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Python Programming and Data Exploration in Python

Get ready for new-age job roles by learning the programming language that is most popular for Data Analytics - Python. A python is a powerful object-oriented programming language. It is also an interpreter compiled byte code programming language and an open-source scripting language.

New Batches available from19 June Hardware and Software Requirements

7,200/- 8200/-

( Excluding GST @ 18% )

Enroll Now

Python Programming and Data Exploration in Python

Get ready for new-age job roles by learning the programming language that is most popular for Data Analytics - Python. A python is a powerful object-oriented programming language. It is also an interpreter compiled byte code programming language and an open-source scripting language.

New Batches available from19 June Hardware and Software Requirements

7,200/- 8200/- (Excluding GST @ 18%)
  • module

    Suitable for - School and College students, and Working professionals

  • module

    42 Hours of Intensive Learning on Python Programming for Data Sciences

  • module

    Live Online sessions + App Based Learning

  • module

    42 hours

    Additional support from nearest NIIT centre

module

Suitable for - School and College students, and Working professionals

module

42 Hours of Intensive Learning on Python Programming for Data Sciences

module

Live Online sessions + App Based Learning

module

42 hours

Additional support from nearest NIIT centre

Programme Overview

Key Highlights

Learn basics and essentials of Python Programming basics.

Gain an edge by machine learning for conducting data analytics in Python.

Extensive hands-on practice on the tools.

Combination of Live Instructor classes and E-Learning for complete understanding.

LIVE Classroom Like Experience + app based learning Classroom Like Experience
Expert Faculty Expert Faculty
Faculty and Peer Chat     Faculty and Peer Chat
Multiple Learning resources - Recorded Sessions, App Learning, Quizzes and more. Multiple Learning Resources

Course Details

Programme starts with basics of Python Programming and covers the essential programming knowledge required for conducting data analysis in Python, evolving into How to work with Data in Python and applying machine learning algorithms on data for analysing and visualizing data in python. Python is one of the fastest-growing programming language in the world driven by its application in machine learning. Thanks to its versatility, it continues to reign as the tool of choice for companies for data analysis and visualization. With NIIT's python programming and data exploration programming we enable you to speak the language of data.

So, weather you're a school student in search for good grades, a college student who wants a better resume or a working professional looking to upskill, our programme is the best option for you to become an expert in python. 

Key Features

Programming in Python
Working with Data in Python
Data Modelling using Machine Learning
Data Visualization

eligibility

Qualification Academics

Knowledge of Mathematics or Statistics up to Class XII.                        

Knoweldge Additional Skills Required

Basic knowledge of working on the Windows environment and Microsoft excel.

Testimonials

See what our students have to say.


Testimonials

See what our students have to say.


NIIT Certification

On successful completion of the programme you will earn graded/participation certification from NIIT

Connect with a global network of accomplished NIIT Alumni. 

Course Module

Modules Covered :

Modules

1. Understanding python, python installation, python interface and python IDE.

2. Understanding python construct

a. Jump/branching

b. Loops

c. Functions

d. Variables and their scope

e. Modules

f. Operators and expressions

3. Importing/exporting data in python

4. Exceptions handling in python

5. Collections and dictionaries

6. Object oriented programming in python

7. Data exploration in python

a. Working with data in python

b. Data modelling using machine learning techniques

Case Study 1: On online credit card fraud detection

Description: In this case study, we will focus on a particular form of credit card fraud—buying from an online store. We are assuming that for some of those transactions (of a higher value), some retailers require the customers to call in and confirm their credit card details. Then we identify the fraudulent merchant from the data provided, In order to catch the thief you need to find the merchant to which, all the affected parties shopped at, before the first fraudulent transaction occurred against their credit card.

Dataset: The dataset consists of data for 1,000 customers and 20 merchants. Over a period of 50 days, customers made over 225 K transactions for a total value of over $57 M

Case Study 2: On classifying the outbound call data of a bank

Industry: Banking, Telemarketing

Description: In this case study we will classify the outbound calls of a bank to see if such a call will result in a credit application or not using three most popular classification methods Gradient Boosting Naïve Bias, Generalized Linear Model and Random Forest. We will compare the performance of these methods using various performance and cost metrics for example, precision, recall, F1-score and Receiver Operating Characteristic (ROC).

Dataset: We will use the data related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The dataset has 45211 records across 17 attributes ordered by date (from May 2008 to November 2010).

Case Study 3: On Forecasting River flow using Time Series models

Industry: Natural resource management

Description: We will see various techniques of handling, analyzing, and building models for time series data. We will use the autoregressive moving average (ARMA) model and its generalization—the autoregressive integrated moving average(ARIMA) model to predict the future from time series data.

Dataset: The datasets for this chapter come from the web archive of monthly river flows where in all the time series data is in chronological order (reading across).The river flow data units of measurement are cubic meters per second.

Case Study 4: On Price Distribution Analysis of Sacramento’s Houses.

Industry: Real Estate, Sales

Description: In this case study we will process real estate transactions data of houses sold in Sacramento by imputing missing observations and normalizing and standardizing the features. Then we will investigate the correlations by calculating the Pearson, Kendall, and Spearman correlation between the features of interest. Lastly we will visualize the interactions between interesting features by creating, displaying, and saving histograms.

Data-set: The Data set used consists of 985 real estate sales transactions took place in the Sacramento area over a period of five consecutive days.

FAQs

Who should join this course?

Undergraduates and graduates looking to enter the domain of data analytics and wanting to become hands-on with Python programming, understanding machine learning algorithms and building machine learning models in python.

 

Importance of Python programming language?

Programme starts with the basis of python programming and progresses towards imparting in-depth knowledge of python programming required for conducting data analysis in python, evolving into working with data in data analysis in python.

What learning resources are available?

Expert Faculty: Experienced faculty in related field interacting with students. Apart from guiding students on concepts and its implementation, he will pose challenges to learners to think through all the topics leading to better learning and increased retention.

Study Material: Students will be provided with advanced study materials in form of e-books.

Hands-on Learning: Online and app based hybrid learning: 42 hours of live instructor session + 42 hours of online practice exercises + weekly quizzes on app.

 

What topics are covered as a part of the curriculum?

Python Programming:

  • Getting started with Python
  • Type variables and operators
  • Strings, lists, dictionary
  • Control statements and loops
  • Functions and scope of variable
  • Module and package
  • File handling and exceptions
  • Collections
  • Class and objects

Data Exploration in Python:

  • Preparing the data
  • Exploring the data
  • Classification techniques
  • Clustering techniques
  • Regular dimensions
  • Regression methods
  • Time series techniques
  • Visualization in Python

What certificates will I be receiving for this course?

1 Graded Certificate

Only students successfully completing the programme with a CWAP of >=50% will be awarded the Graded Certificate in “Python Programming and Data Exploration in Python"

2 Participation Certificate

Students who do not appear for the appraisals or score a CWAP of < 50% will be issued only a Participation Certificate in “Python Programming and Data Exploration in Python”.

What will be my takeaway from this course?

At the end of this program, the learner will be able to:

i. Getting hands-on with Python Programming

ii. Analyze data sets in Python

iii. Understand machine learning algorithms

iv. Create machine learning models in Python

v. Compare and implement machine learning models

vi. Visualize data in Python

 

Why NIIT?

Over 38 years of training expertise with a learner centric pedagogy ensures NIIT students are the first choice of top I.T. companies.

INDUSTRY MAPPED CURRICULUM

PRESENT ACROSS 30 COUNTRIES

35 MILLION+ LEARNERS WORLDWIDE

INDIA'S MOST TRUSTED EDUCATIONAL BRAND

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