What is data science?
In short, data science is a combination of data mining and computer science.
Since the invention of the first computer, data has been constantly being produced. Initially, companies relied on data mining, which simply meant generating new information. But in today's environment, websites and applications are more than just brochures, bulletin boards or online information tools. They are now a medium for millions of users to come together and share experiences. Users are now interacting with the site, creating content, comments, likes, research, and more. All of this leads to the creation of a large amount of data, and the company expects to use this data to add more value. Their products.
In 2010, the term “big data” was created for the vast amount of data around us, paving the way for the rise of data science, which can gain insights from a large number of unstructured data sets to support the business. In the present and future, data science is about the collection, analysis and modeling of data. However, the most important part is its application, such as machine learning, which makes it possible to make machines more precise through data-driven methods, and deep learning has become a type of machine learning that changes our daily lives. And the way we experience things.
Data scientist work in industry
- collection: from
The most important job of data scientists is to collect data from a variety of sources.
- Exploration and transformation: from
Structured and unstructured data must be cleaned and transformed to eliminate exceptions in the data.
- analysis: from
This is the core part of the work. Based on the transformed data, data scientists try to understand what the user is doing or what they are looking at and why they left, and then provide a logical solution, such as what can be done to attract more users and provide them with better Experience.
- Learning and optimization: from
A/B testing allows data scientists to experiment on a variety of models and check which models work best.
- Representation and visualization: from
The whole task is not to create advanced models, but to keep them simple in a way that customers and others can understand.
- Artificial intelligence and machine learning: from
This is the last part of data scientists using complex algorithms and machine learning principles to improve the performance of mission-specific machines.
What can you learn from online training in data science?
Data science is the use of statistics, creating code, developing models, and solving problems. To achieve this goal, the focus of the training is to provide students with in-depth training on the following tools:
- Hadoop, MapReduce and Spark are used to process data.
- The SQL programming language is used to program and design database systems.
- Python is the most powerful language in machine learning.
- R and Excel help with analysis and data modeling.
- Other important tools are SAS, Minitab and XL Miner.
Online training covers all of the above important concepts while giving students the opportunity to participate in real-time projects. After the training, placement assistance can also be provided to help students find work in leading companies.