Business Toys

Data Scientist Certification Program

4.5 /5 ratings

Follow this learning path to become a certified Data Scientist. Learn the most in-demand technologies such as Data Science on R, Python and implement concepts such as data exploration, regression models, hypothesis testing etc.

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

Top Rated Reviews

Ritesh Kumar
My Name is Ritesh and I work for Kaseya as Sr. Business Analyst. My educational background is BBA and I have completed Data Science Certification Program from Business Toys. I had also completed Business Analytics Certification from Business Toys and they had helped me a job as a Business Analyst at Kaseya. After 6 months of experience I learnt that Data Science should really be helpful for me to grow in my career so I got myself enrolled for Data Science Certification Program. I started demonstrating my Data Science skills at work and my work started getting recognised by senior management. I recently got promoted to a position of Sr. Business Analyst and I am working of Data based automations at Kaseya. I highly recommend Business Toys they for any kind of upskilling programs.
---- Now at ----Kaseya
Arshdeep Kaur
I got to know about Business Toys from my friend. It’s a wonderful platform to get the best mentor support and handholding. I have completed Data Scientist Certification Program from Business Toys and course has helped me get my first job at Amazon. Mentors always provided me an unbiased and frank feedback about my learning progress which made me work on my skills in right direction. Me being from non-technical graduation background, I was initially facing difficulties with python programming but with support of trainers and extra assignments I could adapt to it. Mentors also helped me focus on my strengths and helped me complete projects in Business and E-commerce domains. Team also prepared my resume and helped me throughout the job search process. My hearty thanks to everyone from Business Toys and I recommend everyone to learn Data Science from Business Toys.
---- Now at ----Amazon
Abhishek Mishra
I have always been very passionate about Data Science and Data Analysis. In fact I love working with Data. That is why I joined Business Toys finishing school to do a professional course in Data Science. What I loved about Business Toys is that both Mani sir and Omkar sir teach practically using lots of case studies. In fact they teach every topic with cases. I also worked on many hands on industry projects like Bike showroom, Retail coffee shop, etc. I really enjoyed doing the course and what I felt was that the course is designed based on what the industry demands from data scientists. After completing the course, I got the opportunity to intern with Business Toys. I am really thankful to Business toys and the mentors for helping me with resume development and interview preparations. Now, I am working as a data analyst in Analytical wizards, US based healthcare Industry.
---- Now at ----Analytical Wizards
Yazhini TK
Business Toys team is exceptional and for me they are real heroes. I personally know they have helped many professionals and freshers including me in building their career. In my experience while taking up Data Scientist Certification Program, I found the team highly professional and ready walk an extra mile to help students. Mentors helped me throughout my process of learning and helped me overcome my weaknesses like programming, mathematical concepts, etc. Weekly assignments helped me a lot to get a proper practice and better understanding. Trainers kept me highly motivated throughout the course towards my dream of becoming Data Scientist. I do not hesitate in recommending Business Toys for learning Data Science to any professional or student who wish to get into Data Science.
---- Now at
Madhu Mauthe
I am from Electrical Engineering Background and also have completed my MBA. After I started reading and following Data Science blogs and articles, I was fascinated to enter this field. During my search process, I came across a webinar conducted by personnel from Business Toys which made me have a word with the spoke person. I checked the reviews from other who did their courses and made my decision to join Business Toys. Team is really dedicated of building success stories and make the entire process of learning very transparent and easy. I had payment issues for which I availed the zero cost EMI option which was available with the institute itself. I felt this type of training I had never underwent in my entire education and it was really worth investing the time and money with Business Toys. I highly recommend Data Scientist Certification Program to fresher who wish to build career in Data Science.
---- Now at ----icomm Technologies

Potential Recruiters


Program overview

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.

  • 150+ Hours instructor-led online training
  • 100% placement assistance
  • 100% Practical hands-on sessions
  • 10+ Industry projects
  • 0 cost EMI
  • Personalized Student Success mentors
  • 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)
  • 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.
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.
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.
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
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.
  • Keras overview, GPU and CPU for deep learning, 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.
Recurrent Neural Network (RNN)
  • Why RNNs? Recurrent Neural Networks, Deep RNN, Bidirectional RNNs.
  • Introduction to web scrapping using scrappy.

The Projects that you build with us

Data Scientist Certification Program
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Accepted by top multinationals

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Total Investment :
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