Data Science is a type of data analysis formed at the intersection of statistics and mathematics, on the one hand, programming, on the other hand, and knowledge in any area of business, on the third. Therefore, data science agencies are very popular.
Now startups are gaining popularity very actively. However, there are things you need to know before starting, including data. And now we will consider them.
Forget about your previous experience. It is a mistake to think that this will help me in a startup. This is a completely different world with its own rules and laws. At the same time, previous experience can prepare you for hard, constant work.
Spend money efficiently. The money will run out. They will end quickly. The startup has no profit and cannot have it. Expenses will always eat up income. Therefore, think about how and what to spend. Spend expecting the worst-case scenario, but betting on fast growth.
Be prepared to deal with constant stress. Running out of money, everyday work, constant failures, problems and errors in the product, a nervous team. How not to get stuck here? There is no way around stress. The main thing is to try not to get depressed.
Remember that the whole blow falls on you. For all mistakes, failures, shortcomings to answer you. Don’t try to find someone with whom you can share this burden.
There are good IT people. Don’t be told the fairy tales that good IT people are unrealistically expensive. Or that there are no good IT people. Not true. Seek and you will find. The main thing is not to be afraid to part with bad employees.
PR is correct. PR is important for a startup, and it’s very important to understand this from the start. But an article in a newspaper, a newspaper, is naturally much inferior to a blog on a technology site, not to mention any rating. In any start-up, ratings are very important.
You need to be able to say “No”. Your time is very valuable. Don’t waste it on unnecessary projects, partnerships, and events. Investors, accelerators and incubators are also not all worth your time and attention. The sooner you start saying “No,” the more effective you will become.
Companies use Data Science regardless of the size of their business, according to statistics from Kaggle (the professional social network for data scientists). And according to IDC and Hitachi estimates, 78% of enterprises confirm that the amount of information analyzed and used has increased significantly lately. Business understands that unstructured information contains knowledge that is very important for a company and that can affect business results, the authors of the study note.
And this applies to a wide variety of areas of the economy. Here are just a few examples of industries that are using Data Science to solve their problems:
-online trade and entertainment services: recommendation systems for users;
-health care: prognosis of diseases and recommendations for maintaining health;
-logistics: planning and optimization of delivery routes;
-digital advertising: automated content placement and targeting;
-finance: scoring, detection and prevention of fraud;
-industry: predictive analytics for planning repairs and production;
– real estate: search and offer of the most suitable objects for the buyer;
-public administration: forecasting employment and economic situation, combating crime;
-sport: selection of promising players and development of game strategies.
And this is just the shortest and most cursory list of Data Science uses. The number of different cases using “data science” is growing exponentially every year.
Every Internet user and just a consumer every day, dozens of times come across products and solutions that use Data Science tools. For example, the audio service Spotify uses them to better tailor tracks for users according to their preferences. The same can be said for offering movies and series on video streaming like Netflix. And at Uber, data science is seen as a tool for predictive analytics, forecasting demand, and improving and automating all products and customer experiences.
To work with data, data scientists use a whole range of tools – statistical modeling packages, various databases, and special software. But, most importantly, they use artificial intelligence technologies and create machine learning models (neural networks) that help businesses analyze information, draw conclusions and predict the future.
If you are doing an IT startup without using Data Science, most likely it is outdated already at the level of an idea. Basically, the same goes for fintech, medicine, retail, logistics, entertainment web or mobile services. One of the key ways a data scientist can benefit a startup is by creating data products that can be used to improve products. Moving from model training to model deployment means learning a whole new set of tools for building production systems. Rather than just outputting a report or model specification, creating a model means that the data scientist team must maintain operational issues to keep the system running.
There are a lot of data-driven startups around the world. Some of them are based on open data, others on the creation of convenient interfaces for working with the data of companies and individuals, and others on algorithms that help to give any data a new quality.
To provide value to a startup, data scientists must be able to create data products that include the most relevant and new features. This can be done in conjunction with a team of engineers, machine learning development services, or it can be a responsibility that is exclusively related to data science. The top recommendation for startups is to use serverless technologies when building data products to reduce operational costs and enable faster product iterations.