Friday, September 20, 2019

What is Data Science and What Do Data Scientists Do? | How to Become a Data Scientist

Data science is a multidisciplinary scientific field that uses mathematics, statistics, data analysis, information science, machine learning and their related methods, processes, algorithms, and systems, in order to understand and analyze actual phenomena with data and extract knowledge and insights from structured and unstructured data.
A data scientist collects large sets of structured and unstructured data from varying sources, analyzes data for actionable insights, cleans and validates data to ensure accuracy, completeness, standardization, and uniformity.

Data Science
Data Science and Data Scientist: Challenges and Opportunities, What are the main challenges that data scientists face and how to deal with them. 

What is Data Science and What Do Data Scientists Do? 

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Scientists believe that the bulk of the data and intellectual output on earth and throughout history has been achieved only in the past few years, but with the continuous development of science and technology and the immersion of humans in more electronic worlds, we believe that the volume of data and information produced by man has not yet reached its maximum speed.
It is estimated that 90 percent of the world's data has been generated in the past two years. For example, Facebook users upload 10 million images per hour. The number of connected devices in the world - the Internet of Things (IoT) - is expected to increase to more than 75 billion by 2025.
Big Data has become a treasure trove of knowledge that can be used to achieve various gains, but this is not possible without relying on powerful expert systems, and specialists who are able to prospect and analyze this data and extract what benefits and whatnot. 
There is no point in collecting and accumulating data unless there are clear objectives to exploit it.
Without the expertise of professionals who turn state-of-the-art technology into actionable insights, Big Data is better than nothing. Today, many organizations are opening their doors to big data and unlocking their power - raising the value of a data scientist who knows how to manipulate actionable insights from big data.
The wealth of data collected and stored can bring transformational benefits to organizations and communities around the world, but only if we can interpret them. This is where data science comes into play.

What is Data Science?

Data science is an interrelated area that uses scientific methods, algorithms, processes, and systems to extract value from 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.  Data 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 agreement that data science differs from existing technologies and sciences today and will be a promising research path in the future.

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.

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, which is preparing the data so that the data scientist can extract useful information from them.

How Data Science Can Improve Your Business Efficiency?

Many organizations and companies use data science teams to transform data into a competitive advantage by improving products and services. Most companies have made data science a priority and continue to invest heavily in it. For example, logistics companies analyze weather conditions, traffic patterns, and other factors to improve delivery speeds and reduce costs.
Healthcare companies use data science to analyze medical test data and reported symptoms to help doctors diagnose and treat diseases early and more effectively.
IT companies use data science to analyze data collected from call centers to identify potential customers, so the marketing department can take action to retain them.
 According to a Gartner survey, more than 3,000 Chief Information Officer (CIO) is heavily involved in the business model change, in which participant’s ranked professional analytics and information as the best distinctive techniques for their organizations. Many chief executive officers (CEOs) believe that data science tools and technologies are the most strategic for their companies and therefore attract the newest investments.

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:

IT 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. They 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 the effective team players who can balance team development with project planning and monitoring.

Business managers:
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. They work closely with the IT manager and data scientist to ensure the delivery of projects.

The challenges of implementing data science

Data Science
Data Science and Data Scientist: Challenges and Opportunities

What Are the Major Challenges Faced by Data Scientists?
At the moment, data science is expanding its branches all over the world. But it involves a lot of challenges that create many obstacles for data scientists when dealing with data. There are many companies in the world that suffer from inefficient team workflows because different people use different tools and processes that do not work well together. Without better integration and disciplined central management knowledge, executives may not see a full return on their investment. This chaotic environment poses many challenges. Let us discuss some of the major obstacles faced by data scientists.

Data scientists cannot work efficiently. Because access to data must be given by an information technology system administrator, data scientists often wait a long time to get the data and resources they have to analyze that data. Once they arrive, the data science team can analyze the data using different and possibly incompatible tools. For example, a data scientist may develop a model using the python programming language but the application in which it will be used is written in a different language such as R language. This is why it may take weeks or even months to publish templates in useful applications.

IT administrators take a lot of time to support. Due to the proliferation of open-source tools, the IT department has a growing list of tools that need support. For example, the data scientist in marketing may use different tools than the data scientist in finance. Teams may also have different workflows, which means the IT department must constantly re-create and update environments.

Application developers do not have access to use machine learning. Sometimes, there is a need to re-encode machine learning models that developers receive or may not be ready for deployment in applications. Because access points may be inflexible, templates cannot be deployed in all scenarios and leave application developer scalability.

Business managers can be removed from data science. The data science workflow is not integrated into decision-making processes and systems at all times, which creates problems for business managers in intentionally collaborating with data scientists.
Without more disciplined central management skills, it is difficult for business managers to understand why it takes longer from prototype to production, and are often less likely to support investment in projects they consider too slow.

The Emergence of Data Science Platform

Why Do We Need Data Science Platform?
When companies faced challenges while using data science in their business, they realized that without an integrated platform, data science would be ineffective, insecure, and difficult to measure. After considering and understanding the potential benefits of data science and possible challenges data scientists face,, they thought that there should be a platform that would deal with emerging challenges properly and then a data science platform emerged in the business world. Data science platforms are software hubs around which all data science works. They put the whole process of data modeling in the hands of data science teams so that they can focus on gaining insights from the data and communicating them to key business stakeholders. Through central platforms, data scientists can work in a collaborative environment using their favorite open source tools, while synchronizing all their work with a version control system. A good core platform reduces many of the challenges of implementing data science and helps companies turn their data into insights faster and more efficiently.

Benefits of Data Science Platform
The data science platform eliminates bottlenecks in workflows by simplifying management and using open source tools, infrastructure, and frameworks. It also reduces redundancy and stimulates innovation by allowing teams to share code, results, and reports.
For example, the data science platforms may allow data scientists to publish models as an application programming interface (API), making it easier to integrate them into different applications. Using the data science platform, data scientists can access tools, data, and infrastructure without having to wait for the IT department.
The demand for data science platforms in the market has increased. In fact, digital marketing platforms are expected to grow at a compound annual growth rate (CAGR) of more than 39 percent over the next few years and are projected to reach about the US $ 386 billion by 2025.

What Strategies Do Data Scientists Need in the Platform?
If you're ready to explore the possibilities of data science platforms, here are some key possibilities and important strategies to keep in mind:

Prioritize integration and flexibility: Ensure that the platform includes support for the latest open-source tools, including popular version control providers and ensure tight integration with other resources.
Choose a project-based user interface that helps collaborative work: The platform should enable people to work together on models, from conception to final development. Each team member must be given access to data and resources.
Include enterprise-level capabilities: Make sure the platform is able to expand your business as your team grows. The platform must be highly available, have strong access controls, and support a large number of concurrent users.
Make data science as an improved self-service: Look for a platform that reduces the burden of IT and engineering processes and makes it easier for data scientists to accelerate the turnover of environments instantly, track all their work and easily deploy prototypes in the production phase.

How Can You 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.

Data visualization:
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.

Database Management:
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.

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 in 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.

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