5 things to consider if you want to work as a data scientist

Data Science, AI and ML
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#1

Understanding of Data :

Because data science is all about data, the first thing to look for is “a love for data.”

The following questions will aid in determining one’s fondness for data:

  • Do you know the language of data, or at the very least what data/information is?
  • Does your present employment need you to work with data?
  • Are you familiar with tables (rows/columns) and some unstructured data?
  • Most importantly, do you enjoy looking at and manipulating data?

If you answered “yes” to the most of the above questions, then you’re good to go from a “data” standpoint*.*

Understanding of algorithms/logic:

Algorithms are a collection of instructions for a computer to follow in order to complete a specific activity.
Even When you solve a riddle on paper, it’s similar to what algorithms accomplish in computers.
Because all machine learning systems are based on algorithms, having a fundamental understanding of what algorithms are and how they are developed at a high level using logic is a must.

Understanding of programming:

To start their data science adventure, no one needs to be a master coder.
However, one should be familiar with how computer programs are written. Possibly a few fundamentals:

• What are variables and constants, and what are the differences between them? What does “datatype” imply?
• What are loops and conditional statements, and how do you use them?
• What do input/output, functions, and other terms mean?
• Define client/server, databases, APIs, hosting, and deployment, among other terms.

If the preceding questions are answered confidently, the “coding” portion can begin.

Understanding of Statistics:

One of the most important areas of study in data science is statistics. There is no mandatory data science course in this domain, but the following topics should not make an aspiring data scientist uncomfortable:

• percentiles, mean, median, mode, stranded deviation/variance, etc.
• probability/Bayes theorem/distribution, etc.
• Hypothesis testing, anova, chi square, p-value, and other statistical tests

Understanding of business domain:

This point, like the others, is not a roadblock for aspiring data scientists; nonetheless, the more knowledge one has in a given business sector or domain, the easier it will be for him or her to analyze data from that domain.