1. What is Data Science?

Ans: simply, data science is defined as the process of collecting valuable insights from structured and unstructured data by using different tools and techniques. Some  techniques practiced in data science include data analysis,data extraction, data mining, and data retrieval, to produce  results.  Besides, it is widely used to make decisions and predictions, through the use of prescriptive analytics, machine learning, and predictive causal analytics .The person who is doing such kind of jobs is called a data scientist.

2. How to become a Data scientist?

Ans: For becoming a data scientist you have to check your qualifications that you have because these things decided your path in which you became a data scientist. After that you have to take a good training program and start your learning when your learning is complete then you are ready for Data scientist interviews. This is the field where so many skills are required to learn like- Programming language - Python, Mathematics and Statistics, Algorithms, Data Visualizations  e.t.c.

3.What are the key skills of data science?

Ans: The key skills  for a data scientist job are divided into 3 groups:

1. Coding
2. Stats 
3. Domain knowledge

• Coding:   

  • R/Python- you must be good at one of these at least to become a data scientist.

  • SQL- Most firms still use relational databases, and interviews  include coding tests on SQL so be ready for it.

  • Excel-Technically, this is NOT a programming language, but all senior executives use Excel, so it is very important to learn Excel and VBA programming so you can automate reports as well as the smaller “trivial-but-daily” stuff.

• Statistics:

  • Basic statistics and maths like mean, median, averages, statistical differences, chi-square tests,sequence etc. You may know the formula but you should also know how to write the most optimal functions and/or apply it on a subset of the data? Applying stat and maths to dynamically filtered subsets seems to be the point where most people fail.

  • The statistics and logic behind machine learning algorithms.you have to explain it in english If you can't explain it in English, you don’t really know it.For sure you can apply the library functions, but it will be harder to explain your models or even tweak parameters for corner case scenarios to others.

By following this you can become a data scientist.

4. what is the difference between Data Analytics , Data Analysis, Data mining and Data science?

Ans:

• Data Science- Data science is almost everything that has to do with retrieving, processing and storing data in order to extract information and insights.Data science uses algorithms as one of its fields in extracting Data.And it deals with both structured and unstructured data.
• Data Mining- Data mining is simply the process of collecting information from huge databases that was previously incomprehensible and unknown and then using that information to make relevant business decisions.
• Data Analytics- Data analytics is the examination of data sets in order to come to the conclusions about the Data they contain , increasingly with the aid of specialized systems and software.
• Data Analysis- The process of data analysis uses analytical and logical reasoning to gain Data from the data. The main purpose of data analysis is to find meaning in data so that derived knowledge can be used to make Data decisions.

5. What is the difference between statistics and data science ?

Ans: Data science is an interdisciplinary examination that collects contributions from different sorts of information, for example organized and unstructured, to dissect and anticipate future patterns dependent on information. It preferably is utilized for understanding the constant problem or situation with the assistance of information. Though, the meaning of statistics is a part of arithmetic which gives an assortment of techniques to collect, dissect and speak to the data.

6. How do I learn Python?

Ans: It's a simple programming language that is easy to pick up. Python doesn’t require a lot of time to grasp because the syntax is quite readable and easy. Python is great for new people as you can pick it up. It is seen that people who are programmers don’t find the language difficult. 

7. What is the average salary of a data scientist?

Ans:  A report by AIM had found out that the average salary of data scientists in India is ?12.7 lakh per annum in year 2018. However, this value has decreased with the average analytics salary capped at ?12.6 lakh per year across all experience levels in 2019. In fact, now  the salary of a data scientist is hiked by 26% then the salary of software engineer in India because of the big data wave .

8. What are the top algorithms that every data scientist should Know?

Ans: It is very important for the Data scientist enthusiasts to know and understand the basic and important machine learning algorithms for keeping  themselves up-to-date with the current trends. 

List of the top five most popular algorithms that all data scientists should know are:

• Random Forest
• K-Nearest Neighbours
• Naive Bayes
• Logistic Regression
• Support Vector Machines

9. How to prepare for a data science interview?

Ans: Cracking any interview requires preparation and dedication in the case of data science it is not restricted to performing well on the big day alone. 

          An aspiring data scientist is expected to prepare for multiple things like:

• Network with peers and stay up-to-date .
• Build a portfolio of projects and MOOCs
• Learn about the job role that you are applying for
• Go through questions asked in previous interviews
• You can read the entire list here.

10. How do companies recruit data scientists?

Ans: There are numerous  traditional and non-traditional ways of hiring data scientists, Some of the traditional routes are:

• Job postings on website
• Job posting on social media
• References

But nowaday Employers have realised that to attract the unique breed of data scientists, they need to use unusual avenues like:

• Following profiles on Github
• Tracking WhatsApp communities and groups
• Organising and attending meetups
• Organising and participating in conferences
• Creating hackathons

11. Why data scientists should  be story-tellers?

Ans: As data gets deeper and more complex, it becomes difficult to bring in simplicity in it. And storytelling makes it a little simpler and more interesting, drawing interest from listeners and readers alike. Also, Stories evoke thought and bring out deeper insights.

Also, when data and analytics reveal great insights, an absence of narrative makes it difficult to relate to the facts. And this is where data storytelling comes into the scenario — it takes data visualisation to a whole new level for understanding the difficult data. With the help of real-life instances and experience, a data storyteller helps its better understanding.

Give feedback on it , if some questions are missing let us know we will surely cover them.