Resonance on Social Media
“Enroll in this Data Science Course and earn $1000, every single month”.
You must have seen posts like that on social media. All of us daily see new paid promotion on social media, for Data Science jobs or Data Science online courses.
Social media has remarkably helped Data Science to grab the attention of the audience from every age group and profession, throughout the world. Computer science students, adults, computer geeks, and even major companies, etc.
However, if you go a couple of years back in the past, these kind of posts, were nowhere to be seen. Data Science seems to be a stone hurled from the sky, though it certainly is not. Data Science has been there since, initial periods of Computer and Internet. However, the need for more data science professionals has dramatically increased in the last few years. Experts are predicting that it would boost up more in the coming years.
People could not have been familiar with terms like Big Data, Data Analytics, Data Scientists, Machine Learning and Artificial Intelligence, were it not for movies and social media. Yet the public is confused, and have little or zero ideas of what these terms actually mean? They cannot wait to get their questions answered. Everyone wants a handsome amount of salary, which is offered in most data science jobs. But it would be totally unwise to make the decision just because of the heavy salary.
Some Common Questions
What is the motivation level of big organizations, when it comes to hiring Data Science professionals? As high as social media posts depict or different.
How much of reality is there in these profound claims of Social Media?
Is Data Science simple enough for everyone to master?
What is the career path of Data Scientist?
What is the nature of the link between Data Science, Machine Learning and Artificial Intelligence?
Are they even linked in any context?
If I want to be a Data Scientist, what skills should I learn?
Where do I have to start?
But the most common question is what the headline of Article says.
“Is Data Science My Cup of Tea?”
Well, it is obvious that the answer to this question is very dependent on one’s own mindset and persona. Not every mind is made to be an engineer. Not every heartbeat to learn Medical Science. Not every personality is supposed to be appropriate for accounting. It is the case with data science, as well.
However, unfortunately, many people are not sure about their aptitude and temperament. They will like to make sure Data Science is their cup of tea before diving in, fearing the wastage of time, energy and money.
So we will talk about different aspects of Data Science in detail, in this post. Our target is to make our readers clear about Data Science, misconceptions about it, its limitations and scope of it are present and future. The mission of this article is to help the reader make up his mind and figure out whether Data Science is his/her cup of tea or not.
Monster of data
Data is everywhere in the world. The volume of this data is continuously growing and multiplying with every passing day. Internet is experiencing massive data growth, be it any kind of data, audio, video, etc. According to Statistics, digital data doubles up every two years. This mammoth of data can lead to drastic changes in society. According to experts, we are at the brink of the data revolution. By the year 2020, every human being on planet earth will be creating and saving about 1.7 Megabytes if new information, every new second.
Data Science and its Applications
The simplest definition is, “To use data creatively and generate business value”.
Data Science is a field that comprises of everything that is related to data cleansing, preparation, and analysis. It may deal with both unstructured and structured data.
Bundles of tactics used when extracting insights and information from data is called Data Science.
It is the scientific blend of multiple trades, such as statistics, mathematics, programming, and problem-solving. It is an art of capturing data in ingenious ways, the ability to look at things differently, and the activity of cleansing, preparing and aligning the data.
Google and other search engines use data science algorithms to deliver the best results for search queries. Data Science is also applicable to digital marketing. Ranging from displaying banners to digital billboards, it provides services in many aspects. That is how digital ads get more CTR than traditional ads.
Big Data and its Applications
The term of big data specifies gigantic volumes of data which traditional applications are not capable of handling. The processing starts with raw and un-aggregated data, which cannot even be stored in a single computer. This buzzword actually refers to the flood of information that overflows the business every day. Data Science deals with Big Data all the time.
Many organizations use big data for customer analytics, fraud analytics, and operational analytics. These organizations include credit card companies, retail banks, private wealth management advisories, insurance firms, and venture funds. Big data is useful for gaining new subscribers, retaining current customers and analyze customer-generated data.
Data Analytics and its Applications The science of inspecting raw data with intentions of drawing solid conclusions, which can help to make business decisions and policies, is called Data Analytics. It involves applying an algorithmic or mechanical process to derive insights. You may attempt to look for a correlation between data sets. You can see Data Analytics as a subset of Data Science.
The difference between Analyst & Data Scientist:
People often cannot differentiate in these two terms. The following picture can explain the difference.
The job of Data Analyst is usually to explain what is going on by processing history of the data.
The job of a Data Scientist is not limited to exploratory analysis. Moreover, he uses various advanced machine-learning algorithms to identify the occurrence of a particular event in the future. He looks at the data from angles, beyond the imagination of normal people.
The Requisite Skill Set You Need
Now we will talk about the areas where you need to be an expert if you want to enter in Data Science subject. If one or more of these areas are not your cup of tea, then most probably Data Science is not your cup of tea. At the most abstract level possible, you can regard Data Science as a mixture of the following three things.
Let us talk about all of them, one by one.
1. Mathematical Expertise
Ability to view data from a quantitative lens is mandatory. It is the crux of mining data insight and building data product. In data, there are textures, dimensions, and correlations that are mathematically expressible. Finding solutions through the utilization of data is no less than a brainteaser.
Although statistics is not only mathematical discipline, you need to learn. There will be times when you will have to deal with Linear Algebra because some inferential techniques and machine learning algorithms stem from it. It will be very handy to have an overall strong mathematical background.
2. Technology/Hacking Skills
The term hacking does not imply the science of breaking into computer systems, here. We are just talking about technical computer science skills, which more specifically relates to computer programming skills. In programmer culture, hacking is used for these meanings. You should know how to solve problems in a clever manner.
A data scientist has to utilize technology for wrangling enormous data sets and working with complex algorithms. Therefore, he needs tools far more sophisticated as compared to Excel. Now we will talk about coding languages, you should be familiar with if you have made your mind to pursue Data Science as your career.
- R Programming- This language is specifically designed for Data Science needs. 43% of data scientists use R language for problem-solving. Be careful, as R comes with a steep learning curve.
- Python- One of the most common coding languages required in Data science jobs. Other languages are Java, Perl, C, and C++. However, Python is the major programming language, you should know. It is so versatile that you can use it in all the steps involved in common, Data Science processes.
- Hadoop Platform- Not heavily required, but one of the preferences in many cases. It turns out to be very useful, when the volume of data you are supposed to store, exceeds the memory limits of your system. Hadoop can quickly convey data to various points on the system.
- SQL/Database Coding- SQL is a very basic database programming language, for operations like add, delete, store, select. You can also extract data from the database. You can carry out complex analytical functions as well. You are supposed to be proficient in SQL if you are a data scientist.
- Machine Learning & Artificial Intelligence- This is the point where many experts fail to meet the criteria. A number of Data Scientists, who are masters of Machine Learning & Artificial Intelligence, is very small. You can get your competitive edge by honing your machine learning skills. Machine Language is used for making predictions and discovering patterns in data science.
Machine Learning is a very complex branch of Artificial Intelligence that is based on mathematical algorithms and automation.
- Data Visualization & Unstructured Data- A lot of data that does not fit in any table, is called unstructured data. Like it or not, but you have to deal with it. Videos, blog reviews, social media feeds, etc are the relevant examples.
You should also know how to visualize the data via data visualization tools like ggplot, d3.js and Matplottlib, and Tableau. The business produces too much data every day, and converting the complex results into simple information is critical.
3. Business Tactics
Data Scientist also has to be a tactical business consultant. No one else can learn from data, as they can. Their duty is to transform observations into shared knowledge and actively participate in designing business strategies that are powerful enough to solve core business problems. Point is to develop skills of strong storytelling, with complete narrative using data. A solution to the problem is an integral part of the story.
What Mindset Do You Need To Become Data Scientist?
Yeah, personality traits are also, what defines Data Scientist. You need to be a deep thinker with intense intellectual curiosity. Thinking out of the box, asking new questions and defying odds to satisfy curiosities are the integral parts of the Data Scientist mindset. The main motivation for many Data Scientists is not money, but stretching mental muscles and find the truth, which is hidden beneath the surface. Urge to find the unknown, and uncover what is covered is what drives them.
If you are a student, choose a university that offers Data Science degree, or at least classes in data science and analytics. Following are some universities that offer such programs.
- Oklahoma State University
- University of Alabama
- Kennesaw State University
- Southern Methodist University
However, you maybe are an adult and professional and do not have data background but want to shift career. Yeah, it would come with a lot of hardship. Nevertheless, if you have a knack for solving problems, you will ultimately do it. SAS Academy for data science is providing you all the tools to become a certified data scientist.
The Final Verdict
We hope, finally you are able to find out the answer to the question, “Is data science my cup of tea?”
We checked out some main terms of data science and their applications. We went through detail of what you need to master to become data scientist, plus what kind of approach you must have. Take your time to make a decision, but once you make a decision make sure to take the action accordingly. Good Luck!