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Comprehensive Data Science Program

Data Science is an interdisciplinary field for engineering and management students or professionals. It analyses and extracts insights from the scientific data.


InnoLabZ learning programme in Data science includes 120-hour of classroom-training and 12-hours of project work. It would help a professional develop his core competency in all the eight tools of Data Science. The course is designed with a weekly 10-hour study schedule and also provides 6 weeks of training in Industrial Project (CAPSTONE). It would also have a brief introduction to Machine Learning. It focuses on Real-Time Problem Solving & is designed for workplace success. We provide 100% placement assistance and guidance for Interview Preparation.

Weekend Classes are also available for Working Professionals or Students engaged elsewhere.



Introduction to Analytics


  • Relevance and Need for Learning Analytics
  • Tools for Performing Data Analysis 
  • Excel, R, SQL, Python, SAS 
  • Importance of Visualization and Reporting 
  • Shiny, ggplots 
  • Understanding Data Analytics, Big Data Analytics, Data Science and Role of Machine Learning and Artificial Intelligence? 
  • What to expect aer you complete this module?
  • Career Paths
  •  Next Steps


Understanding Data and Its Various Types


  • Reading/Writing Data from CSV, Excel Sheets, Databases(MySQL, HIVE, etc), HDFS files
  • Reading/Writing Data on Advanced Big Data Storage formats(JSON, AVRO, PARQUET, ORC, etc)
  •  Structured Data
  • Unstructured Data (Web Scrapping)
  • Working with Continuous Variables, Categorical Variables
  • Tabulating Data
  • Descriptive Statistics
  • Mean, Median, Mode, Quintiles, Std. Deviation
  • Data distributions: Uniform, Normal, Log-Normal
  •  Finding Outliers
  • Treating NULL/Missing Values
  •  Should we really need to remove outliers? Is it always necessary?
  • Understanding Continuous data with Visualization/Graphs/Plots Boxplots
  • Violin plots, Scatter Plots, Histograms, Ogive graphs
  • Importance of Scaling/Normalizing Data Hands-on (Playing with Data)


Introduction to Analytics using R/Excel/Python/SAS (Application side)


  • Integrating R with Excel, Tableau or Power BI 
  • Relationship among variables (univariate, multivariate, multi- colinearity)
  • Method of Least squares and Curve Fitting 
  • Fitting Linear Relationship 
  • Understanding and Fitting Non-linear Relationship
  • Building regression model (With Example) 
  • Splitting Samples into Train and Test 
  • Importance of Cross-Validation Sets 
  • Model Selection 
  • Prediction and Quality checks(Performance Metrics) 
  • Modeling Binary Classifier 
  • Logistic Regression 
  • Neural Networks 
  • SVMs Decision
  • Trees Random Forests
  • Handling Unlabeled Data 
  • Cluster Analysis (k-Means, Gaussian Mixture Models) 
  • Factor Analysis Rule Mining (Apriori, FP-Growth) 


Introduction to Probability


  • Sample space and Events
  • Axioms of Probability 
  • Conditional Probability 
  • Bayes’ Theorem 
  • Random Variables 
  • Discrete Random 
  • Variables Binomial, Poisson, Geometric, Negative Binomial, Hypergeometric , Multinomial 
  • Continuous Random Variable Uniform, Normal, Exponential, Poisson, Gamma
  • Nonhomogeneous Poisson Process 
  • Expectation and Variance Law of Large Numbers (Central Limit Theorem)
  • Hands-on (Playing with Random Numbers and Distributions)


ML Theoretical Side


  • Gradient Descent(Batch, Stochastic) implementation 
  • Maximum Likelihood Estimation and Expectation Maximization 
  • Bayes Theorem 
  • Naïve Bayes and others 
  • Back Propagation 
  • Improvements in Back Propagation
  • Deep Learning(Autoencoders, RBM, etc) 
  • K-Means, K-Mediods implementations 
  • K-NN implementation 
  • SVM 
  • Ensemble Methods
  • Different Metrics Total 


InnoLabZ will award an industry-recognised certificate to the students attending and completing the 'Comprehensive Program in Data Science'.

Course Fee : ₹ 75000


For Corporate & Bulk Booking

Any Corporate, Schools and Colleges are eligible for this course.

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