Honing Data Science: 2019

Tuesday, 13 August 2019

data science components

In this post, we will attempt to define data science by looking at something that's really well known in the field, the data science Venn diagram. Now if you want to, you can think of this in terms of what are the ingredients of data science. Well, we're gonna first say thanks to Drew Conway, the guy who came up with this, and if you want to see the original article, you can go to this address, but what Drew said is that data science is made of three things and we can put them as overlapping circles because is the intersection that's important.

The Data Science Venn Diagram

Here on the top left is coding or computer programming or as he calls it, hacking. On the top right is statistics or stats and mathematics or quantitative abilities in general . On the bottom is domain expertise or intimate familiarity with a particular field of practice, business or health or education or something like that.The intersection in the middle is data science. So it is the combination of coding, Statistics, math and domain knowledge.

Now let's say little more about coding. The reason coding is important because it helps you gather and prepare the data. A lot of the data comes from novel sources and is not necessarily ready for you to gather and it can be in very unusual formats. Coding is important because it can require some real creativity to get the data from the sources to put it into your analysis.

Now there are a few kinds of coding that are important. For instance, there's statistical coding. A couple of major languages in this are R and Python - two open-source free programming languages that are specifically for data. Python is a general-purpose programming language, but well adapted to data.

The ability to work with databases is important too. The most common language there is SQL usually pronounced as the sequel, which stands for structured query language because that's where the data is.

components to learn for a data scientist

data science components to learn

Also, there's the command line interface or if you're on a Mac, people just call it the terminal . The most common language there is Bash which actually stands for a Bourne Shell. Searching is important for data scientists and Regex or regular expressions are critical. While there's not a huge amount to learn there, it's a small little field. It's sort of like super-powered wild card searching that makes it possible for you to both find the data and reformat it in ways that are going to be helpful for your analysis.

Now I'll say a few things about the math. You're going to need things like a little bit of probability, Algebra, Regression - a very common statistical procedure. Those things are important and the reason you need the math is because that's going to help you choose the appropriate procedures to answer the question with the data that you have and probably even more importantly, it's going to help you diagnose problems with things.

Sunday, 14 April 2019

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Wednesday, 10 April 2019

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Friday, 5 April 2019

Learning data science topics

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Monday, 1 April 2019

Why learning data science is important

In this post, we will learn about why learning data science is important in today's world. Let's take a look at this article. It says Data scientist: The sexiest job of the 21st century, and please know that this is coming from Harvard Business Review. So this is an authoritative source and it's the official source of this saying that data science is sexy.

Data Science : The Sexiest Job

Again, you may be saying to yourself, sexy! I hardly think so. Well, oh yeah, it's sexy and the reason data science is sexy is that first it has rare qualities and second it has high demand. Let me say a little more about those. The rare qualities are that data science takes unstructured data then finds the order, meaning and value in the data.

Those are important, but they're not easy to come across. Second: high demand. Well, the reason it's in high demand is because data science provides insight into what's going on around you and critically it provides a competitive advantage, which is a huge thing in business settings.

Now let me go back and say a little more about demand. Let's take a look at a few other sources. So for instance, the McKinsey Global Institute published a very well known paper, and let's take a look at the bottom right corner.

Demand for analytics professionals

Two numbers here. I'm going to zoom in on those. The first one is they are projecting a need in the next few years for somewhere between 140,000 and 190,000 deep analytical talent positions. So this means actual practicing data scientists. That's a huge number, but almost 10 times as high as 1.5 million more data-savvy managers will be needed to take full advantage of big data in the United States.

Now that's people who aren't necessarily doing the analysis but have to understand it, who have to speak data. And that's one of the main purposes of this particular data science course in Hyderabad is to help people who may or may not be the practicing data scientists, learn to understand what they can get out of data and some of the methods used to get there.

Let's take a look at another article from LinkedIn: The 25 hottest jobs skills that got people hired in 2014

LinkedIn article on hottest skills of 2014

Take a look at number one here, statistical analysis and data mining very closely related to data analytics and just to be clear, this was number one in Australia, Brazil, Canada, France, India, the Netherlands, South Africa, United Arab Emirates, United Kingdom.

If you need a little more evidence, let's take a look at Glassdoor, which published an article in the year 2016 , about the 25 best jobs in America. Take a look at number one right here. It's data scientists. It says

Glassdoor article on data scientist being the best job in America

This article says it's going to be 1700 job openings with a median-based salary of over 116,000 and fabulous career opportunities and job scores. So if you want to take all of this together, the conclusion you can reach is that data science pays. And I can show you a little more about that. So for instance, here's the list of the top 10 highest paying salaries that I got from US news.

Data Scientist Salary

We have Physicians or doctors, Dentists, Lawyers and so on. Now if we add Data scientists to this list using data from O'Reilly.com, it goes in third with an average total salary, of about $144,000 a year. That's extraordinary. So in sum, what do we get from all of this? First off, we learned that there is a very high demand for data science. Second, we learned that there is a critical need for both specialists and generalists. So those are the sort of practicing data scientists and for generalist people who speak the language and know what can be done and of course, there's excellent pay altogether. This makes data science a compelling career alternative and a way of making you better at whatever you're doing.

Hope it is clear why learning data science is important now !