Business Toys
Full Stack Data Science Professional Program
Program overview
  • Hands-on learning with 15+ Industry projects across different domains
  • Learn to build Data Science Product Architectures to solve real world problem
  • Curriculum customized for excellent professional learning experience
  • Rigorous program covering tools like Python, R,Tableau, Spark, TensorFlow, Keras, etc.
  • Project Coverage: Insurance, NLP, Computer Vision, Healthcare, etc.
  • Eligibility: Professionals with 3+ years work experience (No programming background required).
Watch Intro Video
550+ Professionals Upskilled
Online Mode
200+ Hours Duration
June 20, 2020 Start Date
83% Success Rate
Apply now
Online Mode
June 20 Start Date
Apply now
Program Highlights
200+ 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
15+ Industry projects
Custom made Industry curriculum
24x7 mentoring support
No cost EMI option
Target career opportunities
Full Stack Data Solution Architect,Data Science Lead, Sr. Data Scientist, Data Science Project Manager, Technology Lead, Manager AI, Product Specialist, etc.
Skills you learn
Bulging end-to-end Data Science enabled products powered by technologies like AWS, MongoDB, SQL, Python, Spark, R, Tableau, Django, Flask, etc.
Ideal for
Professional with 3+ years of experience in any discipline Looking to make career transition to Data Science.
Minimum Eligibility
Any Graduate | Post-Graduate | Phd. with or without technical background
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
Big Data & SQL
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
Essentials Big Data Engineering
  • Intro to Big Data, Hadoop, HDFS & MapReduce, Data warehousing with HIVE, Spark – Introduction to spark, what are RDDs? data frames & Spark SQL, Machine learning on Spark (Spark MLlib), Web scraping, Big Data Case study.
Compulsory submissions:
  • Assignment Project: Scrapping & Crawling
  • Assignment Project: NYC Parking Case Study: Apache Spark
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
TensorFlow and Keras
  • TensorFlow and Keras overview, GPU and CPU for deep learning, installing tensor flow, 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.
Long Short-term Memory (LSTMs)
  • Why RNNs? Recurrent Neural Networks, Training RNN, Need of LSTMs, GRUs, 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
15+ Mini Projects
3+ 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..
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Project 2:
I-pad price prediction mo..
Ever wonder how iPad price their products?. Well this Machine Learning Hack can ..
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Project 3:
Revenue forecasting of Pi..
Learn to predict future sales of pizza delivery company by using multiple foreca..
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1000+ Students Upskilled
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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
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Total Investment : $  USD. 1,200
Total Investment
$  USD. 1,200
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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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 in building participants resume by imparting right skills which are required for targeted job profiles via projects. Resumes are optimized so that search appearances for relevant search on job search portals increase. Students are also well prepared for their interviews via personal mentoring and providing interview questions.
      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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