Data science

Data science

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.

Data science is a “concept to unify statistics, data analysis, informatics, and their related methods” in order to “understand and analyze actual phenomena” with data, It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. However, data science is different from computer science and information science. Turing Award winner Jim Gray imagined data science as a “fourth paradigm” of science (empirical, theoretical, computational, and now data-driven) and asserted that “everything about science is changing because of the impact of information technology” and the data deluge.

 Data science is very broad and frankly speaking an intimidating field which may circulate you in the start but we will help you by providing almost all the essential tips which can lead you to ta very bright start. the following tips should be considered before the approach.

  • Choosing the right field

There are a lot of varied roles in the data science industry. A data visualization expert, a machine learning expert, a data scientist, data engineer, etc are a few of the many roles that you could go into. Depending on your background and your work experience, getting into one role would be easier than another role. For example, if you a software developer, it would not be difficult for you to shift into data engineering. So, until and unless you are clear about what you want to become, you will stay confused about the path to take and skills to hone. 

  • Choose a tool/language and stick to it

As I mentioned before, it is important for you to get an end-to-end experience of whichever topic you pursue. A difficult question which one faces in getting hands-on is which language/tool should you choose?

This would probably be the most asked question by beginners. The most straightforward answer would be to choose any of the mainstream tools/languages there is and start your data science journey. After all, tools are just a means for implementation; but understanding the concept is more important.

To help you find the right tool for your path in the field of data science visit the link to enhance your knowledge about them in broader way.

Lot of beginners face the problem of choosing the best suitable programming language between the three prominent languages for data science Python, R and SAS. In order to help your needs we have analyzed a comparison between these three languages.

  • Peer groups

 The next important thing for you to do would be to join a peer group. Why is this important? This is because a peer group keeps you motivated. Taking up a new field may seem a bit daunting when you do it alone, but when you have friends who are alongside you, the task seems a bit easier.

The most preferable way to be in a peer group is to have a group of people you can physically interact with.  Otherwise, you can either have a bunch of people over the internet who share similar goals, such as joining a Massive online course and interacting with the batch mates.

  • Communication skills

Communication skills are even more important when you are working in the field. To share your ideas with a colleague or to prove your point in a meeting, you should know how to communicate efficiently. With all the technical knowledge you should also gain the skill of communication over time.

  • Network with the relevant people 

You get to meet people in your area who work actively in the field, which provides you networking opportunities along with establishing a relationship with them will in turn help you advance your career heavily. A networking contact might:

  • Give you inside information of what’s happening in your field of interest
  • help you to have mentorship support
  • Help you search for a job, this would either be tips on job hunting through leads or possible employment opportunities directly.


  • Basic knowledge of SQL

Its the most important and crucial skill for data scientist. Knowledge of data storage techniques along with the basics of big data will make you much more favorable than a person which hi-fi words on the resume, it’s because organizations are still figuring their data science requirements.

These organizations want SQL professionals that can help them with their day-to-day tasks.



Data science- Realm of opportunities

Data scientists must have a combination of analytic, machine learning, data mining, and statistical skills in addition to experience with algorithms and coding. Data scientists also have expertise in the following programs: R, SAS, Python, Matlab, SQL, Hive, Pig, and Spark. But one of the most important skills that a good data scientist possesses is the ability to explain the significance of data in a way that can be easily understood by others. Polished verbal and written communication skills are key for this role, in a way they may not be for many others listed below.

Often, data scientists will find themselves called upon to express their findings and analysis to their superiors, colleagues on different teams, and even company stakeholders who may (but just as often may not) be able to follow understand the technical jargon that data science professionals likely take for granted. The HBR rates this lack of easy communication between data teams and non-technical stakeholders as one of the toughest barriers facing the field. “Data teams know they’re sitting on valuable insights but can’t sell them,” HBR says. “They say decision makers misunderstand or oversimplify their analysis and expect them to do magic, to provide the right answers to all their questions. [Executives] don’t see tangible results because the results aren’t communicated in their language.” This is core to what a good data scientist will be able to do. Explaining what information has discovered and what needs to be done about it now that they have, all in a comprehensive and easily digestible way. Not always an easy task.

The demand for data science is huge and employers are investing significant time and money in Data Scientists. So taking the right steps will lead to exponential growth. This guide provides tips that can get you started and help you to avoid some costly mistakes.

Data science experts are needed in almost every field, from government security to dating apps. Millions of businesses and government departments rely on big data to succeed and better serve their customers. Data science careers are in high demand and this trend will not be slowing down any time soon, if ever. There are many fields which can lead your path to success related to data science and some of them are,

  1. Data scientist 
  2. Data architect 
  3. Data engineer 
  4. Data analyst 
  5. Data visualization