Best Data Science Course Training in Hyderabad


Take control of your career and stand apart from the crowd with Codegnan’s data science training in Hyderabad. Master the key concepts of data analysis and machine learning with the guidance of the topmost industry professionals in India. We have helped 2,700+ of our students secure jobs in leading multinationals. Enroll now to become one of them too!

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Data Science Course Overview and Key Features in Hyderabad


Codegnan stands at the helm of premium data science training institutes in Hyderabad. Our sophisticated online and offline data science training will offer you hands-on learning experience with the help of carefully designed curriculum and real-world projects. Not just that, you will receive complete interview assistance from our expert mentors. We make sure that you are already a data science professional before even stepping into the job market. Some top highlights of our intensive course are: 

Career Scope and Job Opportunities in Hyderabad for Data Scientist


Data is the fuel of today’s industry-centric world. Businesses want to grow higher than ever and analytics helps generate insights that optimize this growth. Hence, the need for data science professionals is at its zenith. With so many MCs and startups established in Hyderabad, it is no doubt that career opportunities in data science and machine learning there are soaring high.

1. Career Scope

According to a report by Markets & Markets, the market size of data science platforms is forecasted to grow at a 27.7% CAGR in the upcoming years. As the adoption of data analytics platforms is on a rapid rise, the field is expected to generate plenty of rewarding opportunities for people.


2. Trending Data Science Jobs

In a country like India with a thriving technology sector, the scope for professionals like data scientists, data analysts, data engineers, big data engineers, data architects and data managers is extremely high. By making yourself proficient in analytics and statistical methods for mining data, you can utilize these opportunities to the fullest.


3. Demand for Data Scientists

It is projected by the Bureau of Labor Statistics that the employment of data scientists will increase by more than 34% from 2022 to 2023, making it a job that is here to stay in the future. So if you are having second thoughts about pursuing data science, you should definitely think twice. Get firsthand training from one the best data science coaching institutes in Hyderabad.


4. Job Availability

Hyderabad is a renowned IT center in the country. All types of tech companies, be it a large scale organization or a small business operate there. That’s why a lot of people rush there to find their dream jobs, making it a suitable location to secure well paying positions.

5. Salary Scope of Data Scientists

The average salary of a Data Scientist in Hyderabad ranges between ₹ 3.5 Lakhs to ₹ 20.9 Lakhs with an average salary of ₹ 12.4 LPA. The salary is higher than many professions in the software industry including UI/UX developers, web developers and full stack engineers. 


Learning Outcomes of Data Science Training in Hyderabad


By completing our end-to-end data science training in Hyderabad, you will not only gain in-depth insights into advanced data analysis and machine learning algorithms, but will also:

Data Science Training Curriculum in Hyderabad

• Introduction to programming
• R or Python?
• Why Python for Data Science?
• Different job roles with Python
• Different Python IDEs
• Downloading and setting up python environment
Hands-On – Installing Python – IDLE

• Python input and output operations.
• Comments
• Variables, rules for naming variables
• Basic Data Types in Python
• Typecasting in python
Hands-On – Using comments, variables, data types, and typecasting in python program

● Arithmetic operators
● Assignment operators
● Comparison operators
● Logical operators
● Identity operators
● Membership Operators
● Bitwise Operators
Hands-On – Working with different data types in a program

• Creating strings
• String formatting
• Indexing
• Slicing
• String methods
Hands-On – Performing string operations

● Creating lists
● Properties of lists
● List indexing
● List slicing
● List of lists
● List Methods
● Adding, Updating & removing elements from lists
Hands-On – Slicing, Indexing, and using methods on lists

• Syntax to create tuples
• Tuple properties
• Indexing on tuples
• Slicing on tuples
• Tuple methods
Hands-On – Working with tuples

• Syntax for creating sets
• Updating sets
• Set operations and methods
• Difference between sets, lists and tuples
Hands-On – Performing set operations in a program

• Syntax for creating Dictionaries
• Storing data in dictionaries
• Dictionaries keys and values
• Accessing the elements of dictionaries
• Dictionary methods
Hands-On – Creating dictionaries and using dictionaries methods

• Setting logic with conditional statements
• If statements
• If -else statements
• If-elif-else statements
Hands-On – Setting logic in programs using conditional statements

• Iterating with python loops
• while loop
• for loop
• range
• break
• continue
• pass
• enumerate
• zip
• assert
Hands-On – Iterating with loops in python

● Solving Level by Level Challenges
● Assignments to acquire Bronze and Silver Level badges

• Why List comprehension
• Syntax for list comprehension
• Syntax for dict comprehension
Hands-On – Using List and Dictionary comprehension

• What are Functions
• Modularity and code reusability
• Creating functions
• Calling functions
• Passing Arguments
• Positional Arguments
• Keyword Arguments
• Variable length arguments (*args)
• Variable Keyword length arguments (**kargs)
• Return keyword in python
• Passing function as argument
• Passing function in return
• Global and local variables
• Recursion
Hands-On – Creating our own functions,passing arguments and performing operations

• Lambda
• Lambda with filter
• Lambda with map
• Lambda with reduce
Hands-On – Working with lambda, filter,map and reduce in python

● Creating and using generators
Hands-On – Creating and using generators

• Creating modules
• Importing functions from different module
• Importing Variables from different modules
• Python builtin modules
Hands-On – Creating and importing Modules

• Creating packages
• Importing modules from package
• Different ways of importing modules and packages
• Working on Numpy,Pandas and Matplotlib
Hands-On – Creating and importing packages

• Syntax errors
• Logical errors
• Handling errors using try,except and finally
Hands-On – Handling Errors with try and except

• Creating classes & Objects
• Attributes and methods
• Understanding __init__ constructor method
• Class and instance attributes
• Different types of of methods
• Instance methods
• Class methods
• Static methods
• Inheritance
• Creating child and parent class
• Overriding parent methods
• The super() function
• Understanding Types of inheritance
• Single inheritance
• Multiple inheritance
• Multilevel inheritance
• Polymorphism
• Operator overloading
Hands-On – Creating classes, objects. Creating methods and attributes. Working with different methods. Using inheritance and polymorphism.

• date module
• time module
• datetime module
• time delta
• formatting date and time
• strftime()
• striptime()
Hands-On – working with date and time

• Understanding the use of regex
• re.compile()
• re.find()
• re.split()
• re.sub()
• Meta characters and their use
Hands-On – using a regular expression to search patterns

• Opening file
• Opening different file types
• Read,write,close files
• Opening files in different modes
Hands-On – Reading, Writing, Appending, opening and closing files.

● Introduction to APIs
● Accessing Public APIs
Hands-on – Accessing Public Weather APIs and People in Space API

• Installing BeautifulSoup
• Understanding web structures
• Chrome devtools
• request
• Scraping data from web using beautifulsoup
• scraping static websites
• Scraping dynamic websites using beautiful soup.
Hands-On – Scraping static and dynamic websites using beautifulsoup and selenium

  • Introduction to Pandas, a Python library for data manipulation and analysis.
  • Overview of NumPy, a fundamental package for scientific computing with Python.
  • Explanation of key data structures in Pandas: Series and DataFrame.
  • Hands-on exploration of data using Pandas to summarize, filter, and transform data.
  • Data cleaning techniques, handling missing values, and dealing with outliers.
  • Statistical analysis of data using NumPy functions.
  • Introduction to data visualization and its importance in data analysis.
  • Overview of Matplotlib, a popular plotting library in Python.
  • Exploring different types of plots: line plots, scatter plots, bar plots, histogram, etc.
  • Customizing plots with labels, titles, colors, and styles.
  • Introduction to Seaborn, a Python data visualization library based on Matplotlib.
  • Advanced plotting techniques with Seaborn: heatmaps, pair plots, and categorical plots.
  • Introduction to Plotly, an interactive plotting library for creating web-based visualizations.
  • Creating interactive and dynamic visualizations with Plotly.


Hands-on: lnstagram Reach Analysis

  • Introduction to data bases.
  • WhySQL?
  • Execution of an SQL statement.
  • Installing MySQL
  • Load data.
  • Use, Describe, Show table.
  • Select.
  • Limit, Offset.
  • Order By.
  • Distinct.
  • Where, Comparison Operators, NULL.
  • Logic Operators.
  • Aggregate Functions: COUNT, MIN, MAX,AVG, SUM.
  • Group By.
  • Having.
  • Order of Keywords.
  • Join and Natural Join.
  • Inner, Left, Right, and Outer Joins.
  • Sub Queries/Nested Queries/Inner Queries.
  • Data Control Language: GRANT, REVOKE

Hands-on – Storing and Analysing Scraped Dataset Using SQL

  • Excel Introduction
  • Workbook Window
  • Create & Open Workbooks
  • MS Excel Online
  • Excel vs Google Sheets
  • Office Button
  • Ribbon and Tabs
  • Features of Tabs
  • Quick Access Toolbar
  • Mini Toolbar
  • Title, Help, Zoom, View
  • Worksheet, Row, Column
  • Moving on Worksheet
  • Enter Data
  • Select Data
  • Delete Data
  • Move Data
  • Copy Paste Data
  • Spell Check Insert Symbols
  • Addition
  • Sigma Addition
  • Subtraction
  • Calculate Average
  • Sigma Average
  • Fill Handle
  • Fill Handle with Text
  • Text with Numbers
  • Fill Handle with Dates
  • Create Formula open link
  • Fill Handle in Formula
  • Relative Referencing
  • Absolute Referencing
  • Instruction for Typing
  • Excel IF
  • If Function
  • If with Calculations Excel COUNTIF Advanced If
  • WHAT IF Analysis
  • Introduction to Excel Charts
  • Dynamic Advanced Charts
  • Pivot Table with Dashboard
  • Advanced Pivot Table Tips & Tricks
  • Excel Macros
  • Excel sumif
  • Excel vlookup
  • Excel ISNA
  • Find & Remove Duplicates
  • Create drop-down List
  • Merge cells in Excel
  • Building bar charts and line charts
  • Creating pie charts and scatter plots
  • Designing basic maps and geographic visualizations
  • Using filters to subset data
  • Sorting data by different criteria
  • Applying quick filters for interactive exploration
  • Adding labels, tooltips, and colors to visualizations
  • Formatting axes and gridlines
  • Customizing visual elements for better presentation
  • Combining multiple visualizations into a dashboard
  • Adding interactivity with filters and actions
  • Arranging and organizing dashboard elements
  • Publishing dashboards to Tableau Public or Tableau Server
  • Embedding dashboards in websites or presentations
  • Presenting and sharing dashboards effectively
  • Overview of Power Bl and its features
  • Understanding the Power Bl interface
  • Connecting to data sources
  • Importing and transforming data
  • Creating bar charts and line charts
  • Designing pie charts and scatter plots
  • Building basic tables and matrices
  • Using filters and slicers to subset data
  • Adding interactivity to visualizations
  • Sorting and formatting data
  • Building interactive dashboards with multiple visualizations
  • Adding filters and slicers for user interactivity
  • Formatting and organizing dashboard elements
  • Publishing reports to the Power Bl Service
  • Sharing reports and dashboards with others
  • Configuring security and access controls

Hands-on: lnstagram Reach Analysis

  • Data- types of data
  • A measure of central tendency – Mean-Median-Mode
  • A measure of shape – Variance- Standard deviation, Range, IQR
  • The measure of shape – Skewness, and kurtosis
  • Covariance
  • Correlation – Pearson correlation & Spearman’s rank correlation
  • Probability – Events, Sample Space, Mutually exclusive events, Mutually exclusive events
  • Classical and Conditional Probability
  • Probability distribution – Discrete and Continuous
  • Uniform Distribution
  • Expected values, Variance, and means
  • Gaussian/Normal Distribution
  • Properties, mean, variance, empirical rule of normal distribution
  • Standard normal distribution and Z-score
  • Central Limit Theorem
  • Hypothesis testing – Null and Alternate hypothesis Type – I and Type – II error
  • Critical value, significance level, p-value
  • One-tailed and two-tailed test
  • T-test – one sample, two-sample, and paired t-test f-test
  • One way and two way ANOVA
  • Chi-Square test
  • Introduction to Machine Learning and its types (supervised, unsupervised, reinforcement learning)
  • Setting up the development environment {Python, Jupyter Notebook, libraries: NumPy, Pandas, Scikit-learn)
  • Overview of the Machine Learning workflow and common data preprocessing techniques
  • Definition of data science and its role in various industries.
  • Explanation of the data science lifecycle and its key stages.
  • Overview of the different types of data: structured, unstructured, and semi-structured.
  • Discussion of the importance of data collection, data quality, and data preprocessing..
  • Introduction to Data Engineering: Data cleaning, transformation, and integration
  • Data cleaning and Handling missing values: Imputation, deletion, and outlier treatment
  • Feature Engineering techniques: Creating new features, handling date and time variables, and encoding categorical variables
  • Data Scaling and Normalization: Standardization, min-max scaling, etc.
  • Dealing with categorical variables: One-hot encoding, label encoding, etc.
  • Cross-validation and model evaluation techniques
  • Hyperparameter tuning using
  • GridSearchCV and RandomizedSearchCV Model selection and comparison
  • Introduction to Regression: Definition, types, and use cases
  • Linear Regression: Theory, cost function, gradient descent, residual analysis, Q-Q Plot, Interaction Terms, and assumptions
  • Polynomial Regression: Adding polynomial terms, degree selection, and overfitting
  • Lasso and Ridge Regression: Regularization techniques for controlling model complexity
  • Evaluation metrics for regression models: Mean Squared Error (MSE), R-squared, and Mean Absolute Error (MAE)

Hands-On – House Price Prediction

  • Introduction to Classification: Definition, types, and use cases
  • Logistic Regression: Theory, logistic function, binary and multiclass classification
  • Decision Trees: Construction, splitting criteria, pruning, and visualization
  • Random Forests: Ensemble learning, bagging, and feature importance
  • Evaluation metrics for classification models: Accuracy, Precision, Recall, Fl-score, and ROC curves
  • Implementation of classification models using scikit-learn library

Hands-On – Heart Disease Detection & Food Order Prediction

  • Support Vector Machines (SVM): Study SVM theory, different kernel functions (linear, polynomial, radial basis function), and the margin concept. Implement SVM classification and regression, and evaIuate the models.
  • K-Nearest Neighbors (KNN): Understand the KNN algorithm, distance metrics, and the concept of Kin KNN. Implement KNN classification and regression, and evalu­ ate the models.
  • Naive Bayes: Learn about the Naive Bayes algorithm, conditional probability, and Bayes1 theorem. Implement Naive Bayes classification, and evaluate the model1s performance

Hands-On – Contact Tracing & Sarcasm Detection

  • Ada Boost: Boosting technique, weak learners, and iterative weight adjustment Gradient Boosting {XGBoost): Gradient boosting algorithm, Regularization, and hyper para meter tuning
  • Evaluation and fine-tuning of ensemble models: Cross-validation, grid search, and model selection
  • Handling imbalanced datasets: Techniques for dealing with class imbalance, such as oversampling and undersampling

Hands-On – Medical Insurance Price Prediction

  • Introduction to Clustering: Definition, types, and use cases
  • K-means Clustering: Algorithm steps, initialization methods, and elbow method for determining the number of clusters
  • DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Core points, density reachability, and epsilon-neighborhoods
  • Evaluation of clustering algorithms: Silhouette score, cohesion, and separation met-rics

Hands-On – Credit Card Clustering

  • Introduction to Dimensionality Reduction: Curse of dimensionality, feature ex­ traction, and feature selection
  • Principal Component Analysis {PCA): Eigenvectors, eigenvalues, variance explained, and dimensionality reduction
  • Implementation of PCA using scikit-learn library

Hands-On – MNIST Data

  • Introduction to Recommendation Systems: Understand the concept of recommen- dation systems, different types (collaborative filtering, content-based, hybrid), and evaluation metrics.
  • Collaborative Filtering: Explore collaborative filtering techniques, including
  • user-based and item-based approaches, and implement a collaborative filtering model.
  • Content-Based Filtering: Study content-based filtering methods, such as TF-1DF and cosine similarity, and build a content-based recommendation system.
    Deployment and Future Directions: Discuss the deployment of recommendation systems and explore advanced topics in NLP and recommendation systems.

Hands-On – News Recommendation System

  • Introduction to Reinforcement Learning: Agent, environment, state, action, and reward
  • Markov Decision Processes {MOP): Markov property, transition probabilities, and vaIue functions
  • Q-Learning algorithm: Exploration vs. exploitation, Q-table, and learning rate Hands-on reinforcement learning projects and exercises

Hands-On – Working with OpenAI Gym

  • Introduction to Flask/ Stream lit web framework
  • Creating a Flask/ Streamlit application for ML model deployment Integrating data preprocessing and ML model
  • Designing a user-friendly web interface

Hands-On – Iterating with loops in python

  • Building a web application for Machine Learning models: Creating forms, handling user input, and displaying results
  • Deployment using AWS (Amazon Web Services): Setting up an AWS instance, con­ figuring security groups, and deploying the application
  • Deployment using PythonAnywhere: Upleading Flask application files, configuring WSGI, and launching the application
  • Work on a real-world Machine Learning project: Identify a problem, gather data, and define project scope
  • Apply the learned concepts and algorithms: Data collection, preprocessing, model building, and evaluation
  • Deployment of the project on AWS or PythonAnywhere: Showcase the developed application and share the project with others
  • Presentation and discussion of the project: Demonstrate the project, explain design decisions, and receive feedback
  • Introduction to NLP: Understand the basics of NLP, its applications, and chaIlenges.
  • Named Entity Recognition (NER): Understand the various approaches and tools used for NER, such as rule-based systems, statistical models, and deep learning.
  • Text Preprocessing: Learn about tokenization, stemming, lemmatization, stop word removal, and other techniques for text preprocessing.
  • Text Representation: Explore techniques such as Bag-of-Words (BoW), TF-IDF, and word embeddings (e.g., Word2Vec, GloVe) for representing text data.
  • Sequential Models: Introduction to RNN, LSTM, Hands on Keras LSTM
  • Sentiment Analysis: Study sentiment analysis techniques, build a sentiment analysis model using supervised learning, and evaluate its performance.

Hands-On – Real Time Sentiment Analysis

  • Introduction
  • History of Deep Learning Perceptrons
  • Multi-Level Perceptrons Representations
  • Training Neural Networks Activation Functions
  • Introduction
  • Deep Learning
  • Understanding Human Brain
  • In-Depth Perceptrons
  • Example for perceptron
  • Multi Classifier
  • Neural Networks
  • Input Layer
  • Output Layer
  • Sigmoid Function
  • Introduction to Tensorflow and Keras
  • CPU vs GPU
  • Introduction to Google collaboratory
  • Training Neural Network
  • Understanding Notations
  • Activation Functions
  • Hyperparameter tuning in keras
  • Feed-Forward Networks
    Online offline mode
  • Bidirectional RNN
  • Understanding Dimensions
  • Back Propagation
  • Loss function
  • SGD
  • Regularization
  • Training for batches

Hands-On – Facial Emotion Recognition

  • Introduction to CNN
  • Applications of CNN
  • Idea behind CNN
  • Understanding Images
  • Understanding Videos
  • Convolutions
  • Striding and Padding
  • Max Pooling
  • Edges, Gradients, and Textures
  • Understanding Channels
  • Formulas
  • Weight and Bias
  • Feature Map
  • Pooling
  • Combining
  • Introduction to RNNs
  • Training RNNs
    RNN Formula
  • Architecture
  • Batch Data
  • Simplified Notations
  • Types of RNNs
  • LSTM
  • GRUs
  • Training RNN
  • One to many
  • Vanishing Gradient problem

Hands-On – COVID-19 Cases Prediction

  • Introduction to Generative Models:
  • Understanding GANs {Generative Adversarial Networks)
  • GAN Architecture
  • GAN Training
  • Evaluating GAN Performance
  • GAN Variants and Applications
  • Intro to OpenCV
  • Reading and Writing Images
  • Saving images
  • Draw shapes using OpenCV
  • Face detection and eye detection using OpenCV
  • CNN with Keras
  • VGG

Hands-On – Real Time Pose Estimator

  • Dataset collection
  • Data preprocessing
  • Feature extraction
  • Labeling
  • Model selection
  • Model training
  • Model evaluation
  • Real-time implementation
  • Alert mechanism
  • Continuous improvement
  • Identify a reliable source for house price data
  • Understand the website structure
  • Perform web scraping
  • Preprocess the scraped data
  • Explore and preprocess additional data sources (if applicable)
  • Define the problem
  • Split the data
  • Train the model
  • Evaluate the model
  • Fine-tune the model (optional)
  • Deploy the model
  • Continuously update the dataset and retrain the model
  • Define chatbot goals and scope
  • Gather training data
  • Data preprocessing
  • API integration
  • Model customization
  • User input handling
  • Response generation
  • Post-processing and filtering
  • Error handling and fallback mechanisms
  • Continuous improvement
  • Data collection
  • Data preprocessing
  • Dataset augmentation
  • Model architecture
  • Training
  • Model evaluation
  • Fine-tuning
  • Real-time inference
  • Thresholding and alerts
  • Model optimization

Languages & Tools Covered in Our Data Science Training in Hyderabad

We have handpicked some of the most widely regarded tools for our data science training program including Matplotlib, Seaborn, Plotly, MySQL, AWS, MS Excel, Power BI, Tableau, Jupyter, Numpy, Pandas, Scikit-learn and so much more. As you will begin to get the hang of the latest statistical and analytical technologies, no one can stop you from becoming a skilled data scientist. 

Become a Data Science developer

Talk to our expert Best Data Science mentors and learn how our training programs in Hyderabad can help you become a Best Data Science developer and get a high-paying job.

Data Science Projects You Will Work On


At Codegnan, we ensure students gather substantial practical experience with the help of result-driven data science projects. The projects are so relatable and intriguing that you will never feel like you are working, even when you actually do.

1. Real Time Drowsiness Detection Alert System

The project is not only good for beginners but also professionals who want to gain an overall knowledge of data science. You will get a chance to gain a strong practical understanding of concepts like dataset collection, data preprocessing, model training and real time deployment. 


2. Real Time Rain Prediction Using ML

By utilizing machine learning algorithms, you will build a rain prediction model that shows and continuously updates weather data. As you work with data processing, you will slowly get a grasp of necessary libraries, Flask integration, data visualization, and testing & debugging.


3. Customizable Chatbot Using OpenAI API

Understanding the popular industrial trends, we decided to dedicate one of our projects to an OpenAI based chatbot. You will learn how to handle user inputs effectively, customize AI models, and generate an automated response.


4. House Price Prediction Using LSTM

With this project, you will garner an in-depth knowledge of website structure, web scraping, data splitting, datasets and model fine tuning as you deal with house price data. This project will allow you to learn the complexities of predictive analysis.


5. Fire and Smoke Detection Using CNN

This fire and smoke detection project will offer you a wide practical experience of working with convolutional neural networks. You will amass an all around perspective of dataset augmentation, model architecture, thresholding and real time interference.


Who Should Take this Data Science Training?


If you are genuinely interested in taking your career to new heights or beginning your professional journey in the best ways possible, this course is for you. Join our army if you are:

1. Any Graduate

Graduates of any field including arts, commerce and core science can take our data science coaching classes in Hyderabad. The curriculum is designed in an easy-to-understand way so that nobody is left behind.


2. Computer Science Engineers Looking for Role Shift

As data science involves a lot of programming as well, computer science engineers can easily transition into data science. If you want to bag lucrative job opportunities in the tech sector but not in core computer science, this training may be just what you are looking for.


3. Beginner Developers/ Engineers

Developers or engineers who have just started their careers and realize that software development is just not their thing can also take our data science course in Hyderabad. Your interest in mathematics and statistics is enough to assist you in easing into the program. 


4. IT Professionals

If you want to make your profile stand out in the IT industry, gaining vital data science and ML skills is just what you need. Your existing competencies when combined with analytics will help you do wonders in the job scenario. 

5. Anybody Who Takes Interest in Data Science

The point is no matter who you are, where you are from and what you do, as long as you want to build a bankable career in data science, this course is for you.


Get Data Science Course
Certification in Hyderabad


Codegnan is the best institute for data science training in Hyderabad which ensures that after doing all this handwork, you get the most optimal results. We provide an authorized course completion certificate that you can show off to both your friends and recruiters. Besides that, we will provide our personalized attention to each one of you till you emerge as the master data scientists in shining armours. 


Meet your Data Science Trainers in Hyderabad


Saketh Kallepu

Saketh Kallepu is a Data science professional in the IT field with a background in Computational Intelligence. Having 7+ years of experience in this field, he is an outstanding Data Science Mentor and trainer.

Saketh Kallepu believes, “Don’t work hard, just work with heart.” With this belief, he joined as Co-Founder, mentor, and trainer at Codegnan to change the education platform and build a new technical arena for passionate learners.

Manohar Chary Vadla

Manohar Chary Vadla is a Data Scientist and Mentor with a Bachelor of Commerce in Computer Science background, having 2+ years of hands-on experience in Data Extraction from documents using Python and deep learning techniques.

His areas of expertise include research and implementation in machine learning and deep learning, such as regression, classification, neural networks, and natural language processing (NLP).

As part of the AI-for India Event, in collaboration with GUVI Geek Networks and IITM Research Park, he developed a facial recognition application using the Python programming language.

Codegnan Learners success

1250+ Companies Have Hired Codegnan Learners


Data Science Training Fees in Hyderabad — Get 20% off


We have priced our data science training fees, which is inclusive of the job accelerated program, at ₹ 75,000. But we have news for you. You can get our complete 6 months data science coaching classes at a discounted rate for a limited time period. Hop along soon if you want to build a career that offers you a high return on investment. 

Our other data science training location(s)

codegnan's other training courses in Hyderabad

Phone Number

+91 98887 48888


Kothwal Madhava Reddy Plaza, Beside Indian Oil Petrol Bunk, JNTUH Metro Station, Nizampet X Roads, Hyderabad - 500072

Data Science Training
in Hyderabad FAQs


You can become a data scientist in Hyderabad by enrolling in a good data science training that offers a comprehensive knowledge of data science and machine learning tools and technologies. Codegnan offers distinguished data science training with a job acceleration program to bring you closer to your goals.

Codegnan’s data science training has a duration of 6 months, with classes held from every Monday to Saturday. In this period, you will learn the intricacies of data science from the basics to advanced levels.

Data science is not only a good career but one of the most highly paid professions in 2023. There is a high demand for data science positions in all types of companies from MNCs to startups, making it a popular career choice amongst youngsters. 

There is no eligibility criteria for Codegnan’s data science training in Hyderabad. However, when starting the classes, don’t forget to bring your enthusiasm for learning.

The intensive 6 months data science training of Codegnan is offered at only ₹ 75,000. However, if you get onboarded soon, you may get a 20% discount and receive the course for ₹ 60,000 only.

Upon completing the course, you will receive Codegnan’s industry standardized data science certification. Along with that, you will also receive guaranteed placement assistance. 

There are no prerequisites of this data science course. Candidates from all academic and professional backgrounds are welcome.

No, you don’t have to know how to code for this data science training. The curriculum is designed to suit the needs of programming newbies as well.

After this data science training, you find a myriad of jobs in the data science, analytics, ML and related fields. Some of the most popular jobs you can do are data scientists, data analysts, data engineers, big data engineers, data architects and data managers.

Yes, Codegnan offers both online and offline training for data science in Hyderabad at ₹ 75,000 each. Students can enroll in any mode that is suitable for their unique demands.


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