What is Data Science, and what are the skills required for a data scientist?

What is Data Science?

Let's start with some of the common definitions that are doing the rounds some say that Data Science is a powerful new approach for making discoveries from data, others term it as an automated way to analyze enormous amounts of data and extract information from it. 

Still, others refer to it as a new discipline that combines aspects of Statistics, mathematics, programming, and visualization to gain insights. Now that you have looked at some of its definitions let's learn more about data science.

When domain expertise and scientific methods are combined with technology, we get data science which enables one to find solutions for existing problems. 

Each of the components of data science 

Data scientists should also be domain experts as they need to have a passion for data and discover the right patterns in them. Traditionally domain experts like scientists and statisticians collected and analyzed data in a laboratory setup or a controlled environment.

The data was then subject to relevant laws or mathematical and statistical models, to analyze the data set and derive relevant information from it. For instance, they use the models to calculate the mean, median, mode, standard deviation, and so on of a data set. It helped them test their hypothesis or create a new one 

Different types of data analysis an important aspect of data science

• Data analysis can either be descriptive where when studies a data set to explain what happened. 

• Or be predictive where one creates a model based on existing information to predict the outcome and behavior.

• It can also be prescriptive for one to suggest the action to be taken in a given situation using the collected information.

We now have access to tools and techniques that process data and extract the information we need.

Data Processing Tools

For instance, there are data processing tools for data wrangling.

Programming Languages

We have new and flexible programming languages that are more efficient and easier to use.

Operating Systems

With the creation of operating systems that support multiple OS platforms, it's now easier to integrate systems and process Big Data.

Application designs

Application designs and extensive software libraries helped develop more robust scalable and data-driven applications. 

Data scientists use these technologies to build data models and run them in an automated fashion to predict the outcome efficiently this is called machine learning. 

Which helps provide insights into the underlying data they can also use data science technology to manipulate data extract information from it, and use it to build tools, applications, and services.

But technological skills and domain expertise alone without the right mathematical and statistical knowledge might lead data scientists to find incorrect patterns and convey the wrong information.

What a data scientist does?

Data scientists start with a question or a business problem, then they use data acquisition to collect data sets from the real world. The process of data wrangling is implemented with data tools and modern technologies that include -

• Data cleansing 

• data manipulation

• data discovery

• data pattern identification 

The next step is to create and train models for machine learning

They then design mathematical or statistical models, after designing a data model it's represented using data visualization techniques.

The next task is to prepare a data report, after the report is prepared they finally create data products and services.

Data scientist skills required 

1. Ask the right questions

Data scientists should ask the right questions for which they need domain expertise the curiosity to learn and create concepts, and the ability to communicate questions effectively to domain experts.

2. Understand data structures

Data scientists should think analytically to understand the hidden patterns in a data structure.

3. Interpret and Wangle Data

They should wrangle the data by removing redundant and irrelevant data collected from various sources.

4. Apply statistical and mathematical methods

Statistical thinking and the ability to apply mathematical methods are important traits for a data scientist.

5. Visualize data and communicate and stakeholders

Data should be visualized with graphics and proper storytelling to summarize and communicate the analytical results to the audience.

How to become a data scientist

To get these skills they should follow a distinct road map they must adapt the required tools and techniques like Python and its libraries. They should build projects using real-world data sets that include NYC open data Gap minder and so on, they should also build data-driven applications for digital services and data products. 

Scientists work with different types of datasets for various purposes now that big data is generated every second through different media, the role of data science has become more important so you need to know 

What Big Data is and how you are connected to it to figure out a way to make it work for you?

Every time you record your heartbeat through your phone's biometric sensors, post or tweet on the social network, create any blog or website, switch on your phone's GPS network, upload or view an image, video, or audio, in fact, every time you log into the Internet you are generating data about yourself, your preferences, and your lifestyle.

Big data is a collection of these and a lot more data that the world is constantly creating, in this age of the Internet of Things or IoT big data is a reality and a need. 

Big data is usually referenced by 3Vs

Volume - Volume refers to the enormous amount of data generated from various sources.

Velocity - Big data is also characterized by velocity, huge amounts of data flow at a tremendous speed from different devices sensors, and applications to deal with it an efficient and timely data processing is required.

Variety - Variety is the third V of big data because big data can be categorized into different formats like structured, semi-structured, and unstructured.

Structured data is usually referenced as RDBMS data which can be stored and retrieved easily through SQL.

Semi-structured data are usually in the form of files like XML, JSON documents, and no SQL database.

Text files images videos or multimedia content are examples of unstructured data.

In short big data is a very large information database usually stored on distributed systems or machines popularly referred to as Hadoop clusters, but to be able to use this database we have to find a way to extract the right information and data patterns from it.

That's where data science comes in data science helps to build information-driven enterprises.

• FAQ for Data Science

Q1. What is data science in simple words?

Data science is the field of study that combines domain expertise, statistics, and mathematics to look at real-world problems. It is not the study of how to use data, rather it is a way of creating and analyzing data.

Q2. What is a data science example? 

Some of the most popular examples of data science include Hadoop, Python, R, .NET, SAS, Ruby, Julia, SQL, SPSS, Mahout, Cobol, Apache Spark, Pig, and RDBMS. These are examples of tools that data scientists use to analyze their data and generate insights.

Q3. How to become a data scientist?

To get these skills they should follow a distinct road map they must adapt the required tools and techniques like Python and its libraries. 

They should build projects using real-world data sets that include NYC open data Gap minder and so on, they should also build data-driven applications for digital services and data products.