Since computers first came to existence they have been generating data, tons of data, and as the world entered the era of data the need to extract useful information from it also grew. But in order to extract and process data, we first needed to store it in storage and that task had its own obstacles. Well later by the year 2010, computer engineers had established a good framework successfully to solve the problem. After that, came the real task of using that data, processing it, and extracting important information from it. Data science can be used for various things but such as artificial intelligence but its main task is to develop programs using different algorithms and using that program to process tons of data and extract information from there.
Data science can be a really big help for your business and its future and if you are reading this blog A complete guide of data science -Tech Pathway blog then you are probably already familiar with the idea. Welcome, to help you better understand the basics of data science we have brought to you A complete guide of data science -Tech Pathway blog.
Generally speaking, Data science is not a single topic or subject, rather it’s a combination of different approaches and techniques that data scientists use to process data, extract useful information out of it, and use that information to find the best available solution. Before data science came this process was done by mathematicians and statistics specialists but later with the advancement of technology data experts started using artificial intelligence and machine learning in order to achieve optimization and computer science as a method for processing and analyzing data. Once they realized the power of automation and machine learning, data science became very famous and is currently used widely by almost every data scientist.
In other words, we can also say that the popularity of Data Science lies in the fact it encompasses the collection of large arrays of structured and unstructured data and their conversion into a human-readable format, including visualization, works with statistics and analytical methods such as machine and deep learning, predictive models, and probability analysis, neural networks and their application for solving actual problems.
Those who are a little bit familiar with data science often hear terms such as artificial intelligence, machine learning, data science, big data, deep learning, and data science. Well, all of these terms are somehow a part of data science that needed to be fully understood in order to learn about Data Science.
Machine learning is a powerful creating tool for processing and extracting knowledge from the data. In other words, ML models can be trained on data independently.
In simpler words, Machine learning uses data to know about the situation and ask for what it should do. Once we have put a response it uses that response and repeats it whenever a similar situation arrives.
Deep learning is the creation of multi-layer neural networks in areas where more advanced as well as fast analysis is needed and traditional machine learning cannot cope. Different layers of multiple machine learning networks work as multi-layer neural networks and the “Depth” of that conduct mathematical calculations.
In simpler words, when we create so many layers of machine learning about how a machine should respond in a situation it creates a layer of artificial neurons and those neurons start adapting and responding to new problems as they come.
Artificial Intelligence is the combination of deep learning and machine learning that focus on making machines learn human’s problem behavior and evolve them.
In simpler words, we can say that Artificial intelligence refers to when a machine, as well as computers, learn to solve problems on their own same as humans. The invention and first use of AI can date back to 1936 when Alan Turing built the world’s first AI-powered machines. But despite quite a long history, today AI in most areas is not yet fully able to completely replace human beings.
Big data can be referred to as a field of ways that treat, analyze, and systematically extract the information from a big set of data that cannot be processed using traditional data processing methods.
In simpler words, we can say that working with the large set of unstructured data sets can be done using big data approaches.
Now when you are all familiar with machine learning, deep learning, as well as big data it’s time to get familiar with data science. Data science is the process of extracting data, processing it, and then extracting important. Knowledge out of it in order to make future decisions about the business using the combined approach of deep learning, machine learning, and big data.
Now let’s take a look at what data scientist actually do
- Detection of anomalies such as abnormal behaviour of customers, frauds, etc.
- Recommendation system for customers as well as personalized marketing such as email newsletters, retargeting customers, etc.
- Metric forecasts about business growth, market demand, inflammation, and other important details.
- Processing large pools of data and helping businesses to make a decision based on the results for example granting a loan of a person based on previous behaviour.
- Developing a Chatbot based on previous interaction’s experience with the customer.
How Data scientists achieve these results
- First, they collect all the data they can gather slightly related to the task.
- Second, they check the data in order to look for anomalies and unrelated data.
- Third, they will process the data and study the results.
- After that, all the results are convert into models of graphs and diagrams in order to represent them in front of the decision-making team.
- Once the results in visualization form are present in front of the decision-making team they will analyse the results and make decisions based on them.
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