the area of interest for many programmers and developers. However, still there are not many developers in this stream. 

Schools, Colleges and Organizations have started providing courses on AI. It is one of the best career option. But Artificial Intelligence is just a main stream. One should have a clear mind about his career opportunities. Following are few options you can opt if interested in Artificial Intelligence and want to have a career towards it:

1. A.I. Research Scholar
2. A.I. based Software Developer
3. Data Scientist
4. Machine Learning Engineer
5. Automation Engineer

To know more about them visit: https://www.technotification.com/2018/04/top-5-career-opportunities-in-artificial-intelligence.html

The buzzwords in technology are no new to someone. Whether it be Artificial Intelligence, Machine Learning, Data Sciences or Analytics, each of these are invading in our lives promising us better future. However, it is believed that expertise interested in data sciences are not widely spread. Data Sciences is a field that can improve business, can help in other technological fields, can help in decision making and more. 

It is rightly said that prevention is better than cure. A wrong step in data sciences can affect the decisions and the results. One should avoid the following mistakes while dealing with data:

1. Assuming your data is ready to use and all you need
2. Not exploring your data set before starting work
3. Not using control group to test your new data model in action
4. Starting with targets rather than hypotheses
5. Automating without monitoring the final outcome

To study mistakes like these read https://www.cio.com/article/3271127/data-science/12-data-science-mistakes-to-avoid.html?nsdr=true

Whether you are a data analyst in a firm or a developer training its machine learning model, you deal with data. Rather you need data! Data is one of the essential things which is needed to create a foundation. The decisions and results are relied on the output you get from the data. Thus, data is important and like every other thing, it also works on the principle of Garbage In, Garbage Out.

Many people make mistake while feeding data to their data set with a hope to get better results.

However, they end up having an ugly dataset with a greater risk of damaging their product.

The 6 most common mistakes are: Not Enough Data, Low Quality Classes, Low Quality Data, Unbalanced Classes, Unbalanced Data, No Validation or Testing.

These mistakes can be fixed which could further help in fetching good results.

One just need to remember that their dataset is equally important to the model they are working on. Without a balanced dataset, getting a fine finish product is next to impossible.

To know how to fix those mistakes visit: https://hackernoon.com/stop-feeding-garbage-to-your-model-the-6-biggest-mistakes-with-datasets-and-how-to-avoid-them-3cb7532ad3b7