Data is the new oil. The world’s data volume has doubled every two years for the past 20 years. Data has been called “the world’s new natural resource” by Forbes Magazine. McKinsey & Company predicts that, by 2023, data will be worth $10 trillion more than all other global commodities combined. As humans have decoded our genome, we can also decode our digital self with big data analytics tools. The demand forData Researcher is expected to grow at 36% per year through 2020. Data scientist salaries are higher than other IT positions and grow faster than different IT roles. A successful data scientist can earn an average of $150,000 per year.

Qualifications of a Data Researcher

A data researcher is a person who has the knowledge and skills to analyse data and interpret it in a meaningful way. This includes identifying patterns and trends and drawing conclusions from the data. Data researchers are experts in statistics and data analysis. They are responsible for collecting, analysing, and interpreting data to provide insights into the consumer market.

The qualifications for a data researcher are diverse. They can come from various statistics, mathematics, computer science, or any other field that provides the skills necessary to analyse data. A data researcher must have a deep understanding of statistics and mathematics and an understanding of business environments. In addition, data analysts must be able to work with all kinds of data sets and apply different types of statistical models.

The qualifications of a Data Researcher are:

• A Data Researcher must communicate effectively with people who don’t have the same level of knowledge as them.
• They should work well in team settings, collaborating with others and sharing their ideas.
• They should also be creative and imaginative in their problem-solving approach.

Job Role and Responsibilities of a Data Researcher

Data research is an emerging career path getting more attention in recent years. A data researcher can be found in many industries and companies, from startups to large corporations. They are usually responsible for gathering and analysing large amounts of data to find patterns or trends that can help the company grow.

A data researcher’s job role varies depending on the industry they work in and their specific responsibilities within the company they work for. Data researchers are not just IT professionals. They have a wide variety of skill sets and interests to work in diverse fields such as marketing, finance, and the medical sector. They are responsible for finding out the insights from data relevant to their employer’s needs.

Responsibilities of a Data Researcher:

• Data gathering
• Data cleaning
• Data processing
• Data visualisation
• Presentation of findings

Skills Required to Be a Data Researcher

Data Researcher is one of the most sought-after jobs these days. It’s an excellent opportunity to work on technology and data analysis cutting-edge. Data Researcher is a person who can extract data from various sources, analyse it and produce insights. Data researchers need to have a deep understanding of statistics and the ability to think abstractly. They should also be able to communicate clearly in both written and verbal formats.

The skillset required for this job are:

• Ability to work with large data sets

A Data Researcher needs to have the ability to work with large data sets because it is a part of their job. They must extract information from large data sets and then use that information for various purposes. Without this skill, they would not be able to do their job correctly and might not be able to do anything.

Data is a valuable asset for any company.A Data Researcher’s job is to find the hidden insights in these data sets and present them to make it easy for a business decision-maker to act on them.

• Statistical knowledge

Data researchers need to have statistical knowledge to be able to interpret data and understand its meaning of it. They also need a solid understanding of statistics and mathematics to use statistical models and algorithms. Statistical models are used by Data Scientists who want to explore the underlying relationships in the data. In contrast, algorithms are used by Data Analysts who want to make predictions about future events based on past events.

Data is not just a collection of numbers, but it is a set of observations about the world. Statistical knowledge provides insights into how to interpret data, make sense out of it, and draw conclusions from it.

• Good understanding of programming languages

Data Researcher is a new term for people specialising in extracting data from various sources, such as websites, mobile apps, and social media. Data Researchers need to have a good understanding of programming languages to extract data from different sources. In addition, the knowledge of programming languages will allow them to use tools like Python or SQL to manipulate the data they have extracted.

Data Researchers use programming languages for analysing data, extracting features, evaluating models, and generating reports. Statistical techniques are used to model data, and machine learning algorithms are used to make predictions on the model.

• Good analytical skills

Data Researcher is a profession that requires good analytical skills and the ability to work with data. it’s generated from various sources, such as sensors (e.g. weather stations), business processes (e.g., customer transactions), and research studies (e.g., clinical trials). Data scientists extract knowledge and meaning from data through various methods, including statistical analysis, machine learning, pattern recognition, and data visualisation.

A Data Researcher needs to have a critical eye for detail and find the patterns in data that others might not notice. They need to be able to look at large amounts of data and find the crucial elements. They also need to communicate their findings with others in a way that is easy for them to understand.

• Data Extraction

Data extraction is extracting data from a given source such as a database, web page, or spreadsheet. Data extraction is an essential skill for any data researcher to have. It helps to save time and effort when you need to extract data from various sources. 

Data extraction is an essential step in the Data Researcher’s work. It is often required to extract data and prepare it for analysis or extract a subset of data from a larger dataset. This can be done by using SQL queries or programming languages like Python or R.

How and Where to Learn

Statistics is an essential tool for data scientists. It helps them make sense of the data and make decisions based on it. Books are one of the most popular resources for learning statistics. Many excellent books teach statistics more comprehensively than any other resource. However, there might be some downsides to this approach. Books don’t provide you with the opportunity to get feedback on your work. 

Online courses are another popular way of learning statistics for data science professionals. They provide interactive exercises and video lectures that help you learn at your own pace while still being able to interact with a teacher when needed, which is not an option with books alone.

Here are some options for you to consider based on your preferences:

• Self-Guided Websites and Courses
• Books
• Virtual Classes
• Non-Virtual Classes
• Starting Your Projects

LearnVern is an online learning platform that offers free Statistics for Data Science tutorials.  The course will introduce you to the basics of statistics by teaching you how to use programming language. You will learn how to perform different types of statistical analysis, such as linear regression, correlation, and hypothesis testing. It will help you learn how to use statistics for any data science project.

Conclusion

Data is everywhere. It has been used to make predictions, create new products and services, and solve some of the world’s most pressing problems. Data scientists are the people who use data to do these things.

Data scientists are well-known experts in analysing and interpreting large sets of data and presenting them to others. They can work with various tools, including statistical software packages like SPSS or SAS, database management systems like MySQL or Oracle, programming languages such as Python or R, and visualisation tools such as Tableau.

Data science is one of the most sought-after skills in the current job market. As a result, the demand for data scientists and data engineers has increased exponentially over the past few years.