Data Science

Data Science

Certified Data science training with Free Internship & 100% Placement Assistance

About The Course

This advanced Certified Data Science course in Hyderabad guarantees career transformation. Here’s a one time opportunity to learn with the best Data Science training in Hyderabad. Gain knowledge of data analytics, tools, and operations for data science certification and meet the massive demand for these skills. It is VILT & ILT training!

Here you will learn to read, analyze, clean, engineer and present data in a way that promotes the growth of your business. In order to drive data and extract significant results, this Data Science course can help you progress in leaps and bounds. This Certified Data Science training will accelerate your career as it covers relevant topics & pushes you to work on real-time scenarios.

Artificial Intelligence and Machine Learning in Data Science technology are constantly revolutionizing the industry by innovating and solving complex business problems. Innomatics Research Labs is a hub of advanced training in such technologies.

Certified Data Science Course Curriculum (Syllabus)

  • What is Python?
  • Why does Data Science require Python?
  • Installation of Anaconda
  • Understanding Jupyter Notebook
  • Basic commands in Jupyter Notebook
  • Understanding Python Syntax

  • Data Types and Data Structures

  • Variables and Strings
  • Lists, Sets, Tuples, and Dictionaries

  • Control Flow and Conditional Statements

  • Conditional Operators, Arithmetic Operators, and Logical Operators
  • If, Elif and Else Statements
  • While Loops
  • For Loops
  • Nested Loops and List and Dictionary Comprehensions

  • Functions

  • What is function and types of functions
  • Code optimization and argument functions
  • Scope
  • Lambda Functions
  • Map, Filter, and Reduce

  • File Handling

  • Create, Read, Write files and Operations in File Handling
  • Errors and Exception Handling

  • Class and Objects

  • Create a class
  • Create an object
  • The __init__()
  • Modifying Objects
  • Object Methods
  • Self
  • Modify the Object Properties
  • Delete Object
  • Pass Statements


  • Introduction to Array
  • Creation and Printing of an array
  • Basic Operations in Numpy
  • Indexing
  • Mathematical Functions of Numpy

  • 2. Data Manipulation with Pandas

  • Series and DataFrames
  • Data Importing and Exporting through Excel, CSV Files
  • Data Understanding Operations
  • Indexing and slicing and More filtering with Conditional Slicing
  • Group by, Pivot table, and Cross Tab
  • Concatenating and Merging Joining
  • Descriptive Statistics
  • Removing Duplicates
  • String Manipulation
  • Missing Data Handling


    Data Visualization using Matplotlib and Pandas

  • Introduction to Matplotlib
  • Basic Plotting
  • Properties of plotting
  • About Subplots
  • Line plots
  • Pie chart and Bar Graph
  • Histograms
  • Box and Violin Plots
  • Scatterplot

  • Case Study on Exploratory Data Analysis (EDA) and Visualizations

  • What is EDA?
  • Uni – Variate Analysis
  • Bi-Variate Analysis
  • More on Seaborn based Plotting Including Pair Plots, Catplot, Heat Maps, Count plot along with matplotlib plots.


    Regular Expressions

  • Structured Data and Unstructured Data
  • Literals and Meta Characters
  • How to Regular Expressions using Pandas?
  • Inbuilt Methods
  • Pattern Matching


  • Data Mining (WEB – SCRAPING) This project starts completely from scratch which involves the collection of Raw Data from different sources and converting the unstructured data to a structured format to apply Machine Learning and NLP models. This project covers the main four steps of the Data Science Life Cycle which involves.
  • Data Collection
  • Data Mining
  • Data Preprocessing
  • Data Visualization Ex: Text, CSV, TSV, Excel Files, Matrices, Images
  • Data Types and Data Structures

  • Statistics in Data science:
  • What is Statistics?
  • How is Statistics used in Data Science?
  • Population and Sample
  • Parameter and Statistic
  • Variable and its types

  • Data Gathering Techniques

  • Data types
  • Data Collection Techniques
  • Sampling Techniques:
  • Convenience Sampling, Simple Random Sampling
  • Stratified Sampling, Systematic Sampling, and Cluster Sampling

  • Descriptive Statistics

  • What is Univariate and Bi Variate Analysis?
  • Measures of Central Tendencies
  • Measures of Dispersion
  • Skewness and Kurtosis
  • Box Plots and Outliers detection
  • Covariance and Correlation

  • Probability Distribution

  • Probability and Limitations
  • Discrete Probability Distributions
  • Bernoulli, Binomial Distribution, Poisson Distribution
  • Continuous Probability Distributions
  • Normal Distribution, Standard Normal Distribution

  • Inferential Statistics

  • Sampling variability and Central Limit Theorem
  • Confidence Intervals
  • Hypothesis Testing
  • Z-test, T-test
  • Chi-Square Test
  • F-Test and ANOVA

  • Introduction to Databases

  • Basics of SQL
    • DML, DDL, DCL, and Data Types
    • Common SQL commands using SELECT, FROM, and WHERE
    • Logical Operators in SQL
  • SQL Joins
    • INNER and OUTER joins to combine data from multiple tables
    • RIGHT, LEFT joins to combine data from multiple tables
  • Filtering and Sorting
    • Advanced filtering using IN, OR, and NOT
    • Sorting with GROUP BY and ORDER BY
  • SQL Aggregations
    • Common Aggregations including COUNT, SUM, MIN, and MAX
    • CASE and DATE functions as well as work with NULL values
  • Subqueries and Temp Tables
    • Subqueries to run multiple queries together
    • Temp tables to access a table with more than one query
  • SQL Data Cleaning
    • Perform Data Cleaning using SQL
  • What Is Machine Learning?
  • Supervised Versus Unsupervised Learning
  • Regression Versus Classification Problems Assessing Model Accuracy


    Linear Regression

  • Simple Linear Regression:
  • Estimating the Coefficients
  • Assessing the Coefficient Estimates
  • R Squared and Adjusted R Squared
  • MSE and RMSE

  • Multiple Linear Regression

  • Estimating the Regression Coefficients
  • OLS Assumptions
  • Multicollinearity
  • Feature Selection
  • Gradient Descent

  • Evaluating the Metrics of Regression Techniques

  • Homoscedasticity and Heteroscedasticity of error terms
  • Residual Analysis
  • Q-Q Plot
  • Cook’s distance and Shapiro-Wilk Test
  • Identifying the line of best fit
  • Other Considerations in the Regression Model
  • Qualitative Predictors
  • Interaction Terms
  • Non-linear Transformations of the Predictors

  • Polynomial Regression

  • Why Polynomial Regression
  • Creating polynomial linear regression
  • Evaluating the metrics

  • Regularization Techniques

  • Lasso Regularization
  • Ridge Regularization
  • ElasticNet Regularization
  • Case Study on Linear, Multiple Linear Regression, Polynomial, Regression using Python

  • CAPSTONE PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Regression Techniques.


    Logistic regression

  • An Overview of Classification
  • Difference Between Regression and classification Models.
  • Why Not Linear Regression?
  • Logistic Regression:
  • The Logistic Model
  • Estimating the Regression Coefficients and Making Predictions
  • Logit and Sigmoid functions
  • Setting the threshold and understanding decision boundary
  • Logistic Regression for >2 Response Classes
  • Evaluation Metrics for Classification Models:
  • Confusion Matrix
  • Accuracy and Error rate
  • TPR and FPR
  • Precision and Recall, F1 Score
  • Kappa Score

  • Naive Bayes

    • Principle of Naive Bayes Classifier
    • Bayes Theorem
    • Terminology in Naive Bayes
    • Posterior probability
    • Prior probability of class
    • Likelihood
    • Types of Naive Bayes Classifier
    • Multinomial Naive Bayes
    • Bernoulli Naive Bayes and Gaussian Naive Bayes


    Decision Trees

    • Decision Trees (Rule-Based Learning):
    • Basic Terminology in Decision Tree
    • Root Node and Terminal Node
    • Regression Trees and Classification Trees
    • Trees Versus Linear Models
    • Advantages and Disadvantages of Trees
    • Gini Index
    • Overfitting and Pruning
    • Stopping Criteria
    • Accuracy Estimation using Decision Trees

    Case Study: A Case Study on Decision Tree using Python

    • Resampling Methods:
    • Cross-Validation
    • The Validation Set Approach Leave-One-Out Cross-Validation
    • K-Fold Cross-Validation
    • Bias-Variance Trade-O for K-Fold Cross-Validation

    Ensemble Methods in Tree-Based Models

    • What is Ensemble Learning?
    • What is Bootstrap Aggregation Classifiers and how does it work?

    Random Forest

    • What is it and how does it work?
    • Variable selection using Random Forest

    Boosting: AdaBoost, Gradient Boosting

    • What is it and how does it work?
    • Hyper parameter and Pro’s and Con’s

    Case Study: Ensemble Methods – Random Forest Techniques using Python


    K Nearest Neighbors

    • K-Nearest Neighbor Algorithm
    • Eager Vs Lazy learners
    • How does the KNN algorithm work?
    • How do you decide the number of neighbors in KNN?
    • Curse of Dimensionality
    • Pros and Cons of KNN
    • How to improve KNN performance

    Case Study: A Case Study on KNN using Python

    Support Vector Machines

    • The Maximal Margin Classifier
    • HyperPlane
    • Support Vector Classifiers and Support Vector Machines
    • Hard and Soft Margin Classification
    • Classification with Non-linear Decision Boundaries
    • Kernel Trick
    • Polynomial and Radial
    • Tuning Hyper parameters for SVM
    • Gamma, Cost, and Epsilon
    • SVMs with More than Two Classes

    Case Study: A Case Study on SVM using Python

    CAPSTONE PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing, and above Classification Techniques.

  • Why Unsupervised Learning
  • How it Different from Supervised Learning
  • The Challenges of Unsupervised Learning

  • Principal Components Analysis

  • Introduction to Dimensionality Reduction and its necessity
  • What Are Principal Components?
  • Demonstration of 2D PCA and 3D PCA
  • Eigen Values, EigenVectors, and Orthogonality
  • Transforming Eigen values into a new data set
  • Proportion of variance explained in PCA

  • Case Study: A Case Study on PCA using Python

    K-Means Clustering

  • Centroids and Medoids
  • Deciding the optimal value of ‘K’ using Elbow Method
  • Linkage Methods

  • Hierarchical Clustering

  • Divisive and Agglomerative Clustering
  • Dendrograms and their interpretation
  • Applications of Clustering
  • Practical Issues in Clustering

  • Case Study: A Case Study on clusterings using Python

    Association Rules

  • Market Basket Analysis

  • Apriori

  • Metric Support/Confidence/Lift
  • Improving Supervised Learning algorithms with clustering

  • Case Study: A Case Study on association rules using Python

    CAPSTONE PROJECT: A project on a use case will challenge the Data Understanding, EDA, Data Processing, and Unsupervised algorithms.


  • What are recommendation engines?
  • How does a recommendation engine work?
  • Data collection
  • Data storage
  • Filtering the data
  • Content-based filtering
  • Collaborative filtering
  • Cold start problem
  • Matrix factorization
  • Building a recommendation engine using matrix factorization
  • Case Study
  • Introduction to Neural Networks

  • Introduction to Perceptron & History of Neural networks
  • Activation functions
    • a)Sigmoid b)Relu c)Softmax d)Leaky Relu e)Tanh
  • Gradient Descent
  • Learning Rate and tuning
  • Optimization functions
  • Introduction to Tensorflow
  • Introduction to Keras
  • Backpropagation and chain rule
  • Fully connected layer
  • Cross entropy
  • Weight Initialization
  • Regularization

  • TensorFlow 2.0

  • Introducing Google Colab
  • Tensorflow basic syntax
  • Tensorflow Graphs
  • Tensorboard

  • Artificial Neural Network with Tensorflow

  • Neural Network for Regression
  • Neural Network for Classification
  • Evaluating the ANN
  • Improving and tuning the ANN
  • Saving and Restoring Graphs
  • Working with images & CNN Building Blocks
    • Working with Images_Introduction
    • Working with Images – Reshaping understanding, size of image understanding pixels Digitization,
    • Sampling, and Quantization
    • Working with images – Filtering
    • Hands-on Python Demo: Working with images
    • Introduction to Convolutions
    • 2D convolutions for Images
    • Convolution – Backward
    • Transposed Convolution and Fully Connected Layer as a Convolution
    • Pooling: Max Pooling and Other pooling options

    CNN Architectures and Transfer Learning

    • CNN Architectures and LeNet Case Study
    • Case Study: AlexNet
    • Case Study: ZFNet and VGGNet
    • Case Study: GoogleNet
    • Case Study: ResNet
    • GPU vs CPU
    • Transfer Learning Principles and Practice
    • Hands-on Keras Demo: SVHN Transfer learning from MNIST dataset
    • Transfer learning Visualization (run package, occlusion experiment)
    • Hands-on demo T-SNE

    Object Detection

    • CNN’s at Work – Object Detection with region proposals
    • CNN’s at Work – Object Detection with Yolo and SSD
    • Hands-on demo- Bounding box regressor
    • #Need to do a semantic segmentation project

    CNN’s at Work – Semantic Segmentation

    • CNNs at Work – Semantic Segmentation
    • Semantic Segmentation process
    • U-Net Architecture for Semantic Segmentation
    • Hands-on demo – Semantic Segmentation using U-Net
    • Other variants of Convolutions
    • Inception and MobileNet models

    CNN’s at work- Siamese Network for Metric Learning

    • Metric Learning
    • Siamese Network as metric learning
    • How to train a Neural Network in Siamese way
    • Hands-on demo – Siamese Network

    Introduction to Statistical NLP Techniques

    • Introduction to NLP
    • Preprocessing, NLP Tokenization, stop words, normalization, Stemming and lemmatization
    • Preprocessing in NLP Bag of words, TF-IDF as features
    • Language model probabilistic models, n-gram model, and channel model
    • Hands-on NLTK

    Word Embedding

    • Word2vec
    • Golve
    • POS Tagger
    • Named Entity Recognition(NER)
    • POS with NLTK
    • TF-IDF with NLTK

    Sequential Models

    • Introduction to sequential models
    • Introduction to RNN
    • Introduction to LSTM
    • LSTM forward pass
    • LSTM backdrop through time
    • Hands-on Keras LSTM


    • Sentiment Analysis
    • Sentence generation
    • Machine translation
    • Advanced LSTM structures
    • Keras – machine translation
    • ChatBot
    Tableau for Data Science
    • Install Tableau for Desktop 10
    • Tableau to Analyze Data
    • Connect Tableau to a variety of dataset
    • Analyze, Blend, Join and Calculate Data
    • Tableau to Visualize Data
    • Visualize Data In the form of Various Charts, Plots, and Maps
    • Data Hierarchies
    • Work with Data Blending in Tableau
    • Work with Parameters
    • Create Calculated Fields
    • Adding Filters and Quick Filters
    • Create Interactive Dashboards
    • Adding Actions to Dashboards

    Languages & Tools covered in Certified Data Science

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