Business Toys
Data Scientist Certification Program
Program overview
  • Hands-on learning with 10+ Industry projects across different domains
  • Learn to build end-to-end Data Science models to solve real world problem
  • Rigorous program covering tools like Python, R, Tableau, Spark, Keras, etc.
  • Covers problem across multiple domains like E-commerce, Retail, Healthcare, Telecom, etc.
  • Eligibility: Recent graduates & Professionals with < 2 years work exp.
Watch Intro Video
1000+ Students Upskilled
Online Mode
150+ Hours Duration
June 20, 2020 Start Date
87% Success Rate
Apply now
Online Mode
June 20 Start Date
Apply now
Program Highlights
150+ Hours instructor-led training
Access to LMS and Mobile App
Recorded video of each live session
100% Practical hands-on sessions
Personalized industry mentors
Job placement assistance
10+ Industry projects
Custom made Industry curriculum
24x7 mentoring support
No cost EMI option
Target career opportunities
Data Scientist, Machine Learning Engineer, Data Specialist, Business Analyst, Statistics Expert, etc.
Skills you learn
Machine Learning, Predictive Analytics using Python, Statistics, Data Visualization, Big Data.
Ideal for
Fresher Engineers, MBA's, Other graduates & professions with less then 2 years experience.
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 for a Mobile & Electronics company (A practical use-case)
  • Introduction to the dataset
  • Analysis and interpretations for building strategies using visualizations.
  • Histograms, Pair-plots and its limitation
  • Univariate analysis
  • Mean, median, Variance and standard deviation
  • Percentiles and Quartiles
  • Inter Quartile Range
  • Box plots with Whiskers
  • Summarizing Plots, Univariate, Bivariate and Multivariate analysis
Compulsory submissions:
  • Assignment – Conducting exploratory data analysis on Loan Default Dataset
  • Interview Preparation on Probability and Statistics – 150 + Questions Repository
Foundation to Probability & Statistics:
  • Introduction to statistics, Descriptive statistics – detailed understanding of central tendency, dispersion, kurtosis, skewness, normal distribution and its importance, visualizing distributions,
  • Different types of probability distributions, Bernoulli and Binomial Distribution, Log normal distribution, power law distribution, Box cox transform,
  • Covariance and correlation, correlation vs causation
  • 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.
ExcelR Programing
Projects / use-cases used for topic coverage:
  • Analysis of choice of server for better performance of a dating website.
  • Use cases from multiple sectors like E-commerce, retail and telecom.
Compulsory submissions:
  • Mini Project based on Probability and Statistics
  • Interview Preparation on Probability and Statistics – 100 + Questions Repository
Essential Programming Foundation
Fundamentals of Programming
  • 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.
  • Functions: 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:
  • Assignment 1: Completion of Nympy Taskbook 1
  • Assignment 2: Completion of Numpy Taskbook 2
  • Assignment 3: Pandas Taksbook 1
  • Assignment 4: Pandas Taksbook 2
  • Assignment 5: Matplotlib Taskbook
  • Assignment 6: Seaborn & Matplotlib Taskbook
  • Assignment Project: Covert regression model built in Excel to Python
Machine Learning Track - I
Regression & it’s optimization techniques
  • 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.
  • Optimization of linear models using polynomial regression, Quantile Regression, Regularization – Ridge & Lasso Regression.
  • Logistic Regression – Introduction to classification problems, validation of logistic regression – confusion matrix, accuracy, precision, recall, F1 score, ROC curve, AIC and AUC.
Python Programing
Projects / use-cases used for topic coverage:
  • Project: Performance Analysis and prediction for E-Commerce Major (HR Analytics project).
  • Project: Predicting prices of unlaunched i-pads with 95% accuracy.
  • Project: Predicting genuine buyers for a bike showroom.
Compulsory submissions:
  • Assignment Project I - Building a price recommendation engine for product category of your choice.
  • Assignment Project II – Building a fraud detection system for banks
Database Concepts
Database handling using SQL
  • 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 keywords, Join and Natural joins, Inner, Left, Right, Outer join, Subqueriy/Nested Queries/Inner Queries, DML: INSERT, UPDATE, DETELE, CREATE TABLE, ALTER, ADD, MODIFY, DROP, DROP TABLE, TRUNCATE, DELETE, DCL: GRANT, REVOKE, Learning resources.
Compulsory submissions:
  • Assignment Project: SQL Queries on complex schema design
Database handling using NoSQL | MongoDB
  • Introduction to MongoDB & NoSQL framework
  • Structure of data in MongoDB
  • CRUD Operations and Queries in MongoDB [Create, Read, Update, Delete]
Machine Learning Track II
Dimensionality Reduction Techniques
  • What is Dimensionality reduction? Row Vector and Column Vector, how to represent a data set? How to represent a dataset as a Matrix, Principal Component Analysis - Data Preprocessing: Feature Normalization, Mean of a data matrix, Data Preprocessing: Column Standardization, Co-variance of a Data Matrix.
Projects / use-cases used for topic coverage:
  • Project: Prediction on wine quality and type of wine.
Compulsory submissions:
  • Assignment Project: Optimization of property pricing model
K-Nearest Neighbor & Naïve Bayes Algorithm
  • K-Nearest Neighbor – How it works? Visual representation of KNN, Failure of KNN, Distances – Euclidean (L2), Manhattan (L1), Minkowski, Hamming, measuring effectiveness of KNN, KNN limitations, overfitting and underfitting, how to get best K value?
  • Naïve Bayes – Introduction to conditional probability, Independent and Mutually Exclusive events, Bayes Theorem, Using Naïve Bayes in best possible way.
Projects / use-cases used for topic coverage:
  • Assignment Project: Breast Cancer Detection System using KNN
  • Assignment Project: Email Spam classifier using Naïve Bayes.
Feature Engineering & Hyper-parameter optimization
  • Dealing with categorical variables – one-hot-encoding and label encoding, outlier treatment, missing value treatment – imputations strategies, handling numerical features, Multiclass classification, feature selection and feature reduction,
  • Implementation of K-Fold Cross validation and its types, Grid Search Cross Validation for hyper-parameter optimization
Projects / use-cases used for topic coverage:
  • Project: Hyper parameter optimization and balancing accuracies of Breast Cancer Detection System
Compulsory submissions:
  • Assignment Project: Credit Card Fraud Detection
Machine Learning Track III
Decision Trees & Ensemble Models
  • What is decision tree? Decision criterion for spitting: Entropy, Information Gain, Gini Impurities, constructing decision trees, splitting numerical values, Overfitting and underfitting. Solving regression problems using decision trees.
  • What are ensembles? Bootstrapped Aggregation (Bagging) Intuition, Random forest and their construction, Bias and variance tradeoff, Gradient Boosting, XGBoost, AdaBoost.
Projects / use-cases used for topic coverage:
  • Project: Optimization of accuracies for predicting wine quality and type using Decision Trees and XGBoost.
Compulsory submissions:
  • Assignment Project: Multiclass classification model for predicting classes of Iris flower
Supervised vector machines
  • Geometric representation, vector planes and selection of vector planes, loss functions, Dual form of SVM Formulation, Kernel Tricks, Polynomial Kernel, RBF Kernel, Domain Specific Kernels, SVM Classifiers and Regressors.
Projects / use-cases used for topic coverage:
  • Project: Optimization of accuracies for Credit Card Fraud Detection System using SVC.
Compulsory submissions:
  • Assignment Project: Heat Disease Detection system using all classification Models.
Machine Learning Track IV
Time Series Forecasting
  • Understanding time series data, when do we use time series? Visualizing time series components, Smoothening techniques, ARMA and ARIMA, Different types of Time Series models.
Compulsory submissions:
  • Assignment Project: Self-learning ARCH and GARCH
Model Selection & Deployment
  • Selection of best Machine Learning Model – Grid Search Optimization, Learning Curves, Analysis of learning curves to understand bias and variance in the models.
  • Introduction to flask framework, integrating flask with ML model to generate pickle files, rending APIs, building a custom HTML page for developing UI, Coupling UI with back-end. Deployment of Model on Heroku and AWS Cloud.
Projects / use-cases used for topic coverage:
  • Project 1: Deployment of end to end model
Deep Learning & AI
Model Selection & Deployment
  • History of Neural Network, How Biological Neurons work? Growth of Biological Neural networks, Perceptron, Multi-layered perceptron, Notations, Training a single-neuron model, Feed forward, back propagation, Activation functions, Gradient Descent, Bias and variance trade-off.
Projects / use-cases used for topic coverage:
  • Project: Optimization of Credit Card Fraud Detection
Compulsory submissions:
  • Assignment Project: Build ANN based model for mine detection.
  • Assignment Project: Interview Questions based on Neural Networks – 100 + Questions Repository
  • Keras overview, GPU and CPU for deep learning, SoftMax Classifier, Initialization, Sigmoid, ReLU, Batch Normalization, Dropout, MNIST Classification in Keras, Hyperparameter Tuning in Keras.
Projects / use-cases used for topic coverage:
  • Handwriting Recognition using MNIST Dataset
Compulsory submissions:
  • Assignment Project: Build ANN based model for mine detection.
Convolutional Neural Network
  • Biological inspiration – Visual cortex, convolution – edge detection on images, padding and strides, convolution over RGB images, convolution layer, max-pooling, CNN training, optimization, convolution layers in Keras, Residual Network, Inception Network, Transfer learning.
Projects / use-cases used for topic coverage:
  • Project: Classification of cat/dog images
Compulsory submissions:
  • Assignment Project: Parameter tuning for classification of cat/dog images.
Recurrent Neural Network (RNN)
  • Why RNNs? Recurrent Neural Networks, Deep RNN, Bidirectional RNNs.
  • Introduction to web scrapping using scrappy.
Projects / use-cases used for topic coverage:
  • Review classification and sentiment analysis for reviews scrapped from Redbus
Compulsory submissions:
  • Assignment Project: Scrapping reviews from website of your choice.
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
10+ Mini Projects
1+ Personalized Projects
1+ Architectural 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..
Know more
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
1000+ Students Upskilled
Career Statistics
Minimum salary
Average salary
Highest salary
Career transition
Got promotions
Average salary hike
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
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Step 1
Online Demo
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Step 3
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Step 4
Total Investment : $  USD. 800
Total Investment
$  USD. 800
EMI Plans
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
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Guidance on career transitions
Walk-through program schedule
Frequently Asked Questions
    Certification & Curriculum Related
      What is Data Scientist Certification Program?
      Data Scientist Certification Program is a 4-month rigorous online program designed specifically for freshers and working professionals with less than 3 years of work experience. The program is designed for one to start their career in Data Science, Machine Learning and Deep Learning. This program is designed and delivered by industry practitioners enabling learners to get practical exposure while getting upskilled and establish a professional network that accelerates entry into the Data Science field.
      What can I expect from this certification program?
      One can expect to work on various industry-relevant projects pertaining to data science making him/her skilled Data Science professional at par with leading industry standards.
      What is expected from learners before they get enrolled to this program?
      This program is very sophisticated and complex because of which our mentors, trainers, and industry experts put in lots of effort shaping careers of Data Science aspirants. This program is not going to be easy as it involves one to complete several assignments and projects requiring one to put at least 10 to 12 hours of time commitment per week. Learners need to be disciplined with respect to time commitments and deadlines.
      What will be the teaching learning methodology?
      This program is delivered via a blend of interactive online instructor-led live sessions from our renowned trainers and experts. Additionally, there will be some amount of self-learning topics which will be rolled out to participants by providing relevant study material in the form of books or video lectures. Moreover, we make our online classrooms energetic and vibrant via discussions, cases, competitions, Games and simulations which enhance the learning experience. For more information on our teaching methodology please click here
      I am having more than 3 years of experience? Can I still apply for this certification?
      Yes, you can; however professionals already having a good hand with programming languages like SQL, Python, Java, etc. find it comfortable to learn data science-related programming languages. More emphasis is required on making one aware of concepts like statistics, Machine learning, deep learning algorithms, etc. while providing them real-time exposure of projects. Professionals with more than 5 years of experience are already armed with domain expertise from their previously handled projects, and sometimes also have team/project lead experience. We recommend such professionals to opt for our “Data Science Professional Certification”, targeting Senior Data Scientist / Data Science lead positions by allowing them to build POCs (Proof of concept), architecture-based projects and Data Science Projects. This also allows them to aim for higher salary packages.
      How big is the batch size for online classrooms?
      Purely based on our experience in online teaching-learning pedagogy, the optimal batch size of learners is 10 to 12. We allow a maximum of 10 participants in one batch so that every participant gets personal attention and we can track the learning progress.
      Will I be granted a certificate at the end of the program?
      Post successful completion of the program, you will be awarded a certificate from Business Toys Pvt Ltd which is recognized across the globe and among top industries and will provide the validation that one has upskilled in Data Science.
    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&#39; 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.
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