Course Content

The Data Science: An Overview

• Introduction to the Data Science
• Different Sectors Using Data Science
• The Purpose and Components of Python

2. Data Analytics Overview

• The Data Analytics Process
• Exploratory the Data Analysis (EDA)
• EDA-Quantitative Technique
• EDA - Graphical Technique
• The Data Analytics Conclusion or Predictions
• The Data Analytics Communication
• The Data Types for Plotting

3. Statistical Analysis and Business Applications

• Introduction to the Statistics
• About Statistical and Non-statistical Analysis
• The Major Categories of Statistics
• About the Statistical Analysis Considerations
• The Population and Sample
• What is the Statistical Analysis Process?
• The Data Distribution
• Dispersion

4. Python Environment Setup and Essentials

• About the Anaconda
• The Installation of Anaconda Python Distribution
• Data Types in the Python
• Basic Operators and Functions

5. What is Mathematical Computing with Python (NumPy)?

• An Introduction to the Numpy
• The Activity-Sequence it Right
• Class and Attributes of ndarray
• All About the Basic Operations
• Activity-Slice It
• Copy and Views
• About the Mathematical Functions of Numpy

6. The Scientific computing with Python (Scipy)

• Introduction to the SciPy
• About the SciPy Sub Package - Integration and Optimization
• What is SciPy sub package?
• Know About the SciPy Sub Package - Statistics, Weave and IO

7. The Data Manipulation with Pandas

• Introduction to the Pandas
• Understanding DataFrame
• The Missing Values
• The Data Operations
• About File Read and the Write Support
• What is Pandas Sql Operation?

8 . The Natural Language Processing with Scikit Learn

• NLP: An Overview
• What are NLP Applications?
• About NLP Libraries-Scikit
• The Extraction Considerations
• The Scikit Learn-Model Training and Grid Search

9. The Data Visualization in Python using matplotlib

• Introduction to the Data Visualization
• What are Line Properties?  
• (x,y) Plot and Subplots
• The Types of Plots

10. Web Scraping with BeautifulSoup

• Web Scraping and Parsing
• Understanding and Searching the Tree
• Know the Navigating options
• Know About Modifying the Tree
• How to Parse and Print the Document?

11. Python integration with Hadoop MapReduce and Spark

• Know Why Big Data Solutions are provided for Python?
• Describing Hadoop Core Components
• The Python Integration with HDFS using Hadoop Streaming
• The Python Integration with Spark using PySpark

Trainer Profile

Interview Questions & Answer

1) What is Data Science?

Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.In other words it’s a science and methodology of acquiring data, pre-processing data, analyzing data , visualizing data and drawing meaningful conclusions from the data in order to drive the business need. 

2) Define EDA?

EDA [exploratory data analysis] is an apporach to analysing data to summarise their main characteriscs, often with visual methods.

3) What are the data validation methods used in data analytics?

The various types of data validation methods used are:

• Field Level Validation – validation is done in each field as the user enters the data to avoid errors caused by human interaction.
• Form Level Validation – In this method, validation is done once the user completes the form before a save of the information is needed.
• Data Saving Validation – This type of validation is performed during the saving process of the actual file or database record. This is usually done when there are multiple data entry forms.
• Search Criteria Validation – This type of validation is relevant to the user to match what the user is looking for to a certain degree. It is to ensure that the results are actually returned.

 4) Python or R – Which one would you prefer for text analytics?

The best possible answer for this would be Python because it has Pandas library that provides easy to use data structures and high performance data analysis tools

 5) What is RDD?

 RDD represents Resilient Distributed Datasets (RDDs). In the event that you have enormous measure of information, and isn’t really put away in a solitary framework, every one of the information can be dispersed over every one of the hubs and one subset of information is called as a parcel which will be prepared by a specific assignment. RDD’s are exceptionally near information parts in MapReduce.

6) How would you determine the quantity of parcels while making a RDD? What are the capacities?

You can determine the quantity of allotments while making a RDD either by utilizing the sc.textFile or by utilizing parallelize works as pursues:
Val rdd = sc.parallelize(data,4)
val information = sc.textFile(“path”,4)

7) What Is Sampling?

Sampling is that part of statistical practice concerned with the selection of an unbiased or random subset of individual observations within a population of individuals intended to yield some knowledge about the population of concern.

8) What are various Python Libraries for Data Analysis?

Python is a simple programming language to learn, and there is some basic stuff that you can do with it, like adding, printing statements, and so on. But , if you want to perform data analysis, you need to import specific libraries. Some examples include:

• Pandas - Used for structured data operations
• NumPy - A powerful library that helps you create n-dimensional arrays 
• SciPy - Provides scientific capabilities, like linear algebra and fourier transform
• Matplotlib - Primarily used for visualization purposes
• Scikit-learn - Used to perform all machine learning activities 

In addition to above, there are other libraries as well, like:

• Networks & I graph
• TensorFlow
• BeautifulSoup 
• OS

 9) What is SciPy and what are its functions?

SciPy is a scientific library that includes some special functions:

• It currently supports special functions, integration, ordinary differential equation (ODE) solvers, gradient optimization, and others
• It has fully-featured versions of the linear algebra modules
• It is built on top of NumPy

10) What do you mean by NumPy?

NumPy is the fundamental package for scientific computing with Python. It contains:

• Powerful N-dimensional array objects
• Tools for integrating C/C++, and Fortran code
• It has useful linear algebra, Fourier transform, and random number capabilities.

11) Explain Pandas?

Pandas is used for structured data operations and manipulations.

• The most useful data analysis library in Python
• Instrumental in increasing the use of Python in the data science community
• Used extensively for data mugging and preparation

12) How can you randomize the items of a list in place in Python?

Consider the example shown below:

from random import shuffle

x = [‘Data’, ‘Class’, ‘Blue’, ‘Flag’, ‘Red’, ‘Slow’]

shuffle(x)

print(x)

The output of the following code is as below.

[‘Red’, ‘Data’, ‘Blue’, ‘Slow’, ‘Class’, ‘Flag’]

13)  How to get indices of N maximum values in a NumPy array?

We can get the indices of N maximum values in a NumPy array using the below code:

import numpy as np

arr = np.array([1, 3, 2, 4, 5])

print(arr.argsort()[-3:][::-1])

Output

[ 4 3 1 ]

14) How make you 3D plots/visualizations using NumPy/SciPy?

Like 2D plotting, 3D graphics is beyond the scope of NumPy and SciPy, but just as in this 2D example, packages exist that integrate with NumPy. Matplotlib provides primary 3D plotting in

the mplot3d subpackage, whereas Mayavi produces a wide range of high-quality 3D visualization features, utilizing the powerful VTK engine.

15) Why you should use NumPy arrays instead of nested Python lists?

 let’s say you have a list a of numbers, and you want to add 1 to every element of the list.

In regular python, you would do:

a = [6, 2, 1, 4, 3]

b = [e + 1 fore in a]

Whereas with numpy, you simply have to do:

import numpy as np

a = np.array([6, 2, 1, 4, 3])

b = a + 1

It also works for every numpy mathematics function: you can take the exponential of every

element of a list using np.exp for example.

16) What is Pyspark?

 Pyspark is a bunch figuring structure which keeps running on a group of item equipment and performs information unification i.e., perusing and composing of wide assortment of information from different sources. In Spark, an undertaking is an activity that can be a guide task or a lessen task. Flash Context handles the execution of the activity and furthermore gives API’s in various dialects i.e., Scala, Java and Python to create applications and quicker execution when contrasted with MapReduce

Blog

Frameworks make developers’ lives easier by offering them a structure for application development. Frameworks automate the implementation of common solutions, cutting development time and allowing developers to focus on application logic instead of routine elements.

In this article, we are share a list of the top twelve Python web frameworks that will be useful on your way to becoming a professional backend developer and improving your existing skill set.

Why Python frameworks? Stack Overflow has recently released the results of their annual developer survey for 2019, which declared Python the fastest-growing major programming language.

By far, the most popular Python frameworks are Django and Flask. But that doesn’t mean you should discount the potential of other frameworks. Each framework possesses features that could be a perfect match for your web project.

Things to consider

First, When deciding which framework to use, look at the size and complexity of your project. If what you’re looking to develop is a large system packed with features and requirements, a full-stack framework might be the right choice. If your app is on the smaller and simpler side, you should probably consider a microframework.

Second, you need to check if the framework you’re considering can scale vertically and horizontally. This is a must for projects that are to run on several servers, handle huge amounts of traffic, and support the addition of new features to enhance functionality.

Once you’ve done with choosing a framework, contact a team of developers and ask them for information about the estimated cost to develop your app.

A final decision, though, should come from your own understanding of your project and the tasks you want to simplify.

However, frameworks can also stand in the way of development. When choosing a full-stack framework, you’re often signing up for a set of limitations. Of course, you can find ways to work around them, but be careful you don’t spend more time fighting for your own freedom than you would have writing an app in pure Python.

Full-stack frameworks

A full-stack framework or we can say an enterprise framework is an all-in-one solution with libraries configured to work seamlessly together. It supports the development of backend services, frontend interfaces, and databases. A full-stack framework provides anything a developer requires for building an application. Python offers more than one full-stack framework.

Django

Pyramid

Turbogears

web2py

Microframeworks

A microframework also known as minimalistic web application framework, lacks most of the functionality of a full-fledged framework. For example a web template engine, authentication functionality, accounts, authorization, input validation, and input sanitation. A microframework attempts to provide only the component set required for building an application. It may also focus on providing the necessary functionality for one particular spher

Flask

Bottle

Cherrypy

Falcon

Hug

fastAPI

Asynchronous frameworks

An asynchronous framework is a relatively recent type of Python framework. It’s a microframework which enables developers to handle a large set of concurrent connections. Asynchronous frameworks use non-blocking sockets and feed on Python’s asyncio library.

Sanic

Tornado

Others

Dash