Python Programming and Data Exploration in Python


  • Total Duration 44 Hours
  • Course Fees Actual Fees may vary, depending upon your centre selection. 7,000

    ( Excluding GST @ 18% )

  • Course Type Classroom + Online

Classroom + app based learning

Industry Experience Faculty

Faculty Guidance through app

35 mn learners worldwide

  • Learn one of the most popular tool for data analytics
  • Learn Python Programming basics and essentials, along with machine learning for conducting data analytics in Python
  • Hybrid Learning with Guided practice & Weekly Practice quiz questions on the app along with the classroom Sessions
  • Hands-on application of the Tools
  • App based learning. Connect with Faculty on the App apart from the regular classroom training.


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Program 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.

  Learning Objectives

1. Understanding Python, Python Installation, Python Interface and python IDE

2. Understanding and working with Python Constructs

        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 Modelling using Machine Learning Techniques

8.Data Visualization

Modules Covered:

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

Case Studies

1. Case Study on online credit card fraud detection

Industry: Banking, Finance and Economics

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.

2. Case Study 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).


3. Case Study 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.

4. Case Study 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.

  • Have basic knowledge of working in the Windows environment and Microsoft excel
  • Knowledge of Maths/Statistics upto Class XII


Rating & Reviews


44 Ratings & 5 Reviews

“Blessed to get a good instructor like Hariharan sir. Course was very engaging.”

"Python is a language with too much fun... and I love this course...."

"Valuable programme that helped me in enhance my knowledge and skills in python."

"One of the best course... seems to be very useful and relevant in today's time."

"Experienced Faculty and in depth knowledge of python !! The way she manages the class was awesome !! She had a friendly nature which make us feel comfortable in asking doubts."


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.


Why is this course Unique?

1. Unique Curriculum: The course is designed to make one proficient in data analytics in Python starting from the fundamentals and going onto application of Python which is one of the most widely used tools in the industry.

2. Extensive hands-on skills: The student will gain competence through hands-on learning in the latest technologies and tools, which are being widely used in the Data Analytics industry.

3. Online learning: Apart from the regular ILT classroom sessions, the students will have access to online learning and practice on and NIIT student app.

4. Expert Faculty: Faculty with experience of working in the Data Analytics industry will deliver the program. This will help the student understand the working environment and learn to apply concepts

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: Apart from classroom hours, learners will be provided with dedicated machine room hours to practice their learnings in Python.

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 program 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


Appointment with counsellor