There are two common misconceptions about Data Science: One is to think of the term as a collection of datasets.
In fact, Data Science is not so much about the accumulation of a large amount of information. But it’s about the ability to choose the best solution, to derive business benefits from all this volume.
The second misconception is to think of DS as a “magic pill” that can solve all problems. Indeed, when a manager or business owner adds an understanding of Data Science to domain knowledge.
The company gains a competitive edge and the ability to accelerate. So, while DS is not “fairy dust,” it may well be the fuel that will help the company move forward quickly.
What Do You Need to Remember?
The first thing that comes to mind as the definition of data science consulting is data. And usually, this phrase is understood simply as large amounts of data – Big Data.
However, there is a difference between these two concepts. Big Data talks about the ability to collect and use data arrays. And Data Science is about how to analyze this information in order to extract value from the big data stream for a company or project
Today Data Science is one of the three hype concepts in the IT field (and not only in IT!). Two other popular words are AI (Artificial Intelligence) and ML (Machine Learning):
1. AI – Artificial Intelligence, or artificial intelligence. The term has many official descriptions: only on Russian Wikipedia there are about 6-7 definitions.
In simple terms, AI is a field of computer science that makes systems more intelligent. Programs acquire skills that were only available to humans 10-15 years ago. For example recognition and processing of texts, voices, pictures.
2. ML – Machine Learning, or machine learning. A large subgroup of AI creates algorithms for the self-learning of intelligent systems.
The global goal of ML is to teach the system to solve complex problems. It recommends music, books, or films, performs the duties of a voice assistant, drives a car, decides how risky it is to issue a loan to a client.
3. DS – Data Science, or the science of data analysis. Gives meaning to the collected data, generates ideas, and makes decisions based on the processed information.
The goal of Data Science is to capture the volume of data in order to extract business value. DS overlaps with ML and AI because they can have the same tools.
Why There is So Much Talk About Data Science?
Specialists believe that creating artificial intelligence can be equated with the invention of electricity. AI is a super-tool that allows specific businesses and civilizations to leap forward.
Is Data Science Really Indispensable, And What is So Attractive About it?
Many developers are fascinated by the creation of applications that go beyond the usual programming and logic: “if-then-then.” In Machine Learning, you can write a program that will not only be faster but in some aspects even “smarter” than a person.
Even non-IT businesses generate large amounts of data. With the help of Data Science, this data can be analyzed – and competitive advantage can be gained.
Companies are willing to pay more managers and programmers with ML and DS understanding than ordinary specialists.
Data scientists should not be so much an engineer as a data scientist, so a Business Analyst with basic expertise in DS can apply for this role.
Data science applications:
- E-Commerce: how to calculate profitability, how to keep people on the site, what products to offer to visitors and customers.
- AdTech (Advertising technology): advertising technologies, personalized marketing, evaluation of the quality of advertising campaigns.
- Chatbots based on Machine Learning: basic communication with users (classification of requests, responses in chats, speech recognition, text processing).
In fact, 95, if not 99% of people have already experienced Machine Learning or Data Science, for example, when ordering something, choosing a movie on Netflix, or using Google services.
To get into a serious AI project, you will need the knowledge to manage a DS-team or experience in creating an MVP DS-project, but you should always start with an understanding of approaches and terminology.
In general, a manager does not need to become a Data Scientist or get better results in Kaggle (professional competition in data science) in order to bring more value to a project or company.
You need a thoughtful independent study of the topic or a structured course that will provide basic practical knowledge of Data Science and Machine Learning.