Business Toys

PG Program in Data Science
with Integrated Internship

4.8 /5 ratings

This Program is custom tailored for learners who are starting to seeking for a career in Data Science from technical as well as non-technical background. PGP in Data Science also integrates 2 months Industry Internship to equip the learners with real-time industry experience.

Tools you will learn Python TensorFlow AWS Tableau SQL & more
Success rate
16 LPA
Highest Salary
8.5 LPA
Average Salary
8 months
Start Date

Top Rated Reviews

Sunil Chaudhary
I joined Business Toys after completing my BE in Electronics and Telecommunication Engineering. In my experience this is one of the best Data Science Training Program, as it really help me to get my first job as a Jr. Data Scientist cum Data Analyst with Analytics Wizards. I'm really impressed with the personalized mentoring approach allowing learners to learn at their own pace and grasp the Data Science Concepts in best possible way. I highly recommend Post Graduate Program in Data Science for Data Science Beginners.
---- Now at ----Analytical Wizards
Aman Singh
The Integrated 2 months Internship is the best part of Post Graduate Program in Data Science. I started my Data Science learning journey coming from Electrical Engineering Background. The way I got trained on especially the programming aspects was unbelievable, as I always had this fear of learning programming languages. Through a unique and Innovative problem driven Training Methodology trainers made it look like a cake work.
---- Now at ----Technosoft Global
Vaibhav Keskar
I learnt about Business Toys from a friend of mine who had also completed his PG Program from here. Since I was coming from Mechanical Engineering Background I had no clue about what Data Science and Machine Learning is. With wonderful mentors cum trainers at Business Toys the journey was seamlessly smooth with me completing more then 20 real time projects, along with an Industry Internship with really helped me build my confidence with respect to Python Programming and Data Science Concepts. Mentors also built my resume which I think I would have never Imagined. With series of mock Interview preparations before every Interview I was able to crack my first job with Herman International as Decision Scientist for Healthcare domain.
---- Now at ----Herman International
Natasha Saxena
I still remember the days during my PG Program in Data Science with Business Toys. I must say days full of existing sessions with real-life Industry Projects the solutions for which you will never find anywhere on Internet. I still carry those learnings in my current job at ZS Associates as Data Scientist. I thoroughly enjoyed every session, every assignment and every project that we did while perusing the course, which were full of challenges, critical thinking and trill. I highly recommend Business Toys if you wish to start your career in Data Science.
---- Now at ----ZS Associates
Lew Flauntha
I came from Marketing background with literally no experience in coding and technology, however I always passionate about making decisions with data and facts. I had previously enrolled for couple of online MOOCs from well known education providers however didn't find much interest as they were going above my head. With lost hope in online programs I just gave it a final try by attending a free webinar conducted by Business Toys which changed my mind set for good. Right from day 1 of me attending my PG Program till the last mentoring session, every moment was full of beautiful learning experiences and unmatched quality case studies and projects. Moreover, during my interview with Kohl's Team really helped me with every interview round.
---- Now at ----Kohl's, USA

Potential Recruiters


Program overview

Technology is ever-changing and so are the industrial demands. People who are resistant to change and unwilling to expand their skill-sets have a high risk of losing their jobs. Now is the right time to upgrade your skills and learn new technologies because Industries are openly absorbing talent that is well-versed with the latest Technologies, i.e. Data Science, Big data, Data Analytics, etc..

To make candidates Industry-ready and to expand their range of skills, Business Toys has designed a comprehensive and industry centered Post Graduate Program in Data Science. This Program is custom tailored for fresh graduates, post graduates, and professionals with complete non-technical education and professional background. Program integrates end-to-end needs of Data Science profiles to make learners industry ready with comprehensive and extensive trainings in essential tools and programming languages like Python, Tableau, PowerBI, Microsoft Excel along with comprehensive coverage of Statistics, Machine learning and Deep learning concepts. We have also integrated 2 months of guaranteed online internship program to equip the learners with real-time industry experience.

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  • 200+ Hours instructor-led online training
  • 100% placement assistance
  • 100% Practical hands-on sessions
  • 15+ Industry projects
  • 0 cost EMI
  • Compulsory 2 Months Interneship
  • Recorded video of each live session
  • Custom made Industry curriculum
  • Mock Interview preparation and Resume building
  • 24x7 mentoring support

Syllabus - What you will learn from this course

Preparatory course
Exploratory Data Analysis for a Mobile & Electronics company (A practical use-case)
  • Understanding problem statement
  • Understanding types of Data
  • Analysis of categorical data
  • Univariate Analysis
  • Histograms, boxplots, density plots
  • Cross Tables
  • 3M Analysis of numerical data
  • Concept of percentiles and ranges
  • Univariate analysis and multivariate analysis
  • Creating dashboards using Excel.
Introduction to statistics and 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
Python for Data Science
Introduction to python IDEs
  • Anaconda Distribution : Jupyter Notebook, VSCode, Spyder
Introduction to python
  • Why learn python
  • keywords and identifiers
  • commends & amp; indentations
  • variables
  • data types operators
Control flow
  • If and else statements
  • While Loop, For Loop
  • Break and continue
Data structures
  • Lists
  • Tuples
  • Sets
  • Dictionary
  • Strings
  • Introduction to python user defined functions
  • Lambda functions
  • Modules
  • Packages
  • Exception handling
  • Debugging
Numpy and Pandas
  • Why Numpy and pandas
  • different operations
  • functions associated with Numpy and Pandas
Other important packages
  • Matplotlib, seaborn, scipy, sys, etc.
Machine Learning Track - I
Supervised Machine Learning
  • 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 and Lasso Regression.
  • Logistic Regression – Introduction to classification problems, validation of logistic regression – confusion matrix, accuracy, precision, recall, F1 score, ROC curve, AIC and AUC.
Machine Learning Track II
Decision Trees
  • What is decision tree ?
  • Decision criterion for spitting: Entropy, Information Gain, Gini Impurities, constructing decision trees, splitting numerical values, Overfitting and under- fitting.
  • Solving regression
  • problems using decision trees.
Ensemble Techniques
  • What are ensembles?
  • Bootstrapped Aggregation (Bagging) Intuition
  • Random forest and their construction, Bias and variance trade-off
  • Gradient Boosting
  • XGBoost, AdaBoost
Support Vector Machine
  • 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 Repressors
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 under-fitting
  • 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
Machine Learning Track III
  • Concept and intuition behind clustering
  • Significance of clustering in Data Science
  • Different types of clustering: K-Means Clustering
  • Agglomerative Hierarchical Clustering, Mean Shift clustering
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 Pre-processing
  • Feature Normalization
  • Mean of a data matrix
  • Data Pre-processing: Column Standardization
  • Co-variance of a Data Matrix
  • Concept of Linear Discriminant Analysis (LDA)
  • advantages and disadvantages of PCA and LDA
Recommendation Engine
  • Intuition behind concept of recommendation engines
  • Non-Negative Matrix Factorization (NMF)
  • Decomposition of matrix
  • concept of similarities - Euclidean distance and cosine similarity
  • optimization of time complexity in recommender systems
Machine Learning Track IV
Feature Engineering
  • Dealing with categorical variables – one-hot-encoding and label encoding, outlier treatment
  • missing value treatment – imputations strategies, handling numerical features, Multiclass classification, feature selection, feature reduction
Hyper-parameter Optimization
  • K-Fold Cross Validation and Stratified K-Fold Cross Validation
  • Understanding hyper-parameters for different Machine Learning Algorithms
  • Grid Search Cross Validation
  • Randomized Search Cross Validation
Model Selection
  • Selection of best Machine Learning Model – Time complexity approach, Bias and variance trade-off, Learning Curves, Analysis of learning curves to understand bias and variance in the models
Model Deployment
  • 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.
Deep Learning & AI
Artificial Neural Networks
  • 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
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
  • Hyper-parameter Tuning in Keras
Convolution 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
Recurrent Neural Networks and Long and Short-term memory Neural Networks
  • Why RNNs?
  • Recurrent Neural Networks
  • Training RNN
  • Need of LSTMs
  • GRUs
  • Deep RNN
  • Bidirectional RNNs
Integrated Internship Program
  • Business Toys will provide you an opportunity to work with Companies who develop AI solutions as a Intern for 2 months. This could be an Online or Offline Internship based on the preferences of companies. Candidates will have to under go Interview or an assessment test based on requirement of company.

The Projects that you build with us

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PG Program in Data Science
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