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

Data Science training in Hyderabad has become one of the most opted courses, due to demand in the innovation of existing jobs. Innomatics Research Labs at Kukatpally, Hyderabad offers you complete training in data science courses with Internship, thereby further preaching your aim towards becoming a Data Scientist. As the technological era is growing, so are the new fields in the IT sector. And especially when coming to the profession of data scientists, it has got the demand on skies.

Innomatics Research Labs has always been at the top in identifying the learner’s need, and this is why we came up with catering to the Data Science course in Hyderabad. This course raises your professional standards at work and, thus, boost your career.

Data Science Course at Innomatics Research Labs, Kukatpally & Gachibowli, Hyderabad ensures your training with top industry experts and well-trained data scientists who help you throughout the completion of your data science course. Bagging a vast experience in data science training and also training uncountable batches for years, Innomatics Research Labs has always been forward to come up with new courses for the learners.

Choose your Course of Interest

What are the tools used in Data science?

Apart from this, we also help you gain complete knowledge in various tools like R programming, Python, Statistics, Machine Learning (ML), Deep Learning, Natural Language Process (NLP), Neural Networks, and Artificial Intelligence (AI). Over a period, these tools have gained an essential place in the process of learning a data science course. Therefore, for an aspiring Data Scientist, it becomes critical to learn these tools. Also, one could quickly get access to analytical thinking as the statistical and analytical with algorithms have always been the core of the data science course.

Also, apart from being the most demanded jobs, a person who learns a Data Science course can work for different fields like a Machine learning engineer, or an Application development analyst, Full-stack engineer, Big Data developer. It has got many diversified roots in a single course.

Data science Batch details

Schedule Date & Timings
Weekday Batch 09/03/2020

8AM, 10AM, 7PM

Weekend Batch 14/03/2020


Data Scientist: The Most Demanding Job of the 21st Century

Data Science training in Hyderabad has been the most opted course by various age groups at Hyderabad. As being a major and in-depth learning course, it also consists of many different modules that couldn’t be thought by a single tutor; thus, it has been difficult to find an appropriate faculty for the course. After plentiful research, we got top academicians as our trainers with a huge experience in their credentials.

The data science course at Innomatics Research Labs starts with all the basic terminology and definite meanings. It then is followed by various theories, analyses, future predictions, R programming, big data theory, and also, we make you aware of the prerequisites of the data science training.

Innomatics Research Labs for Students

We also have a very well-defined portal for students who dream of working with giant companies. Data science course training at Innomatics Research Labs in Hyderabad has a well-equipped classroom with various requirements that fulfils the students’ needs during the completion, of course.

Innomatics Research Labs in Hyderabad also specializes in rendering students with various job-oriented courses and also dedicated placements portal to all the students. These courses include Big Data AnalyticsAmazon Web ServicesDigital Marketing, DevOps, and Investment Banking at Kukatpally, Hyderabad. We also have diversified our training field viz Offline, and Online Training, Corporate Training, and even classroom LED training.

Available Slots

Timings (Capacity) Available Seats

(15 seats)

4 seats

(30 seats)

11 seats

(15 seats)

 7 seats

Data Science Course Curriculum

1. Introduction to Data Science

  • Introduction to data science
  • Application of Data Science
  • Life cycle of data science projects
  • Data and its various forms

2. Python - Core, Advanced


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

2. Data Types and Data Structures

  • Variables
  • Strings
  • Lists
  • Sets
  • Tuples
  • Dictionaries

3. Control Flow and Conditional Statements

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

4. Functions

  • Code Optimization
  • Scope
  • Lambda Functions
  • Map
  • Filter
  • Reduce
  • Modules and Packages

5. File Handling

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

6. Python Datatime

7. Object Oriented Programming(OOP)

  • Attributes and Class Keywords
  • Constructers and Destructors
  • Using Self Parameter
  • Class Object attributes and Methods
  • Overloading and Over Riding
  • Inheritance
  • Method Overloading
  • Operator Overloading
  • Abstraction
  • Super Keyword
  • Method Overriding

3. Python for Data Analysis & Visualization

8. Numpy 

  • Arrays
  • Basic Operations in Numpy
  • Indexing
  • Array Processing

9. Pandas

  • Series
  • Data Frames
  • Indexing and slicing
  • Groupby
  • Concatenating
  • Merging Joining
  • Missing Values
  • Operations
  • Data Input and Output
  • Pivot
  • Cross tab

10. Data Visualization

  • Introduction to Matplotlib
    • Line plots
    • Histograms
    • Box and Violin Plots
    • Scatterplot
    • Heatmaps
    • Subplots
  • 11. Visualization with Seaborn

4. Understanding Text using Python

12. Regular Expressions

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



  • Data Mining

This project starts completely from scratch which involves 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 Data Science Life Cycle which involves

  1. Data Collection
  2. Data Mining
  3. Data Preprocessing
  4. Data Visualization.

Ex: Text, CSV, TSV, Excel Files, Matrices, Images


Basic Statistics Terminology

  • What is Statistics?
  • How Statistics is used in Data Science
  • What is Probability?
  • Population and Sample
  • Sampling Techniques
    • Convenience Sampling
    • Simple Random Sampling
    • Systematic Random Sampling
    • Stratified Sampling
    • Cluster Sampling
    • Variables
      • Dependent and Independent Variables
      • Qualitative and Quantitative Data
        • Categorical Data
          • Nominal
          • Ordinal
        • Numerical Data
          • Interval
          • Ratio
        • Discrete and Continuous Data

      Central Tendencies

      • Mean, Median and Mode
      • Standard Deviation and Variance
      • Box Plot and Distribution

      Basics of Probability

      • Probability vs Statistics
      • Terminology
      • Probability Rules
      • Probability Types
        • Marginal Probability
        • Joint Probability
        • Union Probability
        • Conditional Probability

      Probability Theory

      • Conditional Probability
      • Bayes Theorem
      • Confusion Matrix
      • Z-Score
      • Histogram

      Probability Distribution

      • Expectation
      • Variance of Distribution
      • Skewness
      • Kurtosis
      • Discrete Probability Distribution
        • Bernoulli
        • Binomial
        • Geometric
        • Poison
      • Continuous Probability Distribution
        • Exponential
        • Normal Distribution
        • Gaussian Distribution
        • t-Distribution
        • Confidence Interval
          • Standard Error
          • Margin of Error

          Statistical Testing

          • Hypothesis Testing
          • Chi-square test
          • t-test
          • ANOVA



  • What is Machine Learning?
  • Difference between Supervised Learning and Unsupervised Learning?
  • Difference between Regression and Classification Models?

2. Linear and Multiple Regression

  • Relationship between variables: Regression (Linear, Multivariate Linear Regression) in prediction.
  • Hands on Linear and Multiple Regression using a use case.
  • Understanding the summary output of Linear Regression
  • Residual Analysis
  • Identifying significant features, feature reduction using AIC, multi-collinearity check, observing influential points, etc.
  • Hypothesis testing of Regression Model
  • Confidence intervals of Slope
  • R-square and goodness of fit
  • Influential Observation – Leverage
  • Polynomial Regression
  • Categorical Variable in Regression

3. Logistic Regression

  • Logistic Regression Intuition
  • Understanding Logit Function.
  • Hands-on Python Session on Logistic Regression using business case.
  • Measuring the Evaluation Metrics – Confusion Metrics, Accuracy, Precision, recall and ROC Curve.

4. Navie Bayes Classifier

  • Review probability distributions, Joint and conditional probabilities
  • Model Assumptions, Probability estimation
  • Required data processing
  • Feature Selection
  • Classifier

5. Principal Compound Analysis (PCA)

  • Introduction to dimensionality reduction and it’s necessity
  • Background: Eigen values, Eigen vectors, Orthogonality
  • Principal components analysis (PCA)
  • Feature Extraction
  • Advantage and application of Dimensionality reduction.

6. Time Series (Forecasting)

  • Trend analysis
  • Cyclical and Seasonal analysis
  • Smoothing; Moving averages; Auto-correlation; ARIMA
  • Application of Time Series in financial markets

7. Decision Tree (Rule – Based)

  • Decision nodes and leaf nodes
  • Variable Selection, Parent and child nodes branching
  • Stopping Criterion
  • Tree pruning and Depth of a tree
  • Overfitting
  • Metrics for decision trees-Gini impurity, Information Gain, Variance Reduction
  • Regression using decision tree
  • Interpretation of a decision tree using If-else
  • Pros and cons of a decision tree
  • Accuracy estimation using cross-validation 

8. K-Nearest Neighbor (Distance Based Learning)

  • What is KNN and why do we use it?
  • KNN-algorithm and regression
  • Curse of dimensionality and brief introduction to dimension reduction
  • KNN-outlier treatment and anomaly detection
  • Cross-Validation
  • Pros and cons of KNN

9. Support Vector Machine (Distance Based Learning)

  • Linear learning machines and Kernel space, making kernels and working in feature space
  • Hands on example of SVM classification and regression problems using a business case in Python.

10. Esemble Methods

  • Introduction to Ensemble
  • Bias and Tradeoff
  • Bagging & boosting and its impact on bias and variance
  • Random forest
  • Gradient Boosting
  • XGBoost

Case Studies:

  • Predictive Analytics
  • Banking Use cases – Customer Service prediction,
  • Health care Use cases – Heart Disease, Diabetics
  • Insurance Use cases
  • Telecom Churn Prediction
  • Bike Sharing
  • Air Quality


1. Clustering

  • Different clustering methods
  • review of several distance measures
  • Iterative distance-based clustering
  • Dealing with continuous, categorical values in K-Means
  • Constructing a hierarchical cluster, and density-based clustering.
  • Test for stability check of clusters
  • Hands-on implementation of each of these methods in Python

2. Recommendation Systems

  • Association Rules:
    • How to combine clustering and classification;
    • A mathematical model for association analysis
    • Apriori: Constructs large item sets with mini sup by iterations
    • Metrics of rules-Lift, Support, Confidence, Conviction
  • Recommendation Rules:
    • Collaborative Filters
    • Content based Learning

8. Natural Language Processing (NLP) – Text Mining


  • What is Text Mining?
  • Libraries
    • NLTK
    • Spacy
    • TextBlob
  • Structured and Unstructured Data
    • Extracting Unstructured text from files and websites

2. Text Preprocessing

  • Regular Expressions for Pattern Matching
  • Text Normalization
  • Text Tokenization
    • Sentence Tokenization
    • Word Tokenization
  • Text Segmentation
    • Stemming
    • Lemmatization

3. Natural Language Understanding (NLP Statistical)

  • Automatic Tagging
  • N-grams Tagging
  • Transformation based Tagging
  • Bag of Words
  • POS Tagging
  • TF – IDF
  • Cosine Similarity
  • Thinking about the math behind text; Properties of words; Vector Space Model
  • Named Entity Recognition
  • Relation Extraction

4. Matrix Factorization

  • Singular Value Decomposition

5. Text Indexing

  • Inverted Indexes
  • Boolean query processing
  • Handling phrase queries, proximity queries
  • Latent Sematic Analysis

6. Text Classification

Case Studies:

  • Text Mining
  • Sentiment Analysis
  • Spam Detection
  • Dialogue Prediction

9 Artificial Intelligence

1. Introduction to Neural Networks

  • Introduction to Neural Network
  • Introduction to Perceptron
  • Activation Functions
  • Cost Functions
  • Gradient Decent
  • Stochastic Gradient Descent
  • Back propagation

2. Deep Frameworks

  • Installing Tensorflow and Keras
  • Tensorflow and Keras Basic Syntax
  • Tensorflow Graphs
  • Variables and Placeholder
  • Saving and Restoring Models
  • Tensorboard

3. Artificial Neural Network with Tensorflow

  • Neural Network for Regression
  • Neural Network for Classification
  • Evaluating the ANN
  • Improving and tuning the ANN

4. Convolution Neural Networks

  • Convolution Operation
  • ReLU Layer
  • Pooling
  • Flattening
  • Full Connection
  • Softmax and Cross Entropy

5. Building Convolution Neural Network in Python

  • Introduction to Computer Vision
    • OpenCV library in Python
  • Getting Started with Images/Videos
    • Operations on Images
  • Image Processing in OpenCV
    • Geometric Transformation of Images
      • Rotation
      • Affine Transformation
      • Perspective Transformation
    • Imaging Thresholding
    • Contours
    • Edge Detections
    • Morphological Transformation
    • Harris Corner Detection
  • Reshaping Images
  • Normalizing Images
  • Building Convolutional Network with Tensorflow
  • Training CNN for Image Classification

Case Studies:

  • Image Classification

6. Keras (Backend Tensorflow)

  • Keras vs Tensorflow
  • Introduction to Keras
  • Building Artificial Neural Network with Keras
  • Building Convolution Neural Network with Keras

7. Natural Processing Language (Sequential Process)

  • The Idea behind Recurrent Neural Networks
  • Vanishing Gradient Problem
  • LSTM (Long Short-Term Memory)
  • GRU (Gated Recurrent Unit)


  • Face Recognition

Face Recognition project gives details of the person and can recognize the gender and names. This project involves in

  1. Collection of images
  2. Preprocessing the data
  3. Applying the Model (Machine Learning or Deep Learning)
  4. Training and Testing using the model

Ex: Security Unlock, Gender Recognition, Identity Recognition 

  • Chatbot

Virtual Assistants are now a common requirement for an Organization. But, to make the assistant more effective we are now into the chatbots which involves Natural Language Process, Deep Learning and Artificial Intelligence. This interactive chatbots are designed to serve as an intellectual responsive process.

               Ex: Alexa, Siri, Google Assistant

10 Deployment

  • Creating pickle and frozen files
  • Cloud Deploying Machine Learning and Deep Learning model for production

Why Choose Innomatics Research Labs

18+ Data Science Industry experts from fortune 500 companies
Dedicated In-house data scientist team available round the clock
200+ Hours of intensive practical oriented training
Data science Internship on our projects & products
30+ POCs and use cases to work, learn and experiment
200+ professionals trained in last 3 months
5+ products already developed and ready to be deployed
8 parallel Data science batches running currently on both weekdays and weekends
Bi-weekly Industry connects from industry experts from the banking, insurance, retail and health care domain,
Project and use-case based learning to make you Industry ready
Opportunity to participate in meet-ups, hackathons and conferences
Dedicated training programs for NON-IT professionals
New batch on every Monday and Saturday for working and non-working professionals
100% placement assistance
Certified and guaranteed placements for both IT and NON-IT professionals

Why innomatics research labs for Software Training courses like Data Science, Machine Learning, Artificial Intelligence, Python, Statistics, NLP, Data Visualization with Tableau, Big Data with Hadoop, Digital Marketing & AWS training in Kukatpally, Hyderabad?

Data Science Trainee’s Reviews

(Our Students saying about Data Science training)

Vinay keerthi

Innomatics Reviews on Data science course training at Kukatpally Ameerpet, Hyderabad, India

Innomatics Research Lab is one of the best data science training institute in Hyderabad. I joined here as soon as I completed my bachelor’s to attain a great job. This team suggested me to go with a data science course, for the first two days I heard the demo and just got impressed by their teaching and with no second thought choose this course. I’m almost about to complete my course and no doubt they are Turing me into very well qualified data scientist.

Data Science trainee

PV Meenakshi

PV Meenakshi Review on Data science traiining at Innomatics Research Labs Kukataplly, Hyderabad, Ameerpet & Madhapur Branch Hyderabad

One of the best learning institute in Hyderabad I say! In every aspect Innomatics research labs Institute has proved to be the best for me. The topics and projects covered in Machine Learning were real time and it helped me understand the basics along with high level stuff, equally well. Thank you Innomatics for turning me into a Young data scientist!

Data Scientist

Sai Chaitanya

Innomatics Reaserch Labs Review by Sai Chaitanya Data Scientist Intern - Best Data Science training institute in Hyderabad

I joined Innomatics as a student and an Intern in the end of December 2018. It has been a knowledgeable experience till date. I got to work on some interesting projects and case studies as an Data Science Intern. The mentors at the institute are talented, experienced, yet friendly. They are always ready to help. It has been a great experience and I am sure I will benefit a lot from it in my career.

Data Scientist

Loka Sai Eswar

Innomatics Reaserch Labs Reviews on Data science, Data Science trainee review on Innomatics kukatpally

I came to Innomatics when i was indecisive about what to do with career. I just resigned my job as it was kind of monotonous and it didn’t interest me anymore. Just then I got a mail from Innomatics saying they offer classes on Data Science. I don’t mean to sound dramatic, but that was one of the best decisions I made. I found the career that suits me after I came here. The teaching method here is very practical and the faculty too are very helpful. I would recommend to everyone who want to change their career or who want to join Data Science to consider Innomatics research Labs. I am sure you won’t regret!

Data Science trainee

Vaishnavi Mandhani

Innomatics Reaserch Labs Review by Vaishnavi Mandhani on Data science

Innomatics Research labs are the only platform to learn Data Science with real-time working experience, they just don’t teach, let you work and gain experience. There are so many institutions charging Lakhs of rupees but not teaching complete data science. But at Innomatics, I learnt the real data science and practical application of it. After joining here only I got to know what real Data science is. 3 months back I don’t know Data science. Today I can claim that I am an expert of it. As part of my internship, I worked on Docnizer project. It gave me an enormous amount of knowledge in Data Science. Thank you Innomatics – Kukatpally Branch

Data Scientist

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