In this blog we will cover the following topics:

• What is Data Science?
• What are the Top 3 Programming Languages for Data Science?
• What is SQL?
• R & Python: Which is the Best Tool for Data Science?
• What is the job market demand for tools such as R and Python?
• How is Tableau Used for Data Science?
• Why is Tableau Used instead of Excel?
• What is Big Data?
• Is Hadoop Necessary for Data Scientists?
• Can You Use Tableau for Big Data Analytics?
• In which order one should learn data analytics tools viz. R, Python, Hadoop, Tableau, and SQL?

What is Data Science?

It is a well-known fact that without facts and figures, no entity is going to survive the digital era. Data is the oil and fuel of any organization, thanks to the unprecedented rate at which digital mediums are generating them. Companies nowadays have all kinds of information at their disposal leveraging which not only helps them uncover hidden trends and patterns, but also re-strategize and re-formulate the entire model as per the ongoing requirement.

This constant need for upgrading the existing models has given rise to a field that is a blend of analytics and computation. This domain is commonly called “Data Science”.

Data Science is a field that attempts to find the trends and patterns lying under the layers of raw data. After they are meticulously prepared and explored, these data throw up important details which the organization can use for its benefit.

Going by the above description, Data Science can be defined as, “Data Science is a multi-disciplinary domain which is made of three different fields of studies, i.e., Computer Science, Mathematics & Statistics, and Domain Expertise, that seeks to identify the trends and patterns from the bulk of raw data”.

Data Science is a multi-disciplinary domain which amalgamates three vast fields, i.e., Computer Science, Statistics & Mathematics, and their respective Domain expertise. So, anyone who wishes to get into Data Science should acquire these three skills:

1. Computer Science: Computer Science deals with applying techniques, algorithms, and programming, etc., to perform a task. There are many fields in computer science, such as web development, programming, cyber security, analytics and much more.
2. Mathematics and Statistics: Mathematics and Statistics denote the computational portion of Data Science. Data Science uses the concepts of Mathematics and the models of Statistics to wrangle explore the data, and test the hypotheses so that crucial business decisions can be made.
3. Domain Knowledge: Apart from computations and algorithms, Data Science also requires business acumen and domain knowledge that are essential to solve a business problem. Data Science strives to analyze the root-cause and of a problem and tries to resolve it with logic.

The above three components are the primary areas which make Data Science a field. However, Data Science itself is a process that requires multiple tools and techniques to get the final outcome. In the following section we will find out the essential tools that a Data Scientist needs to learn to carry out the entire process successfully!

Necessary Tools for Data Science

Data Scientists need various tools to perform various operations. These tools are required as and when they proceed through the Data Science Life Cycle. Some of the prominent technologies and their respective purposes are as follows:

1. Extracting the Data: As per the Data Science Pipeline, extracting the data is the first step. This implies getting the data from the database to be analyzed. SQL is the most primary technology that Data Scientists use for fetching the raw data from the databases.
2. Cleaning the Data: It is the process of cleaning or preparing the data, and is called Data Wrangling, or Data Munging. It is the process of transforming the raw data to a proper format. This is usually done using SQL or Python Library called Pandas.
3. Building the Model: After Data is explored and wrangled, the next step is to build the model. In order to analyze the patterns in the data, Tableau, SAS or MicroStrategy can be used. After this, data models can be built using R, Python Machine Learning Libraries like Pandas, Scikit, etc., Tableau, or on Hadoop.
4. Predicting the Outcomes: After all the analysis is done and we have trends and patterns in hand, they are visualized using tools like Tableau.

 So, if you look closely, then there are a few primary technologies that are coming at the forefront of the process of Data Science, i.e., SQL, R, Python, Tableau, and Hadoop. Let’s take a closer look at all these technologies, their uses, their alternatives, and how they serve their purpose.

What are the Top 3 Programming Languages for Data Science?

Programming forms an integral part of Data Science. Data Scientists need to develop applications, models, perform statistical analyses, visualize the data, record conclusions, and do many other things, small and big, that require the knowledge of programming.

There are multiple languages out there with different utilities and purposes. However, in the world of Data Science, there are primarily three programming languages that take the front row!

1. SQL
2. Python
3. R

Credit: TowardsDataScience

If you take a close look at the above graph, it will be crystal clear to you that of the top 10 programming languages, Python, R and SQL are the most popular programming language among Data Scientists. About 90% of the Job Ads for Data Scientists ask for Python as a primary skill, 73.4% Job Ads ask for R Programming and about 58.5% Job Ads want the candidate to be a master in SQL.

It is not just about learning these programming languages, but also about knowing the important of each of these as per the level of seniority. This requires you to have a clear idea on what should be the proportion of your skill-set as you climb the ladder up in the organizational hierarchy. This is what we intend to explain next.

Credit: TowardsDataScience

By paying attention to the above bar graph, you will come to know the demand of each of these three skills as per the level of seniority in the Data Science field. Three most important conclusions can be made from the above graph:

1. The demand for Python increases as per the level of seniority, i.e., 94.6% for senior positions, 88.1% for Mid-level positions, and 86.7% for the entry-level candidates.
2. The importance of knowledge of R Programming is somewhat similar for both the senior level as well as Mid-level (76.2% and 75.7% respectively), which is definitely higher than the demand for R Programming in entry-level candidates (60.0%).
3. It is only the knowledge of SQL that is less required when the level of seniority increases. It is highest (73.3%) for entry-level employees, 64.3% for the mid-level employees, and 45.9% for seniors.

In the coming sections of this blog, we will discuss each one of these languages and skills related to them to be acquired by an aspiring Data Scientist.

What is SQL?

SQL is by default the most primary language used for database across the globe. SQL stands for Structured Query Language and is used to connect to the database. The database for which SQL is used is relational in nature. SQL is basically a query language that is used to perform certain operations like Select, Insert, Update, Delete, Create, Drop, etc.

A Data Scientist cannot perform any operation till she is able to pull the right data from the database. For this, it is highly important for an aspiring Data Scientist to learn SQL.

Relational Database Management System or RDBMS is a collection of tables that are related to one another. Most of the databases like MS SQL Server, IBM DB2, Oracle, MySQL, etc., are Relational in nature.

In the following segment let’s get to know why SQL is needed in Data Science in the following segment!

Why is SQL Needed in Data Science?

Data Science is nothing without Database. In order to analyze the data and perform various operations on the data, a Data Scientist has to extract it from the database, and therefore a programming language that can help you connect with the Database is highly important. SQL is that bridge.

SQL is the standard query language for many Relational Databases. Some of the Big Data technologies like Hadoop and Spark use SQL to connect to the Database.  Some of the reasons for which a Data Scientist needs to learn SQL are:

1. Handle Structured Data: Data Scientists require SQL to manage the structured data which is stored in Relational Databases. In order to fetch the data, you need to know a language that can communicate to the database. That is where the knowledge of SQL becomes imperative for a Data Scientist.
2. Necessary for Big Data Technologies: Most of the Big Data Technologies require the Data Scientists to master SQL to manipulate the database.
3. To Test Environment: Data Scientists have to create test environment in order to experiment the data, and for this SQL is used as a standard tool.
4. To Analyze Data: Data Scientists need SQL to analyze the data that is stored in the Relational Databases.
5. For Data Wrangling: SQL is also required to wrangle the data and prepare the data to be analyzed. 

SQL Skills Required for Data Science

Since, SQL is one of the most popular technologies used by data Scientists they have to acquire the following SQL skills:

1. Modeling the Database: A Data Scientist needs to understand the database models using SQL. She should know how to model one-to-one, one-to-many, and many-to-many relationships. She should have a sound understanding of accessing, retrieving and manipulating the database through SQL.
2. Knowing the Commands: SQL commands are instructions that are used to communicate with the database and perform various tasks. SQL commands like DDL, DML, DCL, TCL, and DQL are highly used in Data Science.
3. Aggregation Functions: A Data Scientist needs to know various aggregation functions in SQL like Count(), Sum(), Avg(), Min(), Max(), etc. Since these functions are used all the time, it is very important for a Data Scientist to know Database Aggregations Functions.
4. Window Functions: Window functions are highly useful for Data Analysis as they can perform aggregations without reducing. Window functions help perform predictive analytics and they are quite important for Data Science.

Demand for SQL to get a Job in Data Science

So, now that you have clear idea about how SQL is of high importance for Data Scientists, let’s know how SQL plays an important role in landing a top-notch job.

Credit: Indeed.com

If you pay attention to the above bar graph by Indeed, you would find out that SQL is at the top of all the programming languages that employers are looking for in a candidate. It is the language that everyone in the software industry needs to have a sound knowledge of.

According to Indeed, most of the jobs that have the word ‘Data’ mentioned in the job description are asking for SQL as the Primary skill, more than Python and SQL.

This is partly due to the reason, that SQL is a fundamental skill that anyone willing to enter the Data Field has to acquire.

R & Python: Which is the Best Tool for Data Science?

Whenever you hear someone talking about Data Analytics or Data Science, the mention of R, Python, and SAS is imperative. These three tools have not only dominated the Data Industry, but have come forward as the complete packages for Data Science, which top-notch companies are inclining towards to implement.

In this section we will do a comparative analysis of these three technologies and try to find out the best tool that you should learn for Data Science!

Python for Data Science

Python is a general-purpose, open-source, programming language created by Guido Van Rossem. The objective of creating this programming language was to create simple programming language that is easy to learn and implement. Though it started as a general programming language, in recent years Python has branched out into being one of the most preferred languages for Data Science.

Due to its flexibility and code reusability, Python is well-suited to Machine Learning and Deep Learning. Companies across the globe are paying attractive paychecks to aspirants who have mastered Python. Some of the popular libraries of Python are:

• Pandas
• SciPy/NumPy
• scikit-learn
• matplotlib
• statsmodels

Let’s take a quick look at Python’s Pros and Cons!

Pros of Python

• Blend of Three Types of Programming: Python is a combination of object-oriented, structured, and functional programming Language.
• Easy Syntax: Python is known for its easy syntax and therefore having a linear learning curve. It’s syntax is highly readable.
• Flexibility: Python is highly flexible, due to which it becomes easy to conduct exploratory Data Analysis in it.
• Open-Source: Python is an Open-Source Programming Language, and is freely available. Python is being supported by a huge community group, and hence, it is witnessing quick and humongous changes in its source code.
• Huge Libraries: Although Python was not meant for Data Science, it been enriched with a plethora of libraries for multiple purposes. This is because it is an open-source programming language. Some of the common purposes are regular expressions, documentation-generation, unit-testing, web browsers, threading, databases, CGI, email, image manipulation, etc.
• Embeddable: Python code can be embedded to the source code of a different language to equip the programmer with scripting capabilities.

 Cons of Python

• Slow Execution: Python is interpreted, which is why it is executed line-wise. This makes Python a slower programming language.
• Not Native to Mobile Environment: Python does not completely support Mobile Computing. The two most popular Mobile Operating Systems, Android and iOS, do not support Python.

Learn more about Python in this gripping and insightful post!

R for Data Science

R is an open-source programming language dedicated to Statistics and Mathematics used for computation and visualization purposes. R has been the de facto programming language among Statisticians, Researchers, Mathematicians, and Academic Professionals. Like Python, R is also used quite frequently used by the Data Scientists.

Some of the popular packages of R Programming are:

• dplyr
• plyr
• data table
• stringr
• zoo
• ggvis,
• lattice
• ggplot2
• caret

Let’s take a quick look at R’s Pros and Cons!

Pros of R

• Compatible: R is a highly compatible programming language that can be paired with several of other programming languages like Python, Java, C, C++, etc. Also, it can be integrated with Big Data technologies like Hadoop.
• Amazing Visualization: R has amazing functionalities for creating both static and interactive visualizations with the help of packages like Plotly, Highcharter, Dygraphs, and Ggiraph.
• Range of Packages: R has an array of packages that is managed by CRAN. All these packages facilitate extensive support for all the statistical computations, model building and analytics.
• Platform Independent: R is a platform-independent programming language which can be easily run on Windows, Linux, and Mac.

Cons of R

• Difficult to Learn: The syntax for R Programming is a bit complicated and the learning curve for this language is steep. Therefore, it is not suitable for beginners.
• Slow Execution: At times R executes the codes slowly if it is not written in a proper fashion. Therefore it is mandatory to include libraries to accelerate the execution.

R Vs Python: A Comparative Analysis

Now that you have got a fair idea about both R and Python, as well as their pros and cons, Let’s compare these two so that you can make a better decision!

Read this riveting blog on R vs Python to make the right decision!

What is the job market demand for tools such as R and Python

Burtch Works published a report in 2019, where it compared Python, R, and SAS, and their market trend.

The above graph clearly shows that 41% of people use Python, 30% use R, and 29% people use SAS. This indicates that Python is more popular among the Data Scientists.

In the above graph drawn on the basis of years of experience, Python is more popular among the beginners and college students. SAS is more popular among the experienced analysts and Data Scientists. On the other hand, the popularity of R programming decreases as the level of seniority increases.

Finally, the above graph reflects the popularity of R and Python among professionals who are into Data Science and Predictive Analytics. Clearly, R and Python are more popular among professionals who are into both, unlike SAS professionals who are only into Predictive Analytics.

How is Tableau Used for Data Science?

Tableau is one of the most popular tools used for Data Visualizations and Business Intelligence. Tableau helps create attractive visualizations and dashboards in an interactive fashion. These visualizations help dig deeper into the information to find trends and patterns.

Tableau has the functionalities to draw the following interactive visualizations:

1. Motion Chart
2. Bump Chart
3. Donut Chart
4. Waterfall Chart
5. Pareto Chart

Let’s understand why Tableau is preferred over conventional tool Microsoft Excel in the following section!

Why is Tableau Used instead of Excel?

Microsoft Excel has always been the first choice for Analysts. It can handle medium-length datasets and can perform various operations like Joins, and Loops, etc. However, with the advent of smart BI tools like Tableau, Data Visualization has found a new dimension altogether, aiding Analytics and data Science in a profound way.

Let’s see how Tableau is better than Excel!

Criteria	Tableau	Excel
Purpose	To visualize data	To manipulate data and visualize data.
Geographical Data	Tableau Supports Geographical Data.	Excel does not support Geographical Data.
Data visualization	Allows creating multiple reports and dashboards.	Allows generating quick reports.
Used by	Data Analysts and Data Scientists.	Data Analysts and Developers.
Type of Data	Suited for Big Data and Big Data Analytics.	Suited for small range of data.
Real-time Support	Supports Real-time information.	Arrangements needed to be made to support real-time insights.

What is Big Data?

As per a Forbes article published in 2018, 90% of all the data present in the world right now has been generated between 2016-2018.

While this is a mind-boggling fact, it is also true that data is being generated each and every second creating a huge bulk of data in the digital medium. These data have proven to be providing eye-opening insights to the firms that were previously unknown to the world. The need for processing and analyzing data has given birth to a pool of new fields and job roles that are the talk of the town. Two such terms are ‘Big Data’ and ‘Data Science’!

“Big Data” is the term assigned to the data that is huge, includes data of multiple formats and is generating at an unprecedented speed. All these years companies were considering data that were structured and accessed from defined sources. However, with the sources multiplying every day and the speed of data generation increasing, a huge volume of data is getting generated every second. This data is called Big Data.

So, going by this description, we can clearly figure out three prominent characteristics of Big Data, which was also defined by Doug Laney, a Gartner Analyst, in the year 2001:

1. Volume: Data that is voluminous.
2. Variety: Consists of data of all types, structured and unstructured.
3. Velocity: Data that is generated at an unprecedented speed.

However, looking at the way data is exploding out of the digital medium, two more Vs have been added to the list:

• Value: Data that is immensely valuable.
• Variability: The dataset whose value ranges widely.

The field that analyzes Big Data is called ‘Data Science’.

Is Hadoop Necessary for Data Scientists?

Hadoop is an open-source and scalable Big Data framework that processes bulks of Datasets across clusters of computers. Developed by Doug Cutting, Hadoop is designed to scale up from a single server to multiple machines.

Some of the reasons that make Hadoop a good choice for Data Science are:

1. Ability to Explore Large Datasets: With Hadoop integrated to the architecture, a Data Scientist can easily explore large Datasets in lesser time. Hadoop is based on distributed computing which makes it efficient enough to handle large datasets.
2. Low Cost: Hadoop is open-source in nature and hence deploying this Big Data Framework does not incur much cost. The data in Hadoop is stored on commodity hardware.
3. Lower Chances of Faults: Hadoop secures multiple copies of all the data on various nodes. When a node fails, then the data processing jobs are transferred to other nodes.

Can You Use Tableau for Big Data Analytics?

Tableau is a smart technology that translates Big Data into meaningful and actionable insights. However, with the rapid speed at which data is being generated and the humongous size of the data, companies could do better by opting for Big Data technologies like Hadoop, Spark, etc., instead of traditional databases.

Some of the ways in which Tableau enhances Big Data Analytics are:

• Real-time Analytics: Tableau allows mining the bulks of data so that relevant questions can be asked and the answers can be provided in real-time. This in turn helps better decision-making.
• Complex Data Modeling: Tableau is able to integrate various Big Data technologies seamlessly and helps model even the most complex data and accelerate the overall performance.
• Consolidate the Reports: Tableau allows viewing all the data and reports in one place instead of looking at separate reports.

In Which Order one Should Learn Data Analytics tools viz. R, Python, Hadoop, Tableau, or SQL?

As per the above discussion, the steps you should follow while learning the language will be:

Step 1: SQL: SQL is a very powerful language and is a very basic pre-requisite. First of all, the aspirant should have a sound knowledge of SQL so that you know how to access the database, perform operations and manipulate the database.

Step 2: R or Python: After you have mastered SQL, it is time to start getting your hands on programming languages. Languages that are prominent in the data-drive industry are Python and R.

Step 3: Data Visualization Tool: Various technologies that you have to master are R, Hadoop, Tableau, etc.

Step 4: Tableau: At the end, to visualize the data, you need to master BI tools such as Tableau.

Therefore, all the above technologies are important but learning them at the right stage is also important.