Business Toys

Full Stack Data Science Professional Program

4.9 /5 ratings

This program designed specifically for working professionals having more than 3 years of work experience in any domain and are looking for career transition in data science domain. This program is designed and delivered by industry practitioners enabling learners to get practical exposure while getting upskilled to have smooth career transition.

Tools you will learn Python TensorFlow AWS Tableau SQL & more
Success rate
38 LPA
Highest Salary
12 LPA
Average Salary
6 months
Start Date

Top Rated Reviews

Mani Jacob
I always thought Data Science is the future of technology and being already experienced in IT with 10+ industry experience I could see trend moving towards adoption of Data Science and Artificial Intelligence. Business Toys provided me a really good platform by providing an access to top industry coaches and mentors to nurture learners step-by-step by going into details of every single concepts and its application. I really loved the project based training methodology which prepared my mind to think about solutions to various problems in industry. The game changer for my career was the Proof of concept (PoC) project which I did based on Natural Language Processing where I worked on multiple technologies for end-to- end integration of an application into cloud environment. I feel Business Toys delivers exactly the thing which an experienced IT professional needs through Full Stack Data Science Program to make career transition into Data Science profiles. I highly recommend Business Toys if you are looking out for making career transition to Data Science.
---- Now at ----Microsoft
Mithram Mohan
I was already having a successful IT career until I attended several events organized at our organization about how AI based automation's have started dominating over IT industry. I was working as a Senior Data Engineer and I could already experience the level of automation's taking place in Data Pipelines for Azure and AWS cloud. However, I always wondered Data Science and AI must be very complications which may make me deal with complicated mathematical approaches. Once in my casual conversation with my friend cum mentor from Business Toys where I was curing to understand some statistical concepts which I has no hope I’ll be able to understand, he thought me a very complicated concept with an ease which I could never imagine it could be so simple. It gave me a hope that I can learn data science and I joined Business Toys for its Full Stack Program. I must say it’s worth invested your time and money with them. They are true professionals and deliver what they promise. Go for this program if you are IT professional and desperate about moving to Data Science field.
---- Now at ----Capgemini
I am really glad that I attended Business Toys Full Stack Data Science Program. I believe this is one of the most comprehensive Data Science program with covers Data Science with really great depth. Trainers tirelessly lend help to every learner in training session by resolving every single doubt and cover concepts in sequence in which they are connecting. Every assignment and project was a unique learning for me and even in my job today I take references from my notes. Data Science is an ocean to lean and Business Toys training methodology largely focus in making learners learn how to self-learn new concepts from various sources like research papers, code references, open source projects, etc. Proof of concept (PoC) really helped me get a job as it was really comprehensive and I got opportunity to work on various tools and platforms like AWS, Elastic search, Natural Language Processing, Speech Analytics, etc.
---- Now at ----Amazon
Jator Godlove
I am based out of USA and I attended Business Toys Full Stack Data Science Program. Curriculum is amazing and I have myself got the curriculum validated from my mentors. Without a question, Business Toys delivered the entire curriculum covering every topic in detail. With the skills learnt I was successfully able to complete at least 10 mini projects and one POC which helped me present well with recruiters. Team also helped me with possible interview questions, prepared my resume which really helped me with the job search process. One of the biggest factor which is required for one to successfully complete the course is perseverance or commitment to stay focused and be motivated. Trainers were always approachable and did their best to help me throughout my course.
---- Now at ----Wipro, 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. To make candidates Industry-ready and to expand their range of skills, Business Toys Finishing School has designed a comprehensive and industry centered Post Graduate Program in Data Science. It is a full-time program where students will be provided in-depth, focused and practical training in Data Science for the duration of 6 months. This program is created to balance the upskilling requirements of students and the industry demands, hence helping students and professionals to make an easy transition and upgrade in their career with the desired designation, roles and salary packages

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  • 160+ Hours instructor-led online training
  • No cost EMI option
  • 100% placement assistance
  • 100% Practical hands-on sessions
  • Recorded video of each live session
  • Personalized Student Success mentors
  • Custom made Industry curriculum
  • Mock Interview preparation and Resume building
  • 24x7 mentoring support
  • 15+ Industry projects

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
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.
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
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
Deep Learning & AI
Introduction to Neural Network and Deep Learning
  • 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.
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.
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.
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.

The Projects that you build with us

Full Stack Data Science Professional Program
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Accepted by top multinationals

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