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Data Science Boot Camp  
(Next Batch Starts: Tuesday, August 1st 2023

  Next Course: February 5th 2024 

   Number of Seats: 20 

   Course Fees - $3495 


   Fees Break-up (Payment Plan)

   Admission Fee: $695 (Pay online to book your seat

  • Tuition Fee (Instalment 1 - $1400 ): To be paid before commencement of course 

  • Tuition Fee (Instalment 2 - $1400 ): To be paid later, once participant secures a job**


Weeks 1 - 2

Module 1: Python for Data Science

In this module, you will work with Python syntax and execute your code using vital Python fundamentals.

  • Use Python for Data Science and Machine Learning

  • Implement Machine Learning Algorithms

  • Data exploration & analysis using Pandas and NumPy


Module 2: Data Engineering - Preparing data for Analysis

This module introduces you to the core concepts, processes, and tools you need to know in order to get a foundational knowledge of data engineering.

  • Relational and Non-Relational Database fundamentals including Database Design, Creating Schemas, Tables, Constraints, and working with Postgres and Big Query

  • SQL query language, SELECT, INSERT, UPDATE, DELETE statements, database functions, stored procs, working with multiple tables, JOINs, & transactions.

  • Data wrangling: Learn Filtering, cleaning, merging, formatting datasets and Outlier detection.

  • Webservice / microservice deployment


Weeks 3 – 4

Module 3: Data Science Foundational block

  • Basic Probability Theory, working with random variable and probability distribution, simulation of data from different probability distributions

  • Elementary matrix operations and system of linear equations, computations using Python

  • Exposure to different types of data, its visualization and summarisation using real data


Module 4: Essential Statistics

This module will provide exposure to multivariate data, extensive regression analysis and statistical inference with project based exposure using Python

  • Understanding bivariate and multivariate data along with relevant correlation measures.

  • Learn linear regression analysis, multiple regression analysis, and logistic regression using real datasets.

  • Estimation of parameters and Parametric and nonparametric testing of hypothesis in data based projects.


Weeks 5 – 6

Module 5: Artificial Intelligence (AI) and Machine Leaning (ML)

This module is designed to provide students with the essential knowledge and practical applications of ML/AI tools and frameworks needed to transition into an exciting, high-demand career.

  • Introduction to Artificial Intelligence and Machine Learning fundamentals

  • Learn ML/AI Techniques by coding in Python to create k-means algorithms and apply functions.

  • Predict outcomes using multiple linear regression models, create visual decision trees, and interpret various kinds of ML/AI decision models.


Module 6: Natural Language Processing (NLP) ChatGPT

This module provides Introduction to NLP, examine the what, why, and how of NLP, its key applications, associated challenges, current and future applications

  • Introduction to NLP with ChatGPT as a platform

  • Learn Linguistic Morphology to explore the basics of linguistics and morphology and the importance of morphology as both a problem and resource in NLP. Plus, learn to distinguish prefixes, suffixes, and infixes and how to construct a simple FST for lemmatization.

  • Classifiers POS tagging and named entity recognition and explore various applications. Evaluate the features of document topic classification and notation.


Week 7 – 8

Module 7: Exploratory Business Analytics

This module introduces time series data, trend, seasonal variation, AR, MA, ARIMA, customer churn analysis, sentiment analysis and market basket analysis.

  • Introduction to time series data and its visual representation

  • Identifying trend, seasonal variation, Forecasting future values AR, MA, ARMA, ARIMA models

  • Customer churn and Network Performance Analysis Use cases


Module 8: Visualizing Data using Tableau

This module is designed for Tableau beginners and intermediate students. Anyone who works with data, regardless of technical or analytical background as a data scientist. This course accommodates authoring in Tableau Desktop, Tableau Cloud, and Tableau Server.

  • Connect to data and edit a data source, Sort, filter, and group data and its visualization.

  • Build a range of essential chart types for analysis. Create basic calculations, including quick table calculations.

  • Build interactive dashboards to reveal data insights, share and publish visualizations.


Week 9 – 10

Module 9: Capstone Project

This is the final 2 weeks course in the Jobsbridge Applied Data Science (AI/ML/NLP) with Python Specialization. This capstone project course will give you the chance to practice the work that data scientists do in real life when working with datasets. Students can pick of the two projects based on their interest.

  • ChatGPT POC – NLP Project

  • Network Security Analytics – AI/ML/Tableau Project


Finishing School Mentorship Program

From Week 5 onwards, students will be oriented to prepare their resume, do mock-interviews, apply for jobs and even appear for interviews. Our talent and mentorship team will work closely to ensure that all students who complete the program, get adequate job and placement assistance.

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