top of page

Data Science Analytics(Indian Institute of Internship)

Apply Now

Regus - Kolkata, RDB Boulevard, BP Block, Sector V, Salt Lake, Kolkata, West Bengal, India

Job Type

Virtual or Physical or Online

About the Role

1.BE/B.Tech , BCA , MCA , BBA , MBA Regular Students from Any Recognized University
2.Minimum Class 10 & 12 - 50% Marks
3.Core Concept of Respective Subject
4.Bring Your Previous Project to Show Case Your Technical Expertise.

Need to Work on Real Life Project ; Official Letter of Appointment & Recommendation will be Issued to Each Applicant.


Module 1: Introduction to Data Science

Overview of Data Science and its applications

Key concepts and terminology in Data Science

Introduction to the data science workflow

Ethical considerations in data science projects

Module 2: Data Wrangling and Exploration

Data acquisition and cleaning

Handling missing data and outliers

Exploratory Data Analysis (EDA) techniques

Data visualization using libraries like Matplotlib and Seaborn

Data preprocessing and feature engineering

Module 3: Probability and Statistics for Data Science

Probability theory and concepts

Descriptive statistics and summary metrics

Statistical distributions and their applications

Hypothesis testing and statistical inference

Correlation, regression, and ANOVA analysis

Module 4: Machine Learning Fundamentals

Introduction to supervised, unsupervised, and reinforcement learning

Training and evaluation of machine learning models

Feature selection and dimensionality reduction techniques

Model selection and hyperparameter tuning

Model evaluation metrics and cross-validation

Module 5: Supervised Learning Algorithms

Linear regression and regularization techniques

Logistic regression and binary classification

Decision trees and random forests

Support Vector Machines (SVM)

Ensemble methods (bagging, boosting)

Module 6: Unsupervised Learning Algorithms

Clustering algorithms (K-means, hierarchical clustering)

Dimensionality reduction techniques (PCA, t-SNE)

Association rule learning (Apriori algorithm)

Anomaly detection methods

Latent Dirichlet Allocation (LDA) for topic modeling

Module 7: Natural Language Processing (NLP)

Introduction to NLP and its applications

Text preprocessing techniques (tokenization, stemming, lemmatization)

Bag-of-words and TF-IDF representations

Sentiment analysis and text classification

Named Entity Recognition (NER) and topic modeling

Module 8: Deep Learning and Neural Networks

Introduction to artificial neural networks

Feedforward neural networks and backpropagation

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs) and sequence modeling

Transfer learning and pre-trained models

Module 9: Data Science in Action

Time series analysis and forecasting

Recommendation systems

Image recognition and computer vision

Network analysis and social network mining

Reinforcement learning and Markov Decision Processes (MDPs)

Module 10: Big Data and Cloud Computing

Introduction to Big Data and its challenges

Distributed computing frameworks (e.g., Hadoop, Spark)

Processing and analyzing large-scale datasets

Cloud platforms for data science (e.g., AWS, Google Cloud)

Scalable machine learning with Spark

Module 11: Model Deployment and Productionization

Building and deploying machine learning models

Model serving using APIs and microservices

Model monitoring and performance evaluation

A/B testing and experimentation

Ethical considerations in model deployment

Module 12: Capstone Project

Working on a real-world data science project

Applying concepts learned throughout the course

Data exploration, modeling, and evaluation

Presentation of project findings and results

About the Company

Indian Institute of Internship Exclusively Registered Under Government of West Bengal for Providing Authentic & Trusted Internship for Students During Summer and Winter Session.

Apply Now
bottom of page