Data Scientist Salary | How to Get a Data Scientist Job? | Data Scientist Career
Welcome to this blog, Today, we are going to discuss how to get a Data Scientist job in 2020. So let’s talk about what things we are going to discuss in this post.
1. Who Is a Data Scientist?
2. What does a Data Scientist do?
3. How to get a Data Scientist Job?
4. Data Scientist Salary
5. And Some General information
Who Is a Data Scientist?
Now talking about Data Scientist in the past, there was no such job or profile called Data Science. But in today’s developing world you’ll see that a lot of companies, startups and businesses have now started to hire people who analyze their historical data.
The people who analyze the data are called Data Scientists.
Let’s take an example, if I’m running a business, and I want to predict how many sales I’ll generate in the coming quarter.
If I have some previous data of my sale and give it to a data scientist and tell him to predict how much sale I’ll make in the upcoming quarter.
So basically in this era data is everything, if you have any kind of data related to your business or company then you can predict and do some improvement to grow your company.
All these things are possible by a profile called Data Scientist. For all these reasons companies are hiring Data Scientists to get these types of insight.
Data Scientist is a combination of Mathematician, Computer Scientist, and TrendSpotter.
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What does a Data Scientist Do?
1) Data Collection
Data collection is one of the primary things of a data scientist to collect some shots of data to analyze that on some basic algorithms.
When any client gives any data to them, initially it will be raw data. In next we will see how they make the raw data to useful data and optimized data according to their needs.
2) Data Preparation
The second step of a data scientist is to make that raw data to filter out on the basis of their need. Basically they optimize the data.
They try to make that usable without any noise. There are some formats of data like csv, xlsx or better format. The data is filtered or clean out in a tabular form.
3) Exploratory Data Analysis (EDA)
In Exploratory Data Analysis data scientists do include some statistical analysis data. The statistical analysis helps them to understand the data more quickly.
This step is very important when they are doing data analysis. In this the data may show like histogram, graph or pie chart, etc.
4) Evaluating and Interpreting EDA Results
In this step they specify what they have to do with that data. As we see earlier in step 1 Data collection, we received raw data. In that raw there are some values which are null. In order to replace that value we have to replace it with some value.
This can be only done if they have performed EDA. With the help of EDA they understand what is distribution of data and how data is basically distributed.
So in order to replace that null or none value they have to provide some result why they are replacing and on what basis.
5) Model Building And Model Testing
After the data collection and filtered that data on some basis, now it is ready for Model Building. In Model Building they also perform hyper-parameter optimization.
In Model Testing data scientists test that model and observe what output is given out, if there is some error they fix it by changing values in data or implement optimized algorithms for accurate results.
Once the model is tested and gives accurate results on the basis of data, then that model is ready for further steps.
6) Model Deployment
For model deployment there are various platforms available to deploy the projects. You can also use the cloud base service to deploy the project.
On this step they test the model, live and observe the accuracy rate and some other factors. It helps to create REST API and can be accessed as front-end development.
7) Model Optimization
After Deployment they optimize the model, by giving real world data which they get. They test the model for 15-30 days whether the model is giving accurate results or not. And if not, then they optimize the model for accurate results.
How to get a Data Scientist Job?
Thing A Data Scientist Must Know:-
- Data Visualization
- Machine Learning and Advanced Machine Learning (Deep Learning)
- Tool Box
- Problem Solving
- Big Data
- Risk Analysis
- Data Ingestion
- Data Munging
- Business Domain
Prerequisites to become a Data Scientist
- Effective verbal and visual communication skills
- Good problem solving skills
- Interesting in collecting data and analyzing data
- Teamwork Ability
- Educational background, preferably Computer Science
Data Scientist Salary
So, If you’re going to choose a Data Scientist field, then what salary you will be expecting from this job?
In India the average salary of a Data scientist is ₹ 16,00,00 per annum.
In the US the average salary of a data scientist is $ 120,000 per annum. Let me clear one thing, this is the average salary as you grow you can get more and more. Based on your skill you’ll get paid.
Future of Data Science
- Increase in Data Science role
- More Data Science strategies
- Creations of more Jobs
- Advance in ML to foster Data Science
- Normalised Data Science Education
Data Science Roles
- Data Scientist
- Decision Maker
- ETL Engineer
- Machine Learning Engineer
- Data Engineer
- BI Analyst
- Tableau Developer
- Analytics Manager
List of Companies Hiring Data Scientist In India
- Mu Sigma
- And More
List of Companies Hiring Data Scientist In US
- And More