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The official YDSOA Data Science Blog. Articles and discussions about the world of Data Science. 

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Basil

When you think about handling and processing the huge amount of data, what comes to mind? For many, thoughts of utilizing Python coupled with machine learning algorithms arise. What may not initially come to thought is the notion of using SQL instead. You might be scratching your head at this prospect of using SQL in Data Science or Bioinformatics when there are other alternatives. Or, perhaps, you’re not familiar enough with this particular language to jump to a conclusion. The fact of the matter is that SQL is a programming language you should familiarize yourself with if you’re looking to jump into the world of big data.

So why would somebody use SQL instead of the many alternatives? Simply put, SQL provides simplicity and robustness that you can seldom find anywhere else. Add to the equation that Data Science careers sometimes require more than handling big data. A big skill set that one can have is the ability to conduct database management on web applications; a feat for SQL and an RDMS (we’ll discuss this a little bit later.)

Database Introduction (Introduction to SQL)

For a thorough understanding of SQL and its potential role in Data Science, some basics are needed including an introduction to databases. First off, what exactly is a database? For simplicity, a database is just an organized collection of data. Within this collection, we have even more organization in the form of tables. Tables have specific bits of information stored inside them, and within these tables, there are individual columns that have even more specificity to them.

All of this may seem a little confusing, so we’ll go ahead and see a table, called “table1,” inside a database to clear things up.

Introduction to SQL

(Word of caution:  The database table and associated column names were created with simplicity in mind. You’ll probably never run across a table titled “table1,” or a column titled “address,” especially when dealing with large databases.) 

This sample table contains some information about a fabricated client base. In our actual database, there will be other tables that contain more relevant information, but let’s pretend this is all we need for the moment. As we have mentioned, each column contains a particular characteristic and here we can see the values of clientID, address, city and state.

Every single row in our table contains specific data (in this case, a particular client), whereas the columns include universal values or traits.

SQL is the language, while a Database Management System (DBMS) is the software that contains and manages the data.

Something that a lot of people get confused with is when they hear things about MySQL, SQLite or NoSQL and don’t quite understand its relation to SQL. In our example, we showcased our database table inside a simple Excel file. In a real world example, your data will more than likely be stored in some other software dedicated to database management. This idea is what we refer to as Database Management Software, or DBMS for short. MySQL, SQLite, and NoSQL are all examples of DBMS. You should not worry about mastering DBMS’ until you get the hang of SQL itself. Most DBMS for SQL follow the same protocols, with some minor changes that you can learn later.

A particular kind of DBMS named a Relational Database Management System (RDMS) and uses a specific type of modeling called a relational model. The RDMS, in particular, is called MySQL and is a popular database choice for websites. In fact, it’s what our site uses for database management.

If you are lost with some of the technical jargon, just remember: SQL is the programming language, and a DBMS is the database system we will be using to manage our data. 

Where Can I Practice My Code?

Since we aren’t going to go in-depth with the DBMS, you’re probably wondering how you’ll be playing around with SQL code. There are some programs you can download to do so, or you can utilize this free online SQL interpreter. This website allows you to test out some basic SQL code without having to download more complicated DBMS’.

The Almighty Select Statement

In any SQL or SQL in Data Science course, the first statement you’ll learn is select. It’s quite simple as to why this is the first statement you hear.  In SQL, the objective is to alter and configure the database to suit your needs. To do this, you must select certain attributes and do something with them. However, just selecting something won’t do much. You’ll need to pick something, tell the system where you are selecting it from and then what you want to do with it. This concept of actually doing something with the data leads us to the from statement. Whenever you utilize select, you’ll almost always use from as well. Take a look at the following:

Say, for example; you want to choose all of the client’s addresses from the table example from above.  You would utilize the following SQL statement (given that the table is named table1):

Select address From table1;

The results would yield you all of the addresses for every single row. You could use the same structure if you wanted to pull all of the ClientID’s or any of the other columns from the table.

Don’t Forget The Semi-Colon

The semi-colon in SQL denotes termination, so you’ll need to place it at the end of your SQL statement.

The Where Clause Makes Things Happen

While the select and from statements allow you to pick which specific data you want to handle, the where statement allows you to conduct the altering itself. There are numerous operators that the where clause can use to help you make things happen.

  1. Using the where clause for equality, non-equality, and showing greater than or less than.  (=, <>, >, <.)
  2. Using the where clause to join other clauses.
  3. Using the where clause for mathematical calculations.
  4. Using the where clause to group data.

The list goes on and on for how you can incorporate where into SQL, but just remember that it allows you to make things happen within SQL.

Taking the Basics and Applying it to Data

Our introduction to SQL was overly simplified and was aimed at providing a very brief introduction to those who have never used SQL. If you’re eager to learn more, we recommend a free MOOC provided by Stanford Lagunita.  I’ve personally taken this course, and it’s an excellent introduction to SQL!

We touched at the beginning of this article about some of the ways you might be using SQL in Data Science or Bioinformatics careers. Ultimately, it does depend on the particular job. You may never touch SQL once you are in your future career. Or, on the contrary, you may find that your position uses SQL extensively alongside other programming languages and software. 

Due to the unexpected nature of whether or not you’re going to need SQL, it’s worth a shot to know it at some level. At the minimum, I would recommend you at least have a basic understanding of SQL and how to do simple database analysis and alterations. Again, you never know if this can come in handy.

Some careers may also not care about the specific programming languages, as long as you can conduct the data analysis that they need. Whether you’re using Python, Perl or SQL doesn’t matter nearly as much as whether or not you can perform the tasks. 

Final Thoughts

If you have used any other SQL MOOC’s or have any useful materials for beginners to grasp this programming language, feel free to post the resources below! Be sure also to join us over at the YDSOA Community Forums as we discuss a wide variety of related topics!

Rajib Dutta

The buzz arIs Data Science a Good Career.pngound Data Science continues to grow astronomically. It’s almost monthly that you’ll see an article on Forbes or Indeed discussing how great of a career Data Science is. But just because these websites claim that this is a good job doesn’t mean it’s the best career for you.  A lot of factors come into place in deciding whether you should pursue a career in Data Science.

Gone are the days when tech jobs were only available at Google and Facebook. Today, almost all industries need to hire tech employees. Companies are drowning in all the tsunamic wave of data which is an invaluable asset for drafting business strategies. As a result, the companies need to hire Data Scientists to be able to manage it, analyze it, and use it to identify, predict and solve problems. With the demand for data-savvy professionals increasing at a faster rate, The McKinsey & Company has projected a global excess demand for 1.5 million new data scientists. By 2018, a projected talent gap of 140,000 to 190,000 qualified data science workers is predicted. According to Glassdoor’s list of best jobs for best Work-Life Balance, the data scientist is the best job in America for 2016. One can expect a median base salary of $116,840, with plenty of job openings available.

But what does a work day of a data scientist look like? Are they just confined to an office crunching numbers for the rest of their working life? Not exactly. Data Scientists are constantly trying to predict the future by using numbers. They are working with clients’ or employers problems and replicating models to solve them. What you offer as a data scientist is a comprehensive analysis of the customer’s whole business. This versatility means constant movement and frequent discussions with employees at all levels of your company. So sure, you’ll have a desk with a fancy computer to get the job done, but don’t think of Data Scientists having your stereotypical 9-5 desk job.

The Day-to-Day Activities

Although we just gave you a pretty decent primer on the buzz around Data Science, we haven’t quite answered the topic of whether or not Data Science is a good career. From the perspective of an outsider, Data Science screams loads of mathematics and science. However, if they would take a look at the job sites, they might be shocked at first to find skill qualities such as ‘works well with others,’ ‘knows how to report and communicate’ as part of the job description. Since the roles of Data Scientists mean working across the board with employees of all levels, it’s crucial that you be able to communicate properly. You might be the only Data Scientists in a company, and many of the people you work with would have no relation to statistics or mathematics for years.

 

Is Data Science a Good Career

Communication is one of the most underrated skills for a Data Science. If you know you're not somebody who enjoys communicating sophisticated
and intricate information to the masses, Data Science might not be the best career choice for you. 

 

In other situations, Data Science might permeate into individual units. The chances are that you will be working in the marketing department, the product design department and even the sales department. You can expect to solve real life problems by providing practical solutions. One should also be forward-thinking as you will be using a large amount of data to solve real time problems as they are happening.

So why are we telling you all of this? One must realize that choosing whether or not Data Science is an excellent career choice goes further than just knowing the science behind it. You must understand all the skills necessary and the day-to-day activities that it encompasses. If you know all the programming and statistics, but can’t properly communicate with others; this might not be the field for you.

The Programming

So we’ve gone over the outlook and a brief synopsis of the day-to-day activities for a Data Science. In other articles on YDSOA, we’ve touched on some of the programming and sciences that are needed for a successful Data Science career, including our Machine Learning and SQL articles.  However, there are some more steps you can take to become familiar with the traditional software you’ll be needing to use for jobs in this field.

-R: Let’s start with R.  R is one of the best places to start for those looking to get into Data Science for the fact it has a very active community, and the software itself is free to use. R is traditionally used for statistical analysis, but can also be used for data mining and visualization.  We’ll be rolling out an introductory post into the workings of R programming, but for now, this a great online course to get your feet wet.

-Python: The second language that’s good to have some mastering in is Python. Python is currently one of the most popular programming languages in the world and for good reason.  It’s simplicity, and the overwhelming amount of resources create a user-friendly environment.

-Perl: Perl was originally built in 1987 by a computer programmer named Larry Walt with the purpose of being able to process and handle massive amounts of text. It showed the most popularity in the 1990’s and although it doesn’t have the following it once had, it still remains a force that has stood the test of time in the world of programming.

In addition to its powerful text processing tools such as Regular Expression and other useful abilities, it has several useful add-ons in its repertoire.

Besides the big three, I have listed some other languages and tools that would be helpful add-ons.

-Scala: The hottest language right now, ideal for working with real-time data. We’ll be touching more on Scala in a later article.

-SQL: SQL remains a powerful and easy-to-use programming language, mostly used in database management. Our full SQL introduction can be viewed here

-Excel: Seeing Excel on this list may come as a bit of surprise,  but Excel remains one of the most useful pieces of software a Data Scientist can know. Its incorporation with VBA allows the user to conduct some extremely sophisticated analysis. 

Is Data Science a Good Career?

Being that YDSOA focuses primarily on Data Science and Bioinformatics, you could say that we might be a little bias in our overall consensus on whether Data Science is a good career or not. However, we do believe we've presented some strong evidence on how great the opportunities are in the world of Data Science, and what an interesting a career it truly can be. With that being said, you must understand all aspects that go into the job. Sure, knowing the programming and science behind this career is crucial, and you won’t get farther than a job interview without it. However, don’t underestimate the personal and communication side of things. Realize that you’ll be working with people from a broad spectrum and knowing how to communicate with them properly will be crucial.

Thanks for reading!  If you haven't already, highly encourage you to register today and become a member of YDSOA! Be sure to check out some of our other content from around YDSOA! Network and connect with hundreds of others within the field of Data Science!  Be sure to also check out some of the other great content from our site below: 

Basil

The worlds of Bioinformatics and Data Science share a lot of commonalities. Although one focuses more on biological sciences than the other (Bioinformatics), they still use a lot of the same programming languages, software, and general principles. In this article, we go over exactly the differences and similarities between Bioinformatics Vs Data Science and show you which path is right for you!

What is Bioinformatics? What is Data Science?

In a broad sense, Bioinformatics is the field involving the use of tools to understand and interpret biological data. Data Science is the field involving the use of similar tools to understand data in general.

Two Fields, One Common Goal

Although Bioinformatics and Data Science have many differences, there’s still somewhat of a same underlying goal; using algorithms, tools, and programs to understand and process data. Now if you are a Bioinformatician, that might mean using instruments to help you understand biological data, whereas a Data Scientist may be using similar tools to understand business or marketing data. Does this mean only a Bioinformatician can analyze biological data? No! Both Data Scientists and Bioinformaticians can handle all types of data, but Bioinformaticians have more of a focus on biology than Data Scientists do. Bioinformatics Vs Data Science Although not exclusively, Bioinformaticians tend to deal with biological data more so than Data Scientists.

Which Should You Major or Focus In?

Up until the last couple of years, there was no such thing as a Data Science degree or major. That has changed with the popularity of the field growing at astronomical levels. The answer to whether or not you should major in Bioinformatics, Computational Biology or Data Science lies on what type of career you’d like to pursue. If you want to focus more on the Biomedical Sciences side of things, pursue Bioinformatics or Computational Biology, which gives you a firm grasp on the Sciences needed to handle large biological data. If you want to focus purely on managing data for all disciplinaries, and have no interest in broadening your Biological Science skill-set, err on the side of a Data Science degree. We are not saying that Bioinformatics or Computational Data majors/degrees does not give you ample knowledge or handling all types of data. However, a lot of your time in these degree programs is spent going over biological and chemical systems, so you need to have a passion in these fields, or else you’ll not be enjoying yourself. Just to elaborate on this point, my first year as a Bioinformatics Masters student included challenging courses on human genetics, molecular and cellular biology biological research methods. Someone without any passion in these subjects would have had a torturous time.

At the End of the Day, It is Not Your Degree; It is Your Skills

The biggest takeaway message we have is that it ultimately doesn’t matter what degree you chose, but the skill sets you gain from these majors. Are there jobs that have the requirement of a particular type of degree? Absolutely. These jobs are typically the exception. Instead, most jobs want a set of skills, which anybody can develop regardless if your major is in Data Science or Bioinformatics. Learn as much as you can and hone your skills, and you’ll find that you can make it in all sorts of data-oriented jobs.

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