Machine learning Jobs, the subset of Artificial Intelligence (AI) that enables computers to “learn” to perform tasks they haven’t been explicitly programmed to do.
Basically, machine learning refers to algorithms that ingest huge amounts of data, extract patterns from that data and turn those patterns into actions. It is now being employed in a vast number of industries to improve efficiency and open up new possibilities. When you see an advertisement on a website that seems aligned to your needs and tastes, its machine learning doing its magic. When Amazon makes suggestions about what other products you might be interested in buying, a machine learning algorithm is at work behind the scenes. The same goes for your Facebook newsfeed, and countless more every-day examples.
Every company will value skills and tools a bit differently, and this is by no means an exhaustive list, but if you have experience in these areas you will be making a strong case for yourself as a data science candidate.
- Python Coding – Python is the most common coding language It’s typically seen required in data science roles, along with Java, Perl, or C/C++.
- Hadoop Platform – Although this isn’t always a requirement, it is heavily preferred in many cases. Having experience with Hive or Pig is also a strong selling point. Familiarity with cloud tools such as Amazon S3 can also be beneficial.
- SQL Database/Coding – Even though NoSQL and Hadoop have become a large component of data science, it is still expected that a candidate will be able to write and execute complex queries in SQL.
- Unstructured data – It is critical that a data scientist be able to work with unstructured data, whether it is from social media, video feeds or audio.
Education – Data scientists are highly educated – 88% have at least a Master’s degree and 46% have PhDs – and while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge necessary to be a machine learner. Their most common fields of study are Mathematics and Statistics (32%), followed by Computer Science (19%) and Engineering (16%).
SAS and/or R – In-depth knowledge of at least one of these analytical tools, for machine learning R is generally preferred.
Most machine learning algorithms are about dealing with uncertainty and making reliable predictions. The mathematical tools to deal with such settings are found in principles of probability and its derivative techniques such as Markov Decision Processes and Bayes Nets.
Also of importance are tools and techniques that enable the creation of models from data. Relevant to this task is the field of statistics and its various branches such as analysis of variance and hypothesis testing. Machine learning algorithms are often built upon statistical models.
Understanding data modeling and evaluation concepts is key to creating sound algorithms that can be trained and enhanced over time.
It largely varies on what the company is willing to offer and what your skill sets are.
It is said that a 3.5 – 4.5 LPA package is what you can expect if it is a small company or a start-up.
It can also range up to Rs 900,000 in bigger companies like Amazon, Flipkart etc.
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