How to choose a reliable Data Science analytics provider?


Online stores, banks, streaming services and many other companies collect information about the behavior of their users. To find patterns in this data and use them to solve specific business problems, it is necessary to build more than one model and test dozens of hypotheses. These are the main cases where this can be used.

Data Science is an effective instrument of working with data. In the sphere of IT world it has proven efficiency. It can really change the whole world of programming and business. In fact, data science company is already working every day and changing it. Many startups in the field of big data and artificial intelligence are already under influence.

Data Science uses work with data, and it finds the necessary solutions, which are guided by statistical methods. These solutions nowadays can be used even in medicine to make new diagnoses more accurately using data on diagnoses already made, in online stores to recommend products, make promotions and plan purchases, and even in the operation of unmanned vehicles - neural networks recognize the road, markings and obstacles.

Modern data science falls into two broad categories: DATA ANALYSIS and MACHINE LEARNING. Analyzing data includes the tasks of finding patterns in order to use it to answer business queries. An example of such a task: to find out which product and at what time they buy more, whether there are seasonal surges of interest. Machine learning, unlike analysis, does not provide answers to questions, but it helps to make automatic predictions, for example, to predict airfare prices, taking into account the country of departure and arrival, date and time of departure, and other factors. Classic machine learning tasks: face and speech recognition, recommendations, search.

Three Ways to Use Data Science Tools at Work

1. Automate processes


Even in interesting analytical work, problems of the same type are encountered. If you need to repeat a set of actions, Excel will not help: it does not allow you to automate such processes (if you do not take into account VBA). And I prefer to delegate routine work to a computer. This is where Data Science tools come in handy. Any of them includes an algorithm that describes the entire process of working with data in stages. One part of the algorithm is responsible for loading data, another for the first stage of processing, the third for analysis, and so on. Data Science tools let you create a sequential list of tasks you need to perform to analyze your data, and then customize and repeat individual items or the entire process.

2. Work with Big Data

Sometimes these words mean terabytes of information that is not structured in any way or is created in real-timereal time. This data requires distributed storage and computation, as well as specific skills to operate. In other cases, big data is anany array of information that does not fit into Excel. That is, all tables with more than one million rows. We regularly come across such volumes at work. How to be in this case? We can say that Excel does not pull and the help of an expert is required, but it is expensive and time-consumingtime consuming. Another option is to find a clever shortcut that will allow you to solve the problem without analyzing the entire amount of data. For example, analyze a separate sample or refer to other information resources. But this is not always the case. Besides, there is a danger of missing something. And the third option is to apply the skills of Data Science.

3. Build complex models

Advanced Machine Learning Models help you find insights deeply hidden in your data. They cannot be detected by logical analysis or linear regression. Excel doesn't even remotely have such features. And neural networks or boosting algorithms find such non-obvious patterns that they would never occur to a person. At the same time, the found insights are quite applicable in business.

These are just three categories of tasks that Data Science tools perform better than conventional techniques.

Let's apply the result

Data Scientist often solves common tasks typical for any business: analyze customer behavior, attract and retain a customer, predict demand, build a recommendation system, and launch an effective promotion. But there are also specific tasks: the bank wants to predict the probability of loan repayment, the call center wants to automate answers to frequently asked questions.

Data Scientist helps with this too. It also happens that Data Scientist does not solve a specific problem, but analyzes the current situation and looks for growth areas for the company.

Leave your vote