data science course in Hyderabad
data science course in Hyderabad

IBM Certified Data Science course with Free Internship & 100% Placement Assistance

*Data is the fuel of the 21st Century.

This advanced IBM 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 IBM 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.

Our principle of holistic development lies in the strong bedrock that believes in the amalgamation of theoretical knowledge along with practical training. This makes us the best Data Science course in Hyderabad. 

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The candidate must be pursuing a Bachelor’s degree.
Previous coding experience is an added benefit.

IBM Certified Data Science Course Curriculum (Syllabus)

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Module 1: Python Core and Advanced



  • 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


  • 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
Module 2: Exploratory Data Analysis using Python


  • 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
    Module 3: Advanced Statistics

    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


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    Module 4. SQL for Data Analysis


    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
    Module 5: Machine Learning Supervised Learning
    • 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
    • AUC-ROC
    • 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.

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    Module 6: Machine Learning Unsupervised Learning


    • 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

    CAPSTONE PROJECT: A project on a use case will challenge 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
    Module 7: Artificial Intelligence and Deep Learning
    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


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    Module 8: Computer Vision (CV)


    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
    Module 9: Natural Language Processing (NLP)


    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
    Module 10: Power BI

    Introduction To Power Bi

    What is Business Intelligence?

    • Power BI Introduction
    • Quadrant report
    • Comparison with other BI tools
    • Power BI Desktop overview
    • Power BI workflow
    • Installation query addressal

      Data Import And Visualizations

    • Data import options in Power BI
    • Import from Web (hands-on)
    • Why Visualization?
    • Visualization types

      Data Visualization (Contd.)

    • Categorical data visualization

    • Visuals for Filtering

    • Slicer details and use

    • Formatting visuals

    • KPI visuals

    • Tables and Matix

      Power Queries

    • Power Query Introduction
    • Data Transformation – its benefits
    • Queries panel
    • M Language briefing
    • Power BI Datatypes
    • Changing Datatypes of columns

      Power Queries (Cond.)

    • Filtering
    • Inbuilt column Transformations
    • Inbuilt row Transformations
    • Combine Queries
    • Merge Queries

      Power Pivot And Introduction To Dax

    • Power Pivot
    • Intro to Data Modelling
    • Relationship and Cardinality
    • Relationship view
    • Calculated Columns vs Measures
    • DAX Introduction and Syntax
    IBM certified data science course

    Languages & Tools covered in IBM Certified Data Science

    Sql Software
    Python Image
    My SQL Logo
    Jupyter Logo
    Heroku Logo

    Why Innomatics Stands Out the Best!

    Why innomatics is different from other edtech

    Why Data Science at Innomatics Research Labs?

    • 500+ Industry experts from fortune 500 companies
    • Dedicated In-house data scientist team accessible round the clock
    • 200+ Hours of intensive practical-oriented training
    • Flexible Online and Classroom training sessions
    • 5+ Parallel Data science batches running currently on both weekdays & weekends
    • Backup Classes and Access to the Learning Management System (LMS)
    • One-to-One Mentorship and Free Technical Support
    • FREE Data science Internshipon our projects & products
    • Projects and use-cases derived from businesses
    • 30+ POCsand use cases to work, learn, and experiment
    • Bi-weekly Industry connects from industry experts from various sectors
    • Opportunity to participate in Meet-ups, Hackathons, and Conferences
    • Dedicated training programs for NON-IT professionals
    • 100% placementassistance
    • Globally Recognized Certification from IBM
    Why IBM Certified Data Science at Innomatics Research Labs

    What is the scope of Certified Data Scientists in India?

    Data Science is quoted as the Sexiest Job of the 21st Century – Harvard Business Review

    According to the Harvard Business Review, Data Scientist is the sexiest job of the 21st century. Data Science has also topped LinkedIn’s Emerging Jobs List for 3 years in a row. 

    During the pandemic in 2021, there were around 82,000 job openings globally that required skills in Data Analysis and India witnessed a 45% increase in the adoption of Artificial Intelligence. 

    Therefore with each passing day, individuals and organizations are embracing digital increasing the market demand for Data scientists. The average salary of a Data Scientist in Hyderabad alone is at around Rs 6 lakhs. An entry-level Data Scientist can earn anything between Rs 5 -6 lakhs. If a candidate is willing to constantly learn, upgrade and upskill themselves, the compensation package may go up to Rs 24 lakhs or crores for that matter.

    Job opportunities (Careers) in Data Science Technology

    Data Scientists are needed for businesses in every Industry. Even tech giants such as Google, Amazon, Apple, Facebook, Microsoft are constantly in need of Data Science experts who have in-depth knowledge in data extraction, data mining, visualization, and more. Here are some leading careers in Data Science

    Business Intelligence Developer

    With an average salary of $89,333, they design and develop business strategies for quick decision-making and growth.

    Data Scientist

    With an average salary of $139,480, they explore, analyze, visualize, and organize data for the companies. They analyze the complex data sets and processes to find patterns for decision making and predicting the business and drive strategies.

    Applications Architect

    With an average salary of around $134,520, they track applications behavior and applied in the business to analyze the way they interact with the user.

    Industry Architect

    With an average salary of $126,353, they analyze the business system and optimize accordingly to support the development of updated technologies and system requirements.

    Enterprise Architect

    With an average salary of $161,323 they work with stakeholders, including management and subject matter experts (SME), to develop a view of an organization’s strategy, information, processes, and IT assets.

    Data Architect

    With an average salary is $137,630, they build data solutions that can be applied on multiple platforms.

    Data Analyst

    With an average salary of $83,989, they transform and manipulate large sets of data, which incorporate web analytics tracking and testing.

    Data Engineer

    With an average salary of $151,498, they perform real-time processing on data that is visualized and stored.

    Here are the Success Stories of our Innominions

    Frequently Asked Questions (FAQs)

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    What will I learn in IBM certified Data Science?

    In Data Science, you will learn how to find valuable data, analyze and apply mathematical skills to it to use in business for making great decisions, developing a product, forecasting, and building business strategies. 

    What is the average salary of a Data Scientist?

    Salary of a Data Scientists entirely depends on the skillset. As per the recent reports, on average a Data Scientists earn ₹14,00,000 per year.

    Are there any prerequisites to learn the Data Science course?

    One need not have any major knowledge in Data Science. A basic understanding of technology is all enough to get started. It is better to possess knowledge of mathematical and communication skills, Python, R, and SAS tools.

    What are my takeaways after completion of the Data Science course?

    Based on the program you choose, you will get a course completion certificate from Innomatics. Mastery level certification from IBM.

    What are the career opportunities in Data Science Technology?

    As data has become the never-ending part of this world, businesses need people to work with data for effective business processing. Organizations are ready to recruit and pay top dollars to the right dollars, which can leverage the business.

    Here are some of the roles you can find in Data Science

    • Research Analyst
    • Data Scientist
    • Data Analyst
    • Big Data Analytics Specialist
    • Business Analyst Consultant / Manager
    • Data analyst
    If I study Data Science course in Hyderabad, is placement guaranteed?

    Apart from the training, we do provide placement and career assistance with capstone projects and hands-on training after completing the course successfully. We do offer internship programs, mockup interviews, hackathons to gain more knowledge and explore a wide range of job opportunities.

    Will I get any career support after the Data Science training?

    All our trainees will have access to the Learning Management System (LMS), where they can get the backup classes and stay updates, 1-1 interviews, continuous updates on placements, and hackathons.

    What is the eligibility criteria to learn Data Science course?

    Anyone who has a bachelor’s degree, a passion for data science, and little knowledge of it are eligibility criteria for the Data Science Course.