Today, the value of data is revolutionary in the modern digital economy. The statistical methods and the discovery of patterns in large data sets play a significant role in discovering hidden information and unknown relationships between data. Based on this new information, companies and organizations can innovate with the help of Data Science. It includes developing new products/services, management information, and increasing services for the customer.
Modern organizations are transforming their organization into a data-driven organization to optimize the business and operational processes. This change requires a type of employee with knowledge of data-intensive technologies. It is defined as the Data Scientist. The Data Scientist finds and interprets data, manages large amounts of data, and merges this data. The Data Scientist provides consistent data and creates visualizations to help others understand the data. Besides, he/she builds mathematical models, presents and communicates data insights and findings to specialists and colleagues. Besides, a Data Scientist naturally advises on solutions for applying the data.
What skills do I need to become a data scientist?
Completed education in data science is not the only indication that someone is suitable for a job or analytics project. Software maker Tableau identified five skills that are extremely useful in a data culture. See them as indicators of someone’s suitability to be hired or trained to be a data professional.
● Critical thinking
A critical thinker does not accept everything indiscriminately, nor does he question everything he hears or sees. Critical thinking can objectively observe and analyze issues, hypotheses, and results and form an opinion based on them. So regardless of your feelings or personal situation.
Critical thinkers look at a problem from multiple angles and know which questions need to be answered to arrive at a possible solution. They also consider the sources of the data. They distinguish essential from side issues and relevant from irrelevant information.
Critical thinking is of great value in many positions, but especially when working with data. After all, in that field, questions must be formulated, which contribute to the business objectives. For example: what is the next step for the organization? What questions should then be asked? And which data is relevant in finding useful answers?
● Communicate effectively
The questions analyze, and results are worthless if they are not shared within the organization. Effective communication is essential for people who work with materials that others find difficult or uninteresting. It is precious when someone can clearly explain which insights the data has led to, what the relevance is for the organization and its relation to the business strategy. It must be done for different audiences, whose knowledge of technology varies greatly.
Tableau says effective communication is decisive in increasing the data skills of an organization. When a company wants to create or develop a data culture, everyone who works with data is also an ambassador and a spokesperson. Effective means being able to tell a story that makes results understandable. A pleasant way of presenting, distinguishing between what is fun to tell and what is good to hear. Nevertheless, also telling a complete story and not shy away from the disadvantages.
● Problem-solving way of thinking
Some people think in terms of problems, others in solutions. This last skill is useful for data professionals: having fun understanding issues and looking for the matter’s heart like a detective.
Sometimes, however, wanting to solve problems is an inner urge that is difficult to control. Working in a problem-solving manner also requires making choices: which problems are for later, which ones need to be tackled now and with which method.
Someone who thinks in a problem-solving way sees opportunities in problems and knows which resources to use to arrive at practical solutions.
● Intellectual curiosity
The word intellectual indicates that curiosity as a skill is professional. Gossip and backbiting are not included. Curiosity as a skill drives people to delve into problems, analyze causes thoroughly, and ask interesting questions to get to the bottom of something. Tableau argues that ‘just enough’ is never enough for successful data professionals. They will keep looking for answers until the bottom stone is up.
People with a healthy curiosity ask questions (and listen for the answers) and often show interest in a wide variety of topics.
● Business insight
It has already been mentioned under critical thinking: results of data analysis must contribute to business goals. Someone with business acumen understands how a company functions and what an organization needs. Having business acumen means knowing what problems to solve so the business benefits. Think of finding the cause of a persistent problem, fine-tuning a market segment, and predicting sales trends.
● Sheep with five legs
Those looking for people who have all these skills equally have a long and frustrating journey ahead. After all, not everyone needs the same level of data skills for day-to-day work. A data scientist does not have to be an excellent communicator if a colleague links his work and the organization. And business acumen counts more heavily for those who formulate assignments for analytics than for an executive analyst.
Activities required for Data Scientist
You have already read a bit about a data scientist’s activities, but in this chapter, we will go into that a little more deeply.
As a data scientist, you can work at (large) organizations or companies. There you will work with Big Data to predict the future based on specific patterns in the data. If you can predict the future well, a company or organization will benefit significantly from it. A company or organization makes decisions based on these future predictions. A data scientist has a vital role in this.
Companies and organizations will also expect that as a data scientist, you know about Machine Learning. You can get challenging assignments to make computers smarter. To do that, you write algorithms that make that possible.
A good example is an app that predicts when a patient will become ill. This prediction can be made by analyzing previous patient information. This analysis is automatic by the algorithm that a data scientist has made.
Some other activities of a data scientist are:
● Identify data quality issues.
● Structuring and cleaning data sets
● Explain complex solutions to a customer
● Report and present conclusions from data
● Combining internal and external datasets
● Apply mathematical techniques
The salary of a data scientist
As a data scientist, you have nothing to complain about money. According to DataJobs, a data scientist earns on average between $ 50,000 and $ 70,000 gross per year. Most data scientists, however, have a university degree in their pocket.
The salary is so good because there is a significant shortage of data scientists. The demand is greater than the supply, and many companies are therefore lagging. Money is not the primary driver for most data scientists, They love their profession and enjoy taking on challenging jobs, There is no challenge. Nowadays, data comes in all shapes and sizes, and there are many interesting issues.
Difference between a data scientist and a data analyst
The activities of a data scientist and a data analyst are very similar. Yet, there are differences. Below the critical differences at a glance:
● A data scientist is more concerned with future predictions, and a data analyst provides meaningful insights from data.
● A data scientist has Machine Learning skills, and a data analyst is (usually) not involved with this.
● A data scientist often works with unstructured data from multiple sources, while a data analyst usually works with structured data from one source.
Become a (Junior) data scientist
You already know what the meaning of a data scientist is, what kind of activities are part of the profession, and that you can earn a high salary as a data scientist.
Have you become enthusiastic, and do you want to become a (junior) data scientist yourself? If so, read on quickly. As a starting data scientist, you will start as a junior data scientist.
To become a junior data scientist, it is advisable to follow a specific training course. For example, Computer Science or Physics at HBO or WO level would be suitable. However, it is not easy to become a data scientist, and the matter can be problematic.
To start quickly, it is wise to first follow courses without spending much money. If you have already started a course and think it is too challenging or not fun, you have already spent a lot of money, and you have lost it. However, according to the highly regarded Harvard Business Review, being a data scientist is the sexiest job of the 21st century. It earns well, and the work is enjoyable and challenging—enough reasons to become a data scientist.
Why is Data Science a Good Course?
Outstanding courses to get acquainted with the programming languages and technologies that a data scientist must master can be found on Innomatics. Through online or offline courses, you can learn the theory and also apply it yourself.
In “Innomatics Institute Demo Class,” you can see an extensive review and description of the platform. You can even try the platform utterly free in Demo. Also, on coursera are great learning opportunities to learn to be with data science and data scientists.
Recommended programming languages and technologies to learn Some programming languages and technologies that are useful to learn for a data scientist are: Python and R. Machine Learning SQL Implement predictive analytics with TensorFlow Hadoop. These are some examples, but there are even more programming languages and technologies that can be very useful for a data scientist. If you are motivated to become a data scientist, it is perfect for your future.
Now the task is to turn that motivation into action. A data scientist is a very cool profession with many possibilities. Also, you earn well. Don’t wait any longer and get started!
Advantages of doing Data science
IT executives know that the big data hype has been around for a while, but few understand how to handle the big data and what value it can represent to their organization. If you still doubt starting your first big data initiative, these are five reasons to take the step now.
1. Manage data better
Many modern data processing platforms allow data scientists to collect, rearrange, and analyze different data types. While it takes some technical know-how to work out how data is collected and stored, many current big data and BI tools allow users to play the driver and work with the data without performing complicated technical operations.
The extra level of abstraction has enabled various usage scenarios in which data in different formats can be successfully collected and used for multiple purposes. Examples are video surveillance during significant events and medical patient files.
2. Make optimal use of the speed, capacity, and scalability of the cloud
Organizations that want to work with vast data sets should consider the use of third-party cloud providers. They can provide both the storage and computing resources needed to process a lot of data in a specific time.
The cloud offers two clear advantages. First of all, it allows companies to analyze large data sets without requiring large hardware investment. Also, hosting a big data platform yourself requires the necessary skills and training. A hosted model partly removes this complexity and enables faster implementation of big data technology. The cloud developers also have a sandbox environment set up to start to test applications immediately.
3. Let end-users visualize data
The BI software market is already reasonably mature, and big data requires new data visualization tools that can capture BI data in easy-to-read graphs, tables, and slideshows. These applications must be able to let users quickly browse through data and manipulate it in real-time. Applications must provide APIs that can connect to external sources for additional data sets.
Usability is another consideration. In addition to the CIO, the CFO, CMO, and other non-IT executives are also looking for ways to make better use of data, so they too need access to charts, infographics, and dashboards. Many BI vendors are working towards a self-service analytics model that puts users in control. It will accelerate product adoption and increase ROI as analytics will no longer be used only by rapporteurs and tech-savvy end users.
4. Company discovers new opportunities
As big data analytics tools mature, more CIOs also realize the benefits of a data-driven organization. Political parties are discovering how to target their target audience better, while social networks learn to sell ads based on users’ interests. New research into customer behavior within retail ensures that (online) stores use offers in new ways and advertise products differently.
5. Methods to analyze data are evolving
Data is no longer about simple numbers in a database. Text, audio, and video can also provide insights; advanced tools can discover patterns based on specified criteria. Widely used with processing tools capable of text mining, sentiment analysis, language analysis, and entity recognition. This call center tool can match an incoming caller to the correct agent using predictive routing and other analytics technology. It also applies audio analysis based on predefined criteria to determine what score a call gets. These advanced possibilities will only increase in the coming years.