In this article, we will discuss data science technologies and review data science projects.
What skills do data science specialists need?
Information is the main asset in the modern world. The amount of data is constantly growing and the ability to work effectively with them; companies and organizations gain additional benefits. The main requirements for working with data – are to ensure their receipt, processing, storage, protection, and availability of the specified parameters, at the time required to perform tasks.
Machine learning algorithms have been developing rapidly in recent years. More and more areas of human activity are becoming tied to the opportunities provided by Data Science. But widespread naturally entails increased requirements for professionals and the results of their work. Forecasts should be as accurate as possible, programs – perfect. We have produced more data in the past ten years than we have done before. Therefore, we need to analyze this data and find patterns using Machine Learning methods. Specialists in this field, data analysts, work with large amounts of data, extracting useful information from them.
Data science combines several related disciplines. These are programming, mathematics and statistics, business analytics, and machine learning.
A beginner Data Scientist should have 4 basic skills:
- Programming, which includes: Python (basics, data structures, and understanding of REST); Git; SQL;
- mathematics is at the heart of all ML algorithms, in order to learn ML with understanding;
- in Machine Learning and Deep Learning, you need to understand the basic models and algorithms, what tasks they solve, and also the libraries and frameworks that are used for this;
- Docker basics, docker-compose, and a little about CI / CD, for example, Travis or Gitlab CI.
Five basic steps in working with data
- Search for channels where you can collect data, and the choice of methods for obtaining them.
- Validation, leveling of anomalies that do not affect the result and interfere with further analysis.
- Studying data, confirming assumptions.
- Presentation of information in an understandable form: graphs, diagrams.
- Data-driven decision-making. For example, changing the marketing strategy, increasing the company’s budget.
Data science projects to improve your skills
Projects of Data science play a significant role in today`s programming world. The specialists of this area mostly use the following kinds of projects:
- visualization projects
- research Data Analysis Projects
- forecasting modeling
Advanced visualization – the most visual representation of data using various interactive images, diagrams, and graphs (instead of traditional tables). Modeling, forecasting, and research of data – these tools are designed to help the company classify data, form their own nominal and quantitative scales, as well as use for their analysis a developed mathematical apparatus.
Data Science contains a significant experimental component, for which it is necessary to have both the subject area of analysis and software tools for working with data.