Top 5 Python Libraries for Data Science

Python’s readability and suitability to data science operations have made it one of the most preferred languages for data analysis. If you are an aspiring Data Scientist and want to use python for playing with the data, then this post will help you to begin python for data science. Python comes with numerous libraries for scientific computing, analysis, visualization etc.

Here are the five most important Python libraries for Data Science:

1. Numpy

Numpy is the fundamental library for performing scientific calculations and many of the libraries use Numpy arrays as their basic inputs and outputs. It is an open source extension module of python. In addition, it optimizes python with powerful data structures for computation of multi-dimensional arrays and matrices efficiently.
Even understanding NumPy arrays and array-oriented computing will help you to understand a data set more effectively.


2. Pandas

Pandas is an open source and one of those libraries for data analysis, that contains high-level data structures to manipulate data in a very simple way. It is well suited if your data is in tabular, ordered and unordered time series form and provides tools for shaping, merging slicing datasets and reshaping. It is the best tool for doing data munging and to handle missing data.
With Pandas, you can load your data into data frames, select columns for specific value, perform statistical operations, merge data frames etc.


3. Matplotlib

Matplotlib is best and well-known Python data visualization library. It allows you to quickly make line-graphs, histograms, pie charts etc. Using Matplotlib library, you can customize every aspect of a figure. It exports graphics to common vector and graphics formats like PNG, BMP, JPG etc.


4. SciKit Learn

SciKit-learn is an open source library for the python. It has several supervised and unsupervised machine learning algorithms. It is a free library which contains efficient tools for data analysis and mining purposes. You can perform various algorithms such as support vector machines, naïve Bayes, random forests, DBSCAN etc.


5. SciPy

SciPy is a python module which is widely used in scientific and N-dimensional array manipulation. It is a free library that provides the core mathematical methods to do the complex machine learning process. It contains modules for linear algebra, interpolation and image processing.


Top 5 Data Visualization Tools

Data Visualization is the most important part of data analysis. For a Data Analyst or Data Scientist is important to present the data in a visually appealing format. So, that the insights from the big volume of data can be visually presented in an easy way. There are various data visualization tools that a Data Analyst or Data Scientist uses to present data via graphs and charts.

In this article, we will Explore the best Data Visualization tools:

Top 5 Data Visualization Tools-

1. Tableau

One of the major tools in the Data Visualization that is being used by most for the Analyst and Scientist. It has gained popularity around the world owing for its drag and drop feature in User Interface. This tool is free for some basic versions. It provides facility to connect to a variety of data sources like (CSV, XML, JSON, MS Excel), relational and non-relational database like (PostgreSQL, MySQL, MongoDB) and cloud systems like (AWS, Oracle Cloud, Microsoft Azure etc). There are different types of Tableau versions: Tableau Desktop, Tableau Public, Tableau Server and Tableau Online. For more information on Tableau, reach out at Tableau Official Website.


2. Google Charts

Google offers its charting framework to create highly customizable data visualization. Moreover, it is the easiest to learn and use Google Charts. If you are a Beginner and want to start Data Visualization then, this is the perfect tool to start with. You can use this tool for handling huge data set. It is a free tool, you can reach out at Google Charts Official website.


3. QlikSense

QlikSense is a powerful tool from Qlik family used for data visualization. With this tool, you can analyze data. It provides drag-and-drop user-friendly features. It is intended that user can use it with ease. It is cloud-based infrastructure which makes it strong among other data visualizations tool. It is a free tool, you can reach out at QlikSense Official website.


4. SAS Visual Analytics

SAS is one of the traditional vendors in the advanced analytics space, by offering analytics insights to businesses. SAS Visual Analytics is not only a data visualization tool but it is capable of predictive modeling and forecasting. It provides drag and drop features. For more details, you can reach out SAS Visual Analytics from here.


5. D3.js

D3.js is a JavaScript library which is used to manipulate bind data with Document object Model. It is an open source library. It uses HTML, CSS and SVG to create visual representation of data on browser. You can reach out at D3.js official website.


5 Top Free Online Survey Tools that will help your Business to Grow

In Today’s world, every business organization is starving for quality and valuable data. In E-commerce business, where you are not interacting directly with customers, it is hard to gather data and further analyze it. But there are super amazing free online survey tools, from which data collection process has become very easy. Surveys are the best method to find out what users think about your website and what they think about your products and services. It not only provides you with information but also engages your audience.

Here are list of Top 5 free online Survey Tools listed below:

1. Google Forms
google forms

If you have a Google account, you already have access to the decent survey tool. Google Forms is the finest tool to perform online surveys. This tool is completely free and has no restrictions on the number of surveys you create. The number of questions you can create, the number of responses you can collect. Even you can automatically export your results to Google Sheets for online access and sharing. It also provides free skip logic, which is pretty awesome.


2. SurveyMonkey
Survey monkey

SurveyMonkey is a browser-based survey tool that allows you to create customized surveys and share them with variety of channels. You can embed it in your website or send link to the individuals. In the Free version, you can create 10 questions for a survey and can get 100 respondents. So, if you want to create a customized survey you can opt for the other plans according to your requirement. The paid version starts from $26/month.


3. TypeForm
Type forms

Typeform is a platform that can create a variety of surveys using conversational data collection methods. If you are really a big fan of online surveys and want to create multiple options, then you can opt for Typeform. It has a simple user interface that offers drag-and-drop creation. However, you can use this tool for free up to 100 responses per month and for more responses, you will need to consider a plan starting at $29/month.


4. SurveyPlanet
Survey Planet

SurveyPlanet is an advanced and another easy-to-create survey tool that can create beautiful surveys with no restriction on surveys. It paid plan starts at $180/year which includes custom survey themes, anonymous survey functionality, letting you collect sensitive data from respondents and result in the form of charts.


5. Polldaddy
Polldaddy

Polldaddy allows user to create a variety of polls, quizzes and ratings. If you have a WordPress site, then this free tool is good option for you, you can integrate it. The free version offers unlimited surveys and answers. If you want few more features such as Company’s branding, add custom CSS, export data. Then you can opt for pro version, you will be @29/month.


Conclusion:

When creating a survey for your business it is important to choose best online survey tool. Hopefully, you will find one here. The tools listed above have a wide range of features, you can go through links and choose which suits for your requirements and more importantly it is free initially, for more features you can upgrade your plan.

Why should you use Python for Data Science

Python is a general purpose interpreted, object-oriented scripting, and high-level programming language. Python is a great for beginners or for data scientists who really want to build up their skillset. Guido van Rossum created it during the 1985-1990. It was designed to be highly readable. It has various frameworks for web development and other features to expand it for Graphical User Interface, data analysis, data visualization etc. Through its simplicity it is extensively used by many organizations for evaluating large dataset for doing data analysis. The idea of the blog is to provide you why python is good for Data Science.
Python language is among the most popular Data Science programming language not only with the top companies but also with the tech startups. It offers plenty of benefits which mean that an increasing number of people are adopting Python for their work and it I a practical choice for tech type of all kind-data scientist included and is increased adoption in numerical computation, statistical analysis, machine learning and in several data science applications.

Here are five reasons why you might choose Python for Data Science:

1. Python is easy to use
Python code is more clearly defined and is easy to understand even if you are beginners or for data scientists who want to build up their skill set. Python has few keywords, simple keywords and a clearly defined syntax. Python is great programming language whether you are an experienced data scientist or analyst, a software engineer who is going to start working more closely with machine learning or even a complete beginner, Python is going to be best programming language for data analysis.

2. Python is versatile
Python can run on a wide variety of hardware platform and its source code is easy to understand. Python is a powerful tool whatever problem you want to solve, it will help you to understand the problem more precisely. From building machine learning models, data mining, Python is a great programming language that helps you to solve data problems.

3. Python works better for building analytics tools
If you have dataset and you want to find outliers in a dataset then python works pretty well. It has number of libraries to do statistical analysis for your data. From building machine-learning models, it works well with having an interactive environment of IDE.

4. Easy Data Visualization with Python
Python have a large range of powerful visualization libraries available such a Matplotlib, Plot.ly or Seaborn and plenty of scientific packages for data visualization, Machine Learning, natural language processing, data analysis and much more.

5. Python Community is Growing
Python has a huge community including a strong and growing presence in the data science community. PyPi (Python Package Index) is a useful place to explore the full extent which was developed by the Python community. Pyslackers is a great community for Python enthusiasts.

What is Data Science?

What is Data Science?

Data science is a multidisciplinary blend of various tools, algorithms and machine learning principles with the focused on extracting knowledge and insights from the raw data. The term Data Science has emerged recently with the evolution of mathematical statistics and data analysis and it is also known as data driven science. Mining large amounts of structured and unstructured data, which makes use of scientific methods, processes and systems to extract and identify patterns that help an organization to increase efficiencies, recognize new market opportunities.

Why Data Science?

It’s been said that Data Scientist is the “Sexiest Job of the 21st Century”, the reason behind it is over the past years, companies have been storing their data from the various sources and every company has data from which they want to get meaningful information which will help them in growing.

How data Science will help, let’s understand by using an example:

Say, you have a company which makes LED screens. You have released your first product and it became a massive hit. Every technology has a limited life, so now it’s time to come up with something new. But you don’t know what should be innovated, so as to meet the user’s expectations, who are waiting for your next released. You can take user’s feedback and pick things which users are expecting in the next release. The feedback which you get from user’s end, you can apply various data mining techniques like sentimental analysis etc to get desired results which will help to make better decision.

Who is Data Scientist?

Data Scientist is responsible for deriving insights from large amount of data either structured or unstructured to help organization for their growth. The role of data scientist is becoming increasingly necessary as businesses rely more on data analytics to drive decision making.

If you want to know difference between Data Scientist and Data Analyst, check this

Technical Skills for Data Scientist:
1. R or SAS: Good knowledge of at least one analytical tools is generally preferred.
2. Python: Python is most common coding language typically required for data science roles.
3. Database Management: Either SQL or NOSQL, depending on the requirement.
4. Unstructured data: Data scientist should be able to deal with unstructured data. The unstructured data generated from social media platform or any other platform.
5. Visualization Skills: A data visualization tools like Tableau, Qlikview is generally preferred to present insights from data.
6. Statistical Knowledge: Good Understanding of statistics is vital for a data scientist. He should be proficient with statistical tests, distributions, maximum likelihood estimators etc.

 

Applications of Data Science:
1. Internet search: There are many search engines including Google which make use of data science algorithms to deliver the best result for our searched query.
2. Recommender Systems: A lot of companies used this engine to promote their products in accordance with user’s interest like Amazon, Google Play, Netflix and many more uses this system to improve user’s experience.
3. Price Comparison Websites: These websites are being driven by lots of data which is fetched using APIs and RSS feeds. This websites gives you comparison of the price of the product from multiple vendor at one place.
4. Delivery Logistics: companies like FedEx, DHL are using data science to improve operational efficiency. They used data science to find best routes to ship, best suited time to deliver and best mode of transport.

Loops in R | R Programming

A Loop statement allows repeating a specific statement or group of statements multiple times. In this article, you will learn to create different loops in R Programming:

• For Loop
• While Loop
• Repeat Loop

For Loop: It is used to iterate over a vector.

Flowchart of For Loop:


Syntax of For Loop:

For (val in sequence)
{
#statement
}

Here, the sequence is vector and val takes on each of its value during the execution of loop.
Example:

X<-c(1,2,3,4,5,6,7,8)
sum<-1
for( i in X)
{
sum=sum+i;
}
print(sum);

Output:
[1] 37

While Loop: It is used until a specific condition is met.
FlowChart of While Loop


Syntax of While Loop:

while (test_expression)
{
#statement
}
Here, test_expression is evaluated and the body of the while loop is entered if the result is TRUE. This is repeated each time until test_expression evaluates to FALSE.

Example:

X<-5
while(X< 10){
print(X)
X=X+1
}

Output:
[1] 5
[2] 6 
[3] 7
[4] 8
[5] 9

Repeat Loop: It is used to iterate over a block of code multiple number of times. To exit from this loop you have to use the break statement else it will result into an infinite loop.

FlowChart of Repeat Loop:

Syntax of Repeat Loop:

repeat{
#statement
}

Example:

Z<-2
repeat{ 
print(Z)
Z=Z+1
if(Z==10){
break;
}
}
 
Output:
[1] 2
[1] 3
[1] 4
[1] 5
[1] 6
[1] 7
[1] 8
[1] 9
 

Operators in R | R Programming

This is the fifth part of the R series. In this article will know about Operators in R Programming.The operators are those symbols which tell the compiler to perform mathematical manipulations.

There are mainly five types of operators, which are as follows:

1. Arithmetic Operators
2. Assignment Operators
3. Relational Operators
4. Logical Operators
5. Special Operators

Arithmetic Operators: These operators are used to perform arithmetic operations such as addition, subtraction, multiplication, division etc.
Syntax: Variable name <- value
Examples are:

# Addition
X <- 20
Y<- 10
X + Y

Output:

# Subtraction
X-Y

Output:

# Multiplication
X*Y

Output:

# Division
X/Y

Output:

Assignment Operators: These operators are used to assign values.
Examples are:

# Leftwards Assignment
X<-50
X

Output:

# Rightwards Assignment
10 -> X

Output:

3. Relational Operator: These Operators defines a relation between two entities.

X<-5
X!=3

Output:

4.Logical Operator: These Operators compare two entities and typically used with Boolean (logical) values.

X <-9
9&6

Output:

5. Special Operators: These operators are used for specific purpose.

X <-5:10

Output:


Data Types in R |R Programming

This is the fourth part of the series. In this article, you will know about the data type of R Programming. Here all the functions that I have done in RGui.

Data Types in R:

1.Vectors: A Vector is a sequence of data elements of the same type. But you can mix objects of different classes too. To create a vector in R we use combine function. Here are some of examples.

# numeric vector
Numeric=c(1,2,3,4,5)

Output:

# character vector
s=c(“Ganesh”,”sukrati”)

Output:

# Boolean vector
Boolean=c(“True”, “False”)

Output:

To check the class of any object use class(vector name) function.
class(Numeric)

2.List: A list can store other objects including matrices, data frames and other lists. A list can be created using list() function.

#To create List:
p=list(Numeric,85,”ball”)

Output:

3.Matrices: A matrix is represented by set of rows and columns. It is a 2 dimensional data structure. It consists of elements of same class.

# create matrix
M=matrix(1:6,nrow=3, ncol=2,byrow=TRUE)

Output:

#To know about dimension of matrix:
dim(M)

Output:

#To Bind two columns:
x=c(7,8,9,10,11,12)
y=c(13,14,15,16,17,18)
cbind(x,y)

Output:

4.Data Frames: Data frame can store different data types. The data stored in a data frame can be of numeric or character type.

#To create data frame:
D=c(1,2,3)
E=c(4,5,6)
F=c(7,8,9)
LF=data.frame(D,E,F)

Output: