In today's highly connected world, we are generating more and more data. The rate of occurrence has led to the development of data science and its various tools. Businesses large and small now rely on data science to increase productivity and efficiency.
I also shared this article on Douban, how to quickly find a data science job ~~~
According to the company's indeed one of the top job sites reports, data science needs at home grew by 29% since 2013, an increase of 344%. Looking at current trends, data science role descriptions are also rapidly expanding. People increasingly expect professionals in this field to have machine learning and big data technical skills. Overall, data science has changed the rules of the game in many different areas.
What is a data scientist?
Data scientists are analytical experts who deal with statistics-related data, programming languages, decision-making, artificial intelligence and machine learning . Data scientists use their business expertise, statistical skills and overall intelligence to process data and make appropriate decisions to mitigate threats to business processes.
Data scientists don't need to wait for the data to arrive on their desks (they are the way to go). They search for data and collect, clean up, come up with predictive models and evaluate the business.
What is the difference between a data analyst and a data scientist?
Although the terms " data analyst " and "data scientist" sometimes overlap each other, there are some key differences between the two roles.
- Only valid when data is provided;
- The original data should not be changed; and
- Little or no programming skills.
Data scientist:
- Get the data yourself;
- Can create, modify and utilize original data; and
- Requires excellent programming skills.
Data science is a complex field that includes data analysis, but also includes AI and machine learning.
Still confused? Understand the two roles in our article-Data Scientist and Data Analyst : What is the difference?
Basic skills required for data scientists
To become a data scientist is to learn how to use various tools and programming languages and apply them to solve key problems in business systems. Data analysts should have strong technical skills and self-motivated. Below is a brief guide to the most sought-after skills for data science professionals.
- communication ability
If you decide to pursue a career in data science, you don’t have to worry about being trapped at your desk and unable to complete your daily tasks. This is a dynamic role that requires communication and collaboration with various stakeholders. This kind of process is only possible if you can combine your data science expertise with good interpersonal skills. Individuals in this role should know how to collaborate across functions to deliver successful business models and forecasts.
2. Curiosity
Albert Einstein once said: "My only genius is curiosity." Perhaps the same is true for many data scientists. Innovation only happens when you question something. In data science, even trivial self-questioning can bring insights, which may help you develop a great product or brand. Therefore, it is important to ask questions at all stages of the work to develop a great strategy.
If you want to stand out in the ever-changing world of data science, you must constantly learn new skills and master the latest technology. Cultivate curiosity and motivation to learn.
3. Tell a story
You may not need to develop this skill in order to master data science, but it is a valuable skill.
Storytelling is what makes connections and evokes emotions. Even scripted stories have the ability to create connections and interest the audience. Data scientists don’t need to deal with any falsified stories, but the way they deliver discoveries, state facts and data should involve broader business stories. They need to effectively compress the essence of the data into an easy-to-understand narrative. This will provide other stakeholders with a clear idea to decide which decisions to make. Therefore, the field of data science is full of storytellers.
4. Digital awareness
Raw data contains a lot of numbers, and data science tools break them down into patterns. Therefore, you need to be familiar with mathematics.
In a data science career, you must apply statistical formulas to manipulate data or make decisions from it.
In contrast, data analysts do not need to know a lot of mathematics, they should understand basic statistics, focusing on descriptive statistics and certain probabilities.
5. Logic Master
Data scientists are at the intersection of business, algorithms, infographics, graphs, and data from countless collection points, so they need to be very good at logically testing and validating data. It's almost like connecting dots or puzzles. In a data science career, you need to learn a lot of professional knowledge and still apply basic logic principles.
Technical skills required by data scientists
Data science is a combination of multiple fields (including programming, biostatistics and economics) and various scientific technologies. Let's explore them in more detail to understand the nuances.
Machine learning is a developing sub-professional in the field of data science, so you must let humans have brains to engrave their memories on it. Some repetitive processes are completed with the help of these storage paths. Although the machines are equipped with storage devices, they cannot use them alone to make decisions.
What if you teach a machine to behave like a human? Machine learning is a relatively new phenomenon and represents the answer to this old hypothesis in data science. With the help of machine learning, computers can acquire data by themselves and operate independently.
2. Advanced statistical analysis
Statistics is a branch of mathematics widely used in the field of data science. You can use statistics to solve complex business problems. It cannot be used as a standalone theme, but is used in conjunction with tools (Tableau and Power BI) and programming languages (STATA, R, and Python). If you need to improve your statistical skills, you can get a series of statistical training camps online.
3. Programming Skills
The data scientist must be proficient in programming. Python and R are the two programming languages of choice in the field of data science. Having hands-on experience in these two programming languages can make the task of analyzing and processing data simpler and easier.
Master data skills and climb the corporate ladder
Now that we have introduced the basic skills and technical abilities required to become a data scientist, let us continue to explain how to find a job and climb the corporate ladder.
Enter the field
You can be hired as a data scientist without any experience, but you need to spend some time on a dedicated data science course. That's because it is one of the most senior positions in the company's technical environment. Start your career as a business analyst or data analyst and gradually become a data scientist. This will also enable you to gain important experience and a lot of knowledge.
Internship opportunities
Since many companies cannot afford to hire a large number of full-time employees, it is recommended to intern in an organization of your choice. Most interns will use this opportunity as a stepping stone to full-time work. The internship period provides plenty of opportunities and on-the-job training. It has become the new normal into the corporate world.
Network
In data science, the network is as important as any other discipline. Even if you are actually encountering these connections virtually, going out and discussing data challenges is a key part of your career development.
Maintaining a great LinkedIn profile on your professional achievements and adding a message board will bring you closer to the recruiter's office. Finding a mentor who can help you navigate your career path is also a good thing.
Start your career as a data scientist
As the demand for data scientists continues to grow, those interested in pursuing this meaningful career path need to obtain appropriate learning options to steer their knowledge in the right direction. Excellent data science courses will delve into the origins of the field and assign the most relevant courses to enrich your knowledge base.
For those who are looking for a comprehensive data science course, our data science bootcamp provides accelerated online courses, including courses, mentoring and professional guidance, designed to make you a data science career quickly. If you need to hold the same job at the same time, you can use the exact same rigorous course to study part-time. Contact your consultant immediately to find the best way for you.
Big data analysis services help you jump out of the learning curve. You can click here to enter the original text about how analysis and data science can improve your business efficiency and the big data learning curve.
The full capabilities of any new technology are beyond what any enterprise can achieve without the help of experts. The latest history of Internet-driven technology proves that outsourcing is the key to leveraging cutting-edge data products and services. Analytics and data science are no exception.
Visionary organizations will not struggle to create big data and analytics products for themselves , nor will they start from scratch to form internal departments. Competing organizations are outsourcing their important data needs.
Before these technologies and methods became ubiquitous, big data services, big data products and big data consulting were all full-time jobs, and it was best to be served by professionals. You need to cooperate with a leading big data consulting and service company.
This is where the next generation of data science marketing professionals comes from. You've got the products and services that people want-outstanding data consultants have predictable structural science to keep your brand evolving.
At this point, the real competition is who will access these cutting-edge data services first.
If you need to know more about artificial intelligence, you can enter the " Fangbao Blog " to view related information~
For the full content, please read the original address: