Can I go for big data analytics after B.Sc honors in bioinformatics?

Yes, you can go but follow this. I suggest you look into these 7 things and see how much of each you know - and the practice the ones that seem unfamiliar to you. These were the 7 most common things I saw when I interviewed at big companies (Facebook, Intel, Square, eBay, etc) for data science related positions. Basic Programming Languages: You should know a statistical programming language, like R or Python (along with Numpy and Pandas Libraries), and a database querying language like SQL Statistics: You should be able to explain phrases like null hypothesis, P-value, maximum likelihood estimators and confidence intervals. Statistics is important to crunch data and to pick out the most important figures out of a huge dataset. This is critical in the decision-making process and to design experiments. Machine Learning: You should be able to explain K-nearest neighbors, random forests, and ensemble methods. These techniques typically are implemented in R or Python. These algorithms show to employers that you have exposure to how data science can be used in more practical manners. Data Wrangling: You should be able to clean up data. This basically means understanding that "California" and "CA" are the same thing - a negative number cannot exist in a dataset that describes population. It is all about identifying corrupt (or impure) data and and correcting/deleting them. Data Visualization: Data scientist is useless on his or her own. They need to communicate their findings to Product Managers in order to make sure those data are manifesting into real applications. Thus, familiarity with data visualization tools like ggplot is very important (so you can SHOW data, not just talk about them) Software Engineering: You should know algorithms and data structures, as they are often necessary in creating efficient algorithms for machine learning. Know the use cases and run time of these data structures: Queues, Arrays, Lists, Stacks, Trees, etc. Product Management: This one is definitely debatable, but those who understand the product are the ones who will know what metrics are the most important. There are tons of numbers one can A/B test, so product-oriented data scientist will pick the right metrics to experiment with. Know what these terms mean: Usability Testing, Wireframing, Retention and Conversion Rates, Traffic Analysis, Customer Feedback, Internal Logs, A/B Testing. In each field, I mentioned few buzzwords you should know about.

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Yes, you can go but follow this. I suggest you look into these 7 things and see how much of each you know - and the practice the ones that seem unfamiliar to you. These were the 7 most common things I saw when I interviewed at big companies (Facebook, Intel, Square, eBay, etc. ). For data Science related positions. Basic Programming Languages: You should know a statistical programming language, like R or Python (along with Numpy and Pandas Libraries), and a database querying language like SQL Statistics: You should be able to explain phrases like null hypothesis, P-value, maximum likelihood estimators and confidence intervals. Statistics is important to crunch data and to pick out the most important figures out of a huge dataset. This is critical in the decision-making process and to design experiments. Machine Learning: You should be able to explain K-the nearest neighbors, random forests, and ensemble methods. These techniques typically are implemented in R or Python. These algorithms show to employers that you have exposure to how data Science can be used in more practical manners. Data Wrangling: You should be able to clean up data. This basically means understanding that "California" and "CA" are the same thing - a negative number cannot exist in a dataset that describes population. It is all about identifying corrupt (or impure) data and correcting/deleting them. Data Visualization: Data scientist is useless on his or her own. They need to communicate their findings to Product Managers in order to make sure those data are manifesting into real applications. Thus, familiarity with data visualization tools like ggplot is very important (so you can SHOW data, not just talk about them) Software Engineering: You should know algorithms and data structures, as they are often necessary in creating efficient algorithms for machine learning. Know the use cases and run time of these data structures: Queues, Arrays, Lists, Stacks, Trees, etc. Product Management: This one is definitely debatable, but those who understand the product are the ones who will know what metrics are the most important. There are tons of numbers one can A/B test, so product-oriented data scientist will pick the right metrics to experiment with. Know what these terms mean: Usability Testing, Wireframing, Retention and Conversion Rates, Traffic Analysis, Customer Feedback, Internal Logs, A/B Testing. In each field, I mentioned few buzzwords you should know about.

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