Data science uses artificial intelligence and machine learning to extract meaningful information, answer specific questions or provide management recommendations and advice, improve work performance or avoid big data problems and to predict future patterns and behaviors.
In this article, we will discuss what data science is and what data scientists do and how you can become a data scientist.
|Data science: what are the roles and responsibilities of a data scientist?|
What is Data Science and What Do Data Scientists Do? - How to Become a Data Scientist
What is Data Science?
Data science is an interrelated area that uses scientific methods, algorithms, processes, and systems, in order to understand and analyze actual phenomena with data and extract knowledge and insights from structured and unstructured data.
Data science is a mixture of multidisciplinary fields, primarily focusing on knowledge and understanding of data owned by a particular company or organization and used to solve a problem, answer specific questions or provide management recommendations and advice to improve work or avoid problems.
Data scientists have many skills - including computer science, mathematics, especially algebra, calculus, and statistics, and business knowledge - to analyze data collected from smartphones, sensors, websites, customers, and other sources.
Data science produces insights and reveals trends that companies or organizations can use to make better decisions and create more innovative products and advanced technology services.
Data is the basis of innovation, but its value comes from informational data that scientists have the ability to extract and work on.
A Brief History of Data Science
As a field of expertise, data science is a new science. It originated from the areas of data mining and statistical analysis. Data science emerged in 1996 at a conference in Japan.
Science was first published in 2002 by the International Council for Science (ICSU): Committee on Data for Science and Technology (CODATA). In 2007, the Research Center for Dataology and Data Science was founded in China.
Two researchers at the center had published a paper that defines data science as a new and different class of natural and social sciences.
By 2008, the term data scientists had emerged and the field had begun to take off. At the moment, many colleges and universities have started to offer degrees in data science.
In 2009, Zhu and others defined data science as a new science whose subject is research. There is an agreement that data science differs from existing technologies and sciences today and will be a promising research path in the future.
The Difference between Data Scientists and Data Engineers
Data scientists use computer-based programming languages and techniques.
The data engineer is the one who creates solutions for technical shortcomings in the processing of high-capacity data and speed.
Data engineers prepare the data so that the data scientist can extract useful information from that.
What Do Data Scientists Do?
The demand for qualified data scientists has exceeded the number in recent years. The data scientist ranked the top 50 jobs in America based on metrics such as job satisfaction, number of jobs, and average base salary.
Key responsibilities and duties of the data scientist can include developing strategies for data collection and analysis, preparing data for analysis, exploration, image analysis, and data visualization creating models with data using programming languages such as Python and R, publishing models in applications, using different types of reporting tools to detect patterns, trends, and relationships in data sets.
Data scientists cannot work alone, they usually work in teams to remove big data to obtain information that can be used to predict customer behavior and identify business risks and opportunities.
These teams may include a business analyst identifying the problem, a data engineer preparing and accessing data, and IT engineer overseeing basic processes and infrastructure, and an application developer that publishes models or analytical outputs in applications and products.
These teams are mandated to develop statistical learning models to analyze data, so they must have experience in using statistical tools, as well as the ability to create and evaluate complex predictive models.
What are the Key Steps of a Data Science Project?
Data analysis and action is more iterative than linear, but this is how work typically flows to a data modeling project:
Plan: Identifying the project and its possible outputs.
Setup: Creating a working environment, ensuring that data scientists have the right tools and access to the correct data and other resources such as the ability of computing systems.
Ingestion: Loading data in the work environment.
Exploration: Analyzing, exploring, and visualizing the data.
Modeling: Creating, training, and validating models so that they work as required.
Deployment: Dissemination of models within a production
Who Oversees Data Science Operations?
Data science operations are usually supervised by three types of managers:
Senior IT Managers are responsible for architecture and infrastructure planning that will support data science operations. They continuously monitor processes and use resources to ensure data science teams work efficiently and safely.
IT Managers may also be responsible for creating and updating data science team environments.
Data Science Managers:
Data science managers supervise the data science team and their daily work. Data science managers are team builders whose duties are to produce effective team players who can balance team development with project planning and monitoring.
Business managers work with the data science team to identify the problem and develop an analysis strategy. They may represent the front line of the business such as finance, marketing and sales and they have a very experienced data science team who periodically report existing conditions.
Business managers work closely with the IT manager and data scientist to ensure the delivery of projects.
How Can You Become a Data Scientist?
|How can you become a data scientist?|
How to Become a Data Scientist in One Step?
A data scientist is a professional person making models using data, no one should give you permission to do so. To start with, choose a small project, such as placing the lengths of your house plants in the chart.
Build a model to predict when you arrive to work depending on the weather or a model to estimate the probability that your team will win the tournament.
It doesn't matter what to choose. It doesn't matter if your steps are 100% honest. It doesn't matter if the results are interesting. Just do something.
Don't worry if you don't know how to do all the parts of your chosen project. In fact, it's better to have chosen something you don't know how to do. If you decide to make an app that recommends the best coffee shop based on your location, try making it yourself.
Just diving into the problem will prompt you to learn what you need to finish the project, not afraid of learning a new language or machine learning algorithm or even to write your own algorithm.
Remember, learning is not only done in the traditional sense and classroom. Just learn what you need, and be good enough to build your project and always remember, your project will tell you what you need to learn.
Build an application that you are satisfied with, an application you can see for your parents, and after you finish your project, choose another project, and start again.
What Skills Do You Need to Become a Data Scientist?
If you want to become a data scientist, then you must have a passion to understand and analyze user behavior on social media. This passion will help you a lot during your studies. So I encourage you to look for an inspiration that makes you strong while studying.
To become a data scientist, you must acquire the following important skills:
Mathematics, especially algebra, calculus, and statistics:
In the beginning, you just need the basics of algebra and linear algebra as well as differentiation, but for statistics, you need the two branches of statistics: descriptive statistics and deductive statistics, especially probability theory.
The more you delve into mathematics in general and statistics in particular, the better it is you.
Language Programming especially Python & R:
Nowadays, Python is the most common language among data scientists and AI engineers.
As for the R programming language, it contains more than 5000 libraries and is distinguished in data visualization.
But Python is more common these days because it is faster than R and both have strong libraries and a strong developer community.
No one can understand a table with millions of records of data so you need to display big data in a visual image so that you can discover and analyze it.
In this course, you will explore different ways of data presentation.
Machine learning and deep learning:
In this course, you will learn about machine learning and supervised learning versus unsupervised learning as well as deep learning.
It makes no sense to work with data without having a background on working with data management languages such as SQL and NoSQL.
Data Mining and Knowledge Discovery:
If you are working in a field related to telecommunications, you must be conversant in the field of communications, and if you work in the field of agriculture, you need to be conversant with agriculture, soil and so on.
But don't worry; most fields can gain a good knowledge of them in several months.