Online Data Science Course training | Hyderabad, India

What is Data Science?

One of the most booming career opportunities of the 21st century. Data science is all about mining the hidden data within the customer’s search pattern and using it for a more personalized search result. These results help to grow enterprises with an exponential level of profits. the person who deals with data science is generally known as a data scientist.

Online Data Science Course and It’s Benefits

The data science online training in Hyderabad is not for everyone. This course is only for the people who are really passionate about data science and seek a career in this field. In recent years, the business organizations have realized the value of the past data from their customers and that is why coming up with a module to use that data is very important. The benefit of having your data science course with us, is that you will get hands-on with the real-world scenario.

Advantages of Online Data Science course

Here is a list of some of the few advantages of having an online data science course with us:

  • High on Demand: Data science right at the moment is high on demand; therefore, prospective job seekers can have a huge number of opportunities ahead. By the end of 2026, it is predicted to create 11.5 million job opportunities.
  • A Highly Paid Career: Another reason for opting online data science course is due to the high income. That is why data science has become a highly lucrative career option.
  • Versatility: Once you have completed your training, you will understand that there is a very wide field for data science. Furthermore, some of the applications for data science includes in the health-care, marketing, banking, consultancy, and e-commerce industry.
  • Highly Prestigious: Since a company’s decision making is based on the data scientist, data science is a highly prestigious job. Not only, you will be getting a better pay grade but also huge respect, which everyone strives for.

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Why Choose Us

There are several other online data science courses in India but what makes us unique is the love and effort we put forth for our studies, besides we don’t entertain the idea of earning while manipulating our students. Online data science course certification is always free, and it comes with the package. Even after the completion of the course, free job assistance is provided by us.

We have not become the best data science online training Hyderabad overnight. First of all, we offer online mock tests and mock interviews to help our students hone their skills when in need. There are also sample projects to practice for our students, along with free technical support, even after the course is completed. We believe through our online data science course in giving all our students a placement opportunity, and until that is done, we prefer not to take rest. 

Key Features

60+ use cases

60+ Use cases are developed & ready to deploy

24/7 support

24/7 Support on Learning Management System

Flexible Timings

Flexible timings for working professionals

Placement Assistance

100% placement Assistance after completion of course

Flexible payments

Flexible Payments with Easy Installments

Life-Time Free Access

Life time Free access to Workshops & Seminars

Online 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

1. INTRODUCTION 

  • 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

 

Projects

  • 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

5. Statistics - DESCRIPTIVE & INFERENTIAL

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

6. MACHINE LEARNING – SUPERVISED LEARNING

1. INTRODUCTION

  • 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

7. MACHINE LEARNING – UNSUPERVISED LEARNING

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

1.  INTRODUCTION

  • 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)

 Projects

  • 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 Banking, insurance, retail and health care domain etc.,
 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?

FAQs on Online Data science Training

Who is innomatics?
The Innomatics research lab is one of the most prestigious institutes in Hyderabad. We offer various job oriented courses, including big data analytics, AWS, digital marketing, Blockchains, Devops, data science, and so on.

Why choose an online data science course?
The best reason one could say to choose online data science course that is specially provided by innomatics is due to the flexibility of it. The online course for data science is so intuitive that you will enjoy it while doing it. Another thing is the convenience of learning at your home with the same quality that could be on-campus.

Is my placement guaranteed?
According to our policies, and the rules and regulations set up us by the government, guarantee can’t be given in context to placements. With our online course on data science, you will be groomed in such a way that is worrying about placement won’t even come to your mind. You can easily opt for any data science job wherever you like.

What is the valuation of the certification?
Our online data science certification is recognized in all of the prestigious and renounced data science organizations. So, don’t worry about the certification, once you score with a bright color, your certificate will have a significant value on its own.

What is the format of the data science course?
The online data science training is composed of 200 plus hours of on-demand videos. We are the best when it comes to online courses in Hyderabad. We also offer a 100 percent guaranteed internship, along with a professional level skill development program, so that our students can get placement easily.

How reliable are online training courses?
Well, if you are skeptical about online data science courses, then let us make you assured. The only difference between an online course and offline is your choice of where do you want to study. None of the quality of education is compromised when it comes to online training courses. Along with being cheaper than the offline courses, you will be getting the same training as that of on-campus.

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