Summer Industrial Training and Internship Program
Data Analytics

Eduvance conducts a 30-day Summer Industrial Training and Internship Program (SITIP 2021) on Data Analytics.

The focus of this internship is to give the student a hands-on experience to one of the hottest technologies that is revolutionizing the computer industry. Data analytics is a gateway to the advanced domain of Artificial Intelligence which is utilized in almost all industrial as well as commercial applications around the world.

This internship will focus on concepts of Python programming, Data Filtering and Analysis, Machine Learning fundamentals, different algorithms and projects based on them. The internship will be conducted on the Project and Outcome based Methodology with hands-on labs.


  • The training will be for a total of 120 hours of duration (4 hours per day with alternate lab sessions).



Module 1

  1. Basics of Python Scripting

1.1 Basics of Python
1.1.1 Interpreter vs compiler
1.1.2 Basic Python syntax
1.1.3 Data types conversion
1.1.4 Native data types of Python – lists, dictionaries, tuples

  1. Conditional Statement in Python

2.1 Conditional execution
2.1.1 If else
2.1.2 for
2.1.3 while

Module 2

  1. Modular programming in Python

3.1 Functions in python

             3.1.1 Creating user defined functions

            3.1.2 Calling functions

  1. Advanced scripting techniques in python

4.1 List comprehensions in Python

4.1.1 Using functions in Python (variable inputs also)

4.1.2 Use of one line for loops

4.1.3 Lambda, reduce, map functions

4.2 Exception Handling (optional)

4.2.1 Try/Except

4.2.2 Custom Exception method

            4.3 File I/O(optional)

Module 3

  1. Understanding Multidimensional Arrays

5.1 Introduction and installation of numpy

5.2 Array creation

5.3 Array indexing

5.4 Array slicing

5.5 Boolean indexing (optional)

5.6 Mathematical operations on matrices

  1. Data visualization in Python

6.1 Installing matplotlib

6.2 Matplot object api – axes, figure objects

6.3 Subplot nrow and ncols

6.4 Legend labels and titles

6.5 Setting colors, Linewidths and Linetypes

6.6 Axis range and Axis grid

6.7 Visualization matplotlib – 2d scatter plot, Bar, Histogram

Module 4

  1. Understanding Data Analytics

7.1 Creation of Dataframe with Pandas

7.2 Indexing Dataframe with pandas

7.3 Indexing using labels in pandas

7.4 Pandas series objects

7.5 Pandas Dataframe operations

7.6 Boolean indexing with pandas

7.7 Pandas plotting

7.8 Missing values-data refining


Module 5

  1. Introduction to Machine Learning

8.1 Components of Artificial Intelligence

8.1.1 Machine Learning

8.1.2 Deep Learning

8.2 Classification of Machine Learning

8.2.1 Association Learning

8.2.2 Supervised Learning

8.2.3 Unsupervised Learning

  1. Association Learning

9.1 Market basket analysis

9.2 Apriori algorithm

9.3 Creating Grocery Cart Application

Module 6

  1. Supervised Learning – Regression

10.1 Linear Regression

10.1.1 OLS method

10.1.2 Lab based on OLS Method

10.1.3 SGD method

10.1.4 Lab based on SGD Method

10.2 Polynomial Regression

10.2.1 Lab based on Polynomial Regression

  1. Supervised Learning – Classification

11.1 Perceptron

11.1.1 Lab based on Perceptron

11.2 Support Vector Machine

            11.2.1 Lab based on Support Vector Machine

11.3 Decision Tree

11.3.1 Lab based on Decision Tree

Module 7

  1. Unsupervised Learning – Clustering

12.1 K-means Clustering

12.2 Elbow Method

12.3 Lab based on K-means

Module 8

  1. IBM Cloud Fundamentals

13.1 Setting up the IBM Cloud

13.2 IBM cloud services

14: IBM Watson Fundamentals

14.1 Understanding IBM Watson machine Learning architecture

14.2 Provisioning different services useful for ML application

14.3 Machine Learning Implementation on IBM Watson and Cloud Platform

14.3.1 Lab: Working with IBM Datasets

14.3.2 Lab: Building regression-based models with IBM Watson services

14.4 Implementing case study on IBM Cloud Platform

14.4.1 Lab: Interfacing with IBM machine learning API

14.4.2 Lab: Calculating performance and accuracy


  • Students of 2nd, 3rd and 4th year of Electronics, Electronics and Telecommunication, Information Technology, Computer Science, Biomedical Engineering and Electrical Engineering students of B.Tech are eligible for the training program.


  • Participants will learn Python programming from basics.
  • Participants will learn different Machine Learning algorithms and their applications.
  • The training program includes hands-on labs on Data Analysis and Machine Learning resulting in exploration of concepts.
  • Participants shall become ready to solve challenges from an industry-based perspective.


  • Eduvance certificate on Machine Learning using Python
  • Internship letter from Eduvance


  • Minimum – 40
  • Maximum – 80


  • 5,000/- per student (including 18% GST)


The E-certificate will be provided on successful completion of the programme.

About Eduvance

Eduvance is founded by Ph.D. alumni from U.S. universities. We are dedicated towards providing participants with state-of-the-art training in technology.  We do this by introducing participants to projects and research topics that are at par with leading universities in the United States. This will make the participants technically sound and prepare them for future challenges. We assist participants write and present technical papers. We are advised by U.S. based researchers, faculty and industry professionals that have a proven track record in their field.

Industry Affiliations