How to Start a Data Science Company
How to be a data scientist
A data scientist has a bright future. As new technology keeps evolving then the production of data is also increasing. So the amount of data is increasing multiple times. So the demand for data scientists is also increasing.
Everyone has some questions and it needs to be answered. A data scientist will extract answers from big data.
Government organizations, businesses, and non-profit organizations produce data on a daily basis. Now, this data needs to be sorted, interpreted to fulfill our purposes.
For your business, you require a marketing plan, this marketing plan can be achieved through data if you have, otherwise, you will purchase data from other organizations. A data scientist will go through data and will find a pattern of behaviors, and will produce a marketing plan for your business.
Every average person can gather a small amount of data, get information about it. If the data is big then it needs focus and techniques to handle this big data and extract useful information.
A data scientist has a vast educational background with basic knowledge courses of IT and some other majors include maths and statistics.
Today it is somewhat difficult to define the area of data science. It will take some time to clearly define the scope of data scientists. One possible definition of a data scientist is “it is a person who will produce predictive or explanatory models using machine learning and statistics.
Data scientist Background
A person should have a computer background, he should be a computer engineer. They are able to create machine learning models, with optimized processing as well as cost-effective.
It is also possible that a person from other fields can also join data science because python is used as API, which is a much easier high-level language. The syntax of python is so easy and you can easily learn it.
Advanced tools available help you to write algorithms such as SagaMaker. So sometimes I think programming is not very important to start your career as a data scientist.
Now the question is what to study?
There are many languages you can study but I will recommend Python because it is so simple and it has a large community for data analysis.
When you go through different courses of data science then the focus is on the derivation of models. Institute will train you to understand how different models work. A model is just a BlackBox and you give input to it, and after some process it produces output. It involves some algorithms or techniques which are common to all models. You first understand these techniques then go behind the mathematical or statistical differences between them.
As we already know that the amount of data is increasing due to the advancement of new technologies. Companies want to take advantage of this big data, they are utilizing their resources to make something out from it. Companies need a person, who has the skill of processing, analyzing these structured or unstructured data, produced digitally.
Today it has great scope in the market with a high paycheck. You can work as a freelancer. You can start your own company, companies will contact you for your services.
Before starting your company, there are a few things you keep in mind.
- Planning before to start a company from services to finance.
- Hire competent persons with expertise in their subject.
- Innovative ideas, don’t follow our competitors but adopt different ways to handle the projects.
- Alway connect with developers so that you provide valuable results.
- If you have plenty of customers at a time then you can outsource the best people across the globe to make your job done efficiently.
- Online presence is very important for your company.
It is important to keep in mind that you should always deliver value to your customers. Money is not everything and if your purpose is to earn money and your customer is not satisfied with you then you are losing your business.
There are some key responsibilities for data scientists in a company as given below;
- Extracting Data
- Pipeline for data
- Developing metrics for measuring the quality of a product.
- Visual representation of findings from the data.
- Predicting the future from the given models
- Milestones are set for testing and validating.
Data collection is the first part and an important part of data products. You must also consider the user base and event logs that will access your application. For example, if are going to develop an app then first, you have to analyze the user base in three steps.
- How many users will install your application?
- You must know how many active sessions users are currently using.
- How many users are using paid services?
Here you require data on the above-mentioned parameters. You must embed your application with trackers. Trackers will help you collect data in a dynamic space.
Collected data are analyzed and processed and the result is presented to users. The data pipeline is connected to the database. Data pipeline helps the data scientist to analyze and process the data in real-time.
A scientist can use both lengthy batch queries and small queries to avoid any delay in the process of big data.
Pipelines should be scalable that can withstand big data and there is no chance of data losing after updating and making changes to the pipeline.
You check and test each component of the pipeline to assess their capability and data processing capability.
There are different types of data types and accordingly, pipelines should be designed.
- Raw data has no specific format and has no schema applied to it. So appropriate schema is applied to it in the later stages of the pipeline.
- When a schema is applied to raw data then the data becomes processed data and it is stored in a specific place in the pipeline.
- Cooked data is derived from data from processed data.
Analyzing the metrics of data is an important part of a data scientist. These metrics are related to the measurement of the health of data products. Now cooked data describe the health of data product. As a company, you have to identify the metrics, tell you about the health of data products.