career plan is a practical strategy that allows you to determine your skills and interests, set career goals, and put actions in place that will help you reach them. It’s a continuous process, and it includes an overview of: Your current skills and experience. Your career goals. If you are looking for a high pay scale and also wanted to be in a fast growing industries, then the below mention programs could bring a vital change:

FOR FULL STACK (Front end and back end as a career option):

If you’re planning to do a course in full-stack development, I assume that you know, you need to learn technologies used across both—front-end and back-end. A full-stack developer simply means that a developer can work on front-end and back-end.

A full-stack developer will have a combination of both front-end and back-end development skills. Says Dai, “Being a full stack developer means taking a holistic view?—?comparing the pros and cons of both back-end and front-end before determining where the logic should sit.”

For a true full-stack developer this means not just being able to know the front-end and back-end technologies and how to apply them correctly. It also means being able to engineer a full solution?—?and see where the separation of logic should lie.

Basic required to be a Full stack web developer:

Basic understanding of Html, CSS, CSS5 or 6, Basic of Java and JS, Servers etc, however at our place we (Ismart Learning) provide new aspirant with all the basic information required and make them market ready.


Choosing a right career and to be successful in it is a big question in today’s analytics growing market, one such new vogue is data science.

Data science involves mix of computer science, mathematics and trend spotter, their job is to decipher large data and do further analysis to drive company successfully.

You won’t be able to grab an opportunity until and unless you have knowledge about it in order to build a career in data science skills you need to learn.

  • Applied mathematics.
  • Programming and communication.
  • Ability to test hypotheses.
  • Languages which include Python, Hadoop, SQL, R, SPSS, and tableau.

Other than the skills mentioned above you need to have a degree in:

  • Mathematics/science/operational research/economics or in Information technology.
  • If you are software engineer it will be easy for you to switch on data science as most of the work involves in programming and analysis.


Are you starting your career as a data analyst from ground zero? Then you have to pay extra attention to learning the tricks of the trade and work on honing your skills. To top that, you need a complete plan on how to excel in the field and stand neck-to-neck or even ahead of those who already have some experience in the field.

And, to do that, you need to take into account the below-mentioned aspects.

Statistics and Mathematics

Machine Learning 

Data analysts collect process and perform statistical analyses of data. Their skills may not be as advanced as data scientists, but their goals are the same – to discover how data can be used to answer questions and solve problems.

With the development of computers and an ever increasing move toward technological intertwinement, data analysis began to evolve. Early data analysts use tabulating machines to count data from punch cards. In 1980, the development of the relational database gave new breath to data analysts, which allowed them to use Sequel (SQL) to retrieve data from databases.

Today, data analysts can be found in a wide array of industries utilizing programming languages and statistics to pull, sort and present data in many forms in the benefit of the organization, people, and/or company.

Instead of being free to create their own big data projects, they may be limited to tackling specific business tasks using existing tools, systems and data sets.

However, there are plenty of companies who don’t make a clear distinction between the two roles.

May 17, 2019


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