Business Toys
Data Science Master Bootcamp
Program overview
  • 9.3 star rated Industry curriculum.
  • Aimed at undergraduate and postgraduate Engineering and Management students.
  • Hands-on learning with more than 6 Industry live projects.
  • Extensive project covering technologies like Python, SQL, Tableau, PowerBI, Advanced Excel.
  • 3 month long (100+ hours) program with daily instructor-led sessions.
  • 100% Placement Assistance, Resume optimization, Interview preparation.
Are you UP for your next career breakthrough?
1700+ Students Upskilled
Online (Instructor led) Teaching Mode
100+ Hours Duration
June 20, 2020 Start Date
90% Success Rate
Apply now
Online Mode
June 20 Start Date
Apply now

Confused where to start?

Hear is how we can help you:

Resume feedback
One-to-One career mentoring
Online Live Demo sessions
Guidance on career transitions
Walk-through program schedule
Program Highlights
100+ Hours instructor-led training
Access to LMS and Mobile App
Recorded video of each live session
100% Practical hands-on sessions
Job placement assistance
6 Industry projects
Custom made Industry curriculum
24x7 mentoring support
Target career opportunities
Jr. Data Scientist, Machine Learning Engineer, Data Analyst, Statistical Research Analyst, Business Analyst, etc.
Skills you learn
Statistical analysis, Data visualization, Machine learning, Database handling.
Ideal for
Engineers or Management graduates from any discipline.
Minimum Eligibility
Any graduates and post graduates with or without coding experience are eligible.
Technologies & Tools
Syllabus - What you will learn from this course
Preparatory course
Exploratory Data Analysis – Google Analytics Strategy
  • Data classification and different types of data
  • Analysis of categorical variables
  • Analysis of continuous variables
  • Data visualization – Histograms and boxplots
  • Correlation Analysis – Scatter plots
Essentials Statistics for Data Science
  • Introduction to statistics, Descriptive statistics – detailed understanding of central tendency, dispersion, kurtosis, skewness, normal distribution and its importance, visualizing distributions.
  • Inferential Statistics – population and sample, central limit theorem, confidence interval, discrete and continuous distributions, Hypothesis testing, t-test and different types, Anova, CHI Square test.
Compulsory submissions: 1 Project, 2 Assignments
Essentials of Programming using Python
Python for Data Science Introduction:
  • Python, Anaconda and relevant packages installations, why learn Python? Keywords and identifiers, comments, indentation and statements, Variables and data types in Python, Standard Input and Output, Operators, Control flow: if else, Control flow: while loop, Control flow: for loop, Control flow: break and continue.
Data Structures:
  • Lists
  • Tuples
  • Sets
  • Dictionary
  • Strings
  • Introduction
  • Types of functions
  • function arguments
  • lambda functions
  • modules
  • packages
Important libraries for python using Data Science:
  • Numpy
  • Pandas
  • Data Frames
  • Matplotlib
  • Seaborn
Compulsory submissions: 1 Project, 5 Assignments
Machine Learning Track - I
Introduction to Machine Learning and its types
  • Introduction
  • Different Types in Machine Learning
Linear and Non-Linear Regression:
  • Univariate regression techniques, Multivariate Linear Regression, Assumptions of linear models, Homoscedastic, Residual analysis, Normality and Multicollinearity, evaluation and validation of regression models, analysis and interpretation of regression parameters. Analysis of factors impacting the target variable.
Logistic Regression:
  • Introduction to classification problems, validation of logistic regression – confusion matrix, accuracy, precision, recall, F1 score, ROC curve, AIC and AUC.
Compulsory submissions: 2 Project, 3 Assignments
SQL for Data Science
  • SQL: Introduction to Databases, Why SQL, Execution of an SQL statement, SQL Dataset, Installing MySQL.
SQL Commands
  • USE, DESCRIBE, SHOW TABLES, SELECT, LIMIT, OFFSET, ORDER BY, DISTINCT, WHERE, Comparison operators, NULL, Logical operators, Aggregate functions : COUNT, MIN, MAX, AVG, SUM, GROUP BY, HAVING, Order of keyword.
Advanced concepts & DML
  • Join and Natural joins, Inner, Left, Right, Outer join, Subquery /Nested Queries/Inner Queries, DML: INSERT, UPDATE, DELETE, CREATE TABLE, ALTER, ADD, MODIFY, DROP, DROP TABLE, TRUNCATE, DELETE, DCL: GRANT, REVOKE, Learning resources.
Compulsory submissions: 1 Project, 4 Assignments
Life cycle of Data Science Projects
  1. Data Collection
  2. Data Pre-Processing
  3. Feature Engineering
  4. Model Building
  5. Model Validation
  6. Model Selection
  7. Model Deployment
6 Mini Projects
Projects you build with us
Project 1:
E-commerce website Analyt..
Learn about how companies are effectively using Data Science to get insights abo..
Know more
Project 2:
I-pad price prediction mo..
Ever wonder how iPad price their products?. Well this Machine Learning Hack can ..
Know more
Project 3:
Revenue forecasting of Pi..
Learn to predict future sales of pizza delivery company by using multiple foreca..
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Demo Project
Are You a Genuine Bike Buyers?
Problem statement:
A bike showroom in Bengaluru(India), was witnessing a problem with fake buyers visiting showroom for taking test rides. Due to this problem showroom executives had to do lot to useless follow-ups which were not getting enough conversions.

Our students have developed a Machine Learning model to help showroom executives to identify genuine buyers of bike which resulted in 63% more conversion rate.
Read More
      Showroom CRM
      Data Source:
      Programming Support:
      Logistic Regression
      Machine Learning Model:
      Flask | Django
      Deployment Framework:
      View Project
1700+ Students Upskilled
Career Statistics
Minimum salary
Average salary
Highest salary
Personalized Mentorship
You will be provided with your personalized success coach to make career transition or to get your first job in Data Science.
Industry Projects
You get an opportunity to work on real time Industry Projects which helps you gain practical experience and also build your Resume.
Interview Preparation
Our industry experts will prepare you to ace your interviews through resume optimization, soft-skills training, mock interviews and model Q&As.
Placement Support
Our Industry connect team continually look for companies having career opportunities & helping your Resume reach in the hands of Potential Recruiters.
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Data Scientist Certification Program
Internationally recognized certification
Unique certificate identification number for authentication
Real time validation check through website
Accepted by top multinationals
How can I join?
Online Demo
Get Mentored
Get Enrolled
Step 1
Online Demo
Step 2
Get Mentored
Step 3
Get Enrolled
Step 4
Total Investment : $  USD. 330
Total Investment
$  USD. 330
EMI Plans
Amount (USD)
Easy EMI with 0% 
On registration
1st installment - 1st month of joining
1st installment 1st month of joining
2nd installment - 2nd month of joining
2nd installment 2nd month of joining
Register Now

Still confused where to start?

Hear is how we can help you:

Resume feedback
One-to-One career mentoring
Online Live Demo sessions
Guidance on career transitions
Walk-through program schedule
Frequently Asked Questions
    Career prospects & support
      How will this program help me get a job in Data Science?
      Upskilling: Primarily, our mentors and experts continuously put efforts into building participants' resume by imparting the right skills which are required for targeted job profiles via projects. Resumes are optimized so that search appearances on job search portals increase. Students are also provided with appropriate training for interviews via personal mentoring and providing interview questions. Access to opportunities: Our HR team continuously work in tracking companies who are in hunt of candidates for desired data science profiles. Resumes of participants are then circulated to these potential recruiters, in turn, increasing their chances of getting jobs.
      Access to opportunities
      Our HR team continuously work in tracking companies who are in hunt on candidates of desired profiles. Resumes of participants are then circulated to these potential recruiters increasing their chances of getting jobs.
More questions? FAQ