The Fundamentals of Data Science, Career, Skills, Job
"The Most Attracting Job of the 21st Century," says the Harvard Business Review, is "Data Scientist." Isn't this enough to excite you to learn more about data science?
When organizations started dealing with petabytes and exabytes of data, the era of "Big Data" began. Up until 2010, it was tough for businesses to store data. Now that Hadoop and other popular frameworks have solved the problem of how to store data, the focus is on how to use it. And Data Science is a big part of this. Today, data science is growing in many ways, so we should prepare for the future by learning what it is and how we can add value to it.
What is Data Science?
The first thought is, "What is Data Science?" People have different ideas about data science, but at its core, it is about using data to answer questions. This definition isn't too narrow or too broad, which is good because data science isn't too narrow or too broad of a field!
Data science is the study of how to draw conclusions from raw data by using statistics and machine learning.
So briefly, it can be said that Data Science involves:
- Statistics, computer science, mathematics
- Data cleaning and formatting
- Data visualization
Key Pillars of Data Science
Most data scientists come from different educational and work backgrounds. However, they should all be good at, or even great at, four key areas.
- Domain Knowledge
- Math Skills
- Computer Science
- Communication Skills
Pillars of Data Science
Most people think that domain knowledge is not crucial in data science, but it is essential. The primary objective of data science is to find helpful information in the data so that the company's business can benefit from it. If you don't know how the industry works and how you can't make it better, you have nothing to offer this company.
To get the information you need, you must understand how to ask the right questions of the right people so you can get the correct answers. Some business visualization tools, such as Tableau, help you show valuable results or insights in a way that business people can understand, such as through graphs or pie charts.
Linear algebra, multivariable calculus, and the optimization technique are all essential because they help us understand different machine learning algorithms, which are a big part of Data Science.
Statistics and Probability: It is crucial to understand statistics because it is a part of data analysis. Probability is also an essential part of statistics, and it is a must-learn skill for anyone who wants to master machine learning.
Programming Skills: You need to have a good understanding of things like Data structures and Algorithms. Python, R, Java, and Scala are the languages that are used. C++ is also helpful in places where performance is critical.
Relational databases: You need to know how to use databases like SQL or Oracle so you can get the information you need from them when you need it.
Non-Relational Databases: There are many different kinds of non-relational databases, but Cassandra, HBase, MongoDB, CouchDB, Redis, and Dynamo are the ones that are used the most.
Machine learning: is one of the most important parts of data science, and researchers are doing the most work on it right now. Every year, new progress is made in this area. One needs to know at least how supervised and unsupervised learning work. Python and R both have many libraries that can be used to implement these algorithms.
Distributed computing: Handling a lot of data is also one of the most important skills since you can't process this much data on a single system. Apache Hadoop and Spark are the most common tools. The two most essential parts of these tolls are HDFS (Hadoop Distributed File System), which collects data across a distributed file system, and the tolls themselves. Map-reduce is another part. This is how we change the data. Map-reduce can be written in either Java or Python. There are also tools like PIG, HIVE, and others.
It involves both written and spoken language. In a data science project, once the analysis is done and conclusions are drawn, the project needs to be shared with other people. This could be a report you send to your boss or work team. It could also be a blog post. Often, it's a presentation in front of a group of coworkers. No matter what, a data science project always involves sharing what was learned. So, if you want to be a data scientist, you need to be able to talk to people.
Who is a Data Scientist?
So far, we've talked about what data science is and some of its most important parts. Next, let's talk about who a data scientist is. A special report from The Economist says that a data scientist is someone who:
"Who combines the skills of a software programmer, a statistician, and a storyteller/slash-artist to find the gold nuggets hidden in mountains of data?"
But now we need to know what skills a data scientist has. To answer this, let's talk about the well-known Venn diagram. Drew Conway's Venn diagram of data science shows that it is the intersection of three areas: substantive knowledge, hacking skills, and knowledge of math and statistics.
Let's break down what this Venn diagram means. We know that data science is used to answer questions, so first, we need to know enough about the topic we want to ask about to ask the right questions and know what kinds of data we need to answer them. Once we have our question and the relevant data, we know that the types of data that data science works with often need to be cleaned up and formatted in a certain way, which often requires computer programming skills. Lastly, once we have the data, we must look at it, which usually involves math and statistics skills.
Roles and Responsibilities of a Data Scientist:
Management: The Data Scientist has a small role in management. He helps build a base of futuristic and technical skills in the Data and Analytics field so that he or she can help with different planned and ongoing data analytics projects.
Analytics: The Data Scientist is a scientist who plans, implements, and evaluates high-level statistical models and strategies that can be used to solve the most complicated problems in the business. The Data Scientist makes econometric and statistical models to solve problems like projections, classification, clustering, pattern analysis, sampling, simulations, etc.
Strategy/Design: The Data Scientist plays a crucial role in developing new ways to understand consumer trends and business management and solve challenging business problems, like figuring out how to get the most out of product fulfilment and total profit.
Collaboration: The Data Scientist's job is not a solitary one. In this position, he works with more experienced data scientists to explain problems and findings to the right people so that business performance and decision-making can be improved.
Knowledge: The Data Scientist also explores different technologies and tools to develop new business insights based on data as quickly as possible. In this situation, the Data Scientist also takes the initiative to evaluate and use new and improved data science methods for the business, which he sends to senior management for approval.
Other Duties: A Data Scientist also does tasks related to their job and tasks that the Senior Data Scientist, Head of Data Science, Chief Data Officer, or Employer gives them.
Difference between Data Scientist, Data Analyst, and Data Engineer:
The top three jobs in data science are Data Scientist, Data Engineer, and Data Analyst. So, let's figure out who a data scientist is by looking at what other jobs are like it.
Some Inspiring Data Scientists
When you look at examples of data scientists, you can see how data science is used in many different areas.
Hilary Mason is a co-founder of the machine learning company FastForward Labs, which was recently bought by the data science company Cloudera. She works at Accel as a Data Scientist. She mainly uses data to find answers to questions about mining the web and learning how people talk to each other through social media.
Nate Silver is one of the world's most famous data scientists and statisticians. He started the website FiveThirtyEight. FiveThirtyEight is a website that uses statistics to tell exciting stories about elections, politics, sports, science, and everyday life. He uses a lot of public data to make predictions about many different things. His most famous predictions are about who will win the U.S. elections, and he has an outstanding track record of being right about them.
Daryl Morey is the general manager at Houston Rockets, an American basketball team. The fact that he had a bachelor's degree in computer science and an M.B.A. from M.I.T. helped him get the job as a general manager.
Why do we need data science?
Data science has grown so quickly in recent years because there is a massive amount of data available and being made all the time. A lot of information is being collected about the world and our lives, and at the same time, cheap computers are becoming more common. This has made it so that we have a lot of data and the tools to look at it. Computer memory is getting bigger, the software is getting better, processors are getting smarter, and now there are more data scientists who know how to use all of this to answer questions using data!
Why do data science?
When it comes to employment opportunities, data scientists are in high demand. "Data Scientist" was the third fastest-growing position in 2020, with annual growth of 37%, according to LinkedIn's U.S. Emerging Jobs Report. This industry has topped every "Emerging Job" ranking for the past three years.
Glassdoor also ranked the 50 best occupations in the United States. Data Scientist is projected to be the third-best job in the United States in 2020 regarding work satisfaction (4.0/5), compensation ($107,801), and demand.
As a result, if you're interested in data science, now is a fantastic moment to enter the field. Now there is an increasing demand for data scientists because of the proliferation of data and the improvement in the methods for gathering, storing, and analyzing it. This demand is not limited to the commercial and academic sectors.
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