Is it possible to dive into the world of data by mastering Data Science on your own from scratch? Spoiler: yes. In this article, we, together with the GeekUniversity Faculty of Artificial Intelligence, will talk about the skills and disciplines that need to be mastered on the way to a Data Scientist career and what to consider when you want to hire big data experts.
What does Data Scientist do?
Training should be based on the tasks assigned to the specialist. At the same time, tasks may differ depending on the field of activity of the company. Here are some examples:
- detection of anomalies – for example, non-standard actions with a bank card, fraud;
- analysis and forecasting – performance indicators, quality of advertising campaigns;
- scoring and grading systems – processing large amounts for making decisions, for example, on granting a loan;
- basic interaction with the client – automatic replies in chats, voice assistants, sorting letters into folders.
But for any of the above tasks, you always need to follow approximately the same steps:
- collection – search for sources and methods of obtaining information, as well as the collection process itself.
- Checking – validation, removal of anomalies.
- Analysis – the study, making assumptions, conclusions.
- Visualization – bringing data into a human-readable form (graphs and diagrams).
The result is making decisions based on the analyzed data, for example, about changing the marketing strategy or increasing the budget for any of the company’s activities.
What do you need to know?
Despite the fact that you need to know quite a lot, there are now a huge number of online courses and books that will help you get the skills you need much faster.
- Statistics, mathematics, linear algebra
You will need to study a fundamental course in probability theory, calculus, linear algebra, and mathematical statistics. Mathematical knowledge is important in order to be able to analyze the results of applying data processing algorithms.
To master it from scratch, the first step is to learn three main areas of machine learning:
- Supervised Learning
Allows you to predict the result using pre-marked data. If you need to predict several values (for example, distinguish photographs of cars from airplanes and trains), then this is a classification problem, if one (say, assume the price of an apartment depending on its characteristics) is a regression problem.
- Unsupervised learning
Here, the input data is not marked up, that is, neither the result nor the method of data processing is known in advance. An example is the search for anomalies – unusual credit card transactions, erroneous sensor readings, and the like.
What do you need to be able to do?
- Program in Python
Knowing the basics of programming is a big advantage. But this is a rather large and complex area, and to make it a little easier to learn it, you can focus on one language. Python is ideal for beginners – it has a relatively simple syntax, is feature rich, and is often used to manipulate data.
- Collect data
It is an important analytical process for data science consultants. It allows you to find hidden patterns in order to obtain previously unknown useful information necessary for making any decisions. This also includes data visualization – presenting information in an understandable graphical form.
