Data science includes tools, algorithm and Machine Learning different rules in order to find the hidden pattern in input data. But what is the difference between the work that statistic scientist and data analyst do during recent years?
What is Data science ?
Data science is a very important major in the IT technology that gives us the ability of using data in an organized way. Learning this useful field also gives us this ability to recognize the hidden pattern in the data and do more detailed analysis.
Data science can help us understanding the world around us and making better decision. With learning and using concepts and techniques in data science, we can dominate a smarter and proved world. Finally with growth of data science, It is important to learn continuously and With the advancement of technology and new methods, we should always seek to improve and expand our knowledge and skills in data science.
Data science use Predictive Analytics and Prescriptive Analytics and Machine Learning models for predicting and making decisions. But what each idioms means?
Predictive Analytics helps us to become able to predict the possibility of a special event. For example if you have a company which lends money to its customers, It is important to know that they will repay this loan. For this purpose, you can build a model that can perform predictive analysis on customers' payment history and predict whether they will repay on time or not.
Prescriptive Analytics is a relatively new field that focuses on providing data-driven recommendations. In other words, in addition to predicting probabilities, prescriptive analytics also suggest a range of related actions and outcomes. For example, data collected by vehicles and algorithms can be used to train self-driving cars and make them smarter.
Supervised machine learning can be used to predict future events. For example, machine learning can use a company's transaction data to predict future financial trends or train a model to detect fraud based on fake purchase records.
What is the roadmap of Data science?
In order to learn data science, first step is to know the based rules. We should learn the context like data collection, data cleaning, analyze and extract information from the data and also interpret the results.
The most important step in this way, is that we should know the programming languages and tools related to the data science. Programming languages like Python with well-known libraries like NumPy and Pandas can help us in analyzing the data.
Learning Statistical concepts is a Basic component of data science. We need to be familiar with mean, variance, distributions, correlation coefficients and statistical hypotheses in order to analyze data properly. Also, to evaluate data science models that use machine algorithms, we need to know metrics such as precision, accuracy, and performance characteristic curve.
Next, we have to learn machine algorithms. Algorithms such as linear regression, decision tree, support vector machine (SVM) and neural networks are powerful tools that can help us predict and analyze data.
Data visualization skills are also important skills in data science. We should be able to visualize data in a way that is understandable and interpretable for others through the use of charts, maps and dashboards.
Also, when we want to use data science in a practical way, we need to get to know the related challenges. These challenges may include difficulties in data collection and preprocessing, big data processing, data management, data privacy and security, and correct interpretation of results.
We should know that working with data in SQL is a part of this job. So It is obvious that learning SQL is important.