#1 Data Science Course Training in Vijayawada

Data science is a high-demand subject that is valued across in all sectors. If you are looking for beginner-friendly Data Science training in Vijayawada that ensures placements, codegnan has the perfect one for you. 

This six-month-long course will ensure that you can make data-driven decisions that can help you land a high-paying job

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300 Hours Instructor
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Data Science Course Overview and Key Features in Vijayawada

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Data Science is a discipline that uses subjects like statistics, scientific computing, and other methods to extract knowledge from data. This knowledge is then put into making informed knowledge in companies, which is much more reliable than taking decisions based on gut instincts. 

In this Data Science Course by codegnan, the prime objective is to make you capable of extracting actionable insights from large and complex datasets.

Career Scope and Job Opportunities in Vijayawada for Data Scientists

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The career scope of Data Science jobs in Vijayawada is not only high but also well-paying. The salary of a data scientist in Vijayawada can range anywhere between ₹3.0 to ₹13.0 LPA, making the average income around ₹6.1 LPA. 

Apart from the good pay, here are some reasons why you should opt for a data science course.

1. High demand

 The role of a data scientist is of high demand because more and more businesses are moving towards a data-driven approach when it comes to decision-making.

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2. Endless job opportunities

 Almost all medium to large-size companies are looking to hire data scientists to enhance their decision-making capabilities. Hence, the job opportunities are plenty.

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3. Significant pay

 Although the salary of a data scientist can vary based on a lot of factors (location, experience, company etc.) it is still on the higher end of IT jobs.

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4. Versatility

 Data scientists are not confined by any particular industry. Therefore, from agriculture to tech, wherever a large amount of data needs to be sorted to find insights, data scientists are useful. 

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Data Science Training Curriculum in Vijayawada

• 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.search()
• 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.
  • DML: INSERT
  • DML: UPDATE, DELETE
  • DML: CREATE,TABLE
  • DDL: ALTER, ADD, MODIFY, DROP
  • DDL: DROP TABLE, TRUNCATE, DELETE
  • 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

Learning Outcomes of Data Science Training in Vijayawada

The data science course in Vijayawada is a comprehensive beginner-friendly course where the prime objective is to make you capable of extracting actionable insights from large and complex datasets.

However, along with that, at the end of the course you can expect to-

Do statistical analysis.

Know the basics of machine learning algorithms and data visualization techniques,

Uncover patterns, trends, and relationships in datasets. 

Take information from data and solve real-life problems.

Familiarity with the principles of microservices and DevOps.

Use the information to optimize processes, improve efficiency, and gain competitive advantage for your clients.

Become a Data Science Course developer

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

Languages & Tools covered in Our Data Science Training in Vijayawada

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Mastering these tools is a must if you are trying to become a skilled data scientist. For example, Numpy provides tools for mathematical operations such as addition, subtration, multiplication, and division. Python’s Pandas library is filled with tools required for data manipulation such as selecting, filtering, and aggregating. 

Thus, the tools and languages covered in our course will ensure you are fully capable 

Here are the tools and languages that you will learn about in this data science course by codegnan:

Data Science Projects and case studies you will work on

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1. Real Time Drowsiness Detection Alert System

Learn to collect and preprocess data, extract relevant features, and label it for drowsiness detection. Select and train a model for real-time implementation with alert mechanisms. Continuously improve the system's performance and accuracy.

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2. Real-Time Rain Prediction using ML

Install libraries and fetch live weather data with an API key. Preprocess data, train an ML model, and integrate it with Flask for real-time rain prediction. Test, debug, deploy, and keep the application updated with the latest weather data.

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3. House Price Prediction using LSTM

Scrape house price data, preprocess it, and explore additional sources if needed. Split and train the data using LSTM models. Evaluate, fine-tune (optional), and deploy the model. Continuously update the dataset and retrain the model for accurate predictions.

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4. Fire and Smoke Detection using CNN

Collect, preprocess, and augment the dataset for fire and smoke detection. Build a CNN model with architecture, train and evaluate it. Fine-tune for optimal performance, implement real-time inference, and set up thresholding and alert mechanisms. Optimize the model.

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5. Customizable Chatbot using OpenAI API

Define chatbot goals, gather and preprocess training data, integrate OpenAI API, and customize the model for user input handling and response generation. Implement post-processing, filtering, error handling, and fallback mechanisms for continuous improvement.

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Who Should Take this Data Science Training?

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In case you are wondering if this course will be useful to you or not, here are the profiles suitable for this course. 

1. Any graduate

For fresh graduates a data science course provides a competitive edge in the job market, opening doors to exciting career opportunities across industries. It equips them with the skills to analyze data, make informed decisions, and contribute meaningfully to organizations.

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2. Computer science engineers who are looking to shift roles

Our Data science course offers computer science engineers a way to diversify their career options. They can leverage their technical background to enter the data-driven world, working on projects like AI and machine learning, which are in high demand.

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3. Beginner developers/engineers

 Beginners with basic coding skills can opt for our  data science course to gain proficiency in programming, data analysis, and statistical modeling. These skills can help them become versatile professionals and enhance their employability.

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4. IT professionals

 IT professionals can stay relevant and advance their careers by adding data science expertise. They can harness data for improving systems, optimizing processes, and developing data-driven solutions, aligning themselves with the evolving tech landscape.

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5. Anybody who takes interest in the field of data science

Individuals passionate about data science can turn their interest into a rewarding career. Our data science course will equip them with the knowledge and skills to delve deep into data analysis, visualization, and machine learning, giving them a more promising career path.

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Get Data Science Course Certification in Vijayawada

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Wondering how are you going to prove your expertise when going for an interview? Then don’t worry, as our data science course in Vijayawda comes with a certification. 

After successfully completing the course, you will receive a certification of compilation from codegnan to prove your genuinity. 

Meet Your Data Science course trainer

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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.

Codegnan Learners success

1250+ Companies Have Hired Codegnan Learners

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Data Science Training Fees in Vijayawada — Get 25% off

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The data science training fees in Vijayawada is ₹75000 for both online and in person classes. Which is a steal deal compared to what you will be getting. 

You get:

However, if the price still seems too high for you, you can contact the team and check your eligibility for discount. Usually you can get upto 25% discount, dropping the effective price to ₹60,000 only.

Our other data science training location(s)

codegnan's other training courses in Vijayawada

Phone Number

+91 98887 58888

Location

40-5-19/16, Prasad Naidu Complex, P.B.Siddhartha Busstop, Moghalrajpuram, Vijayawada, Andhra Pradesh 520010

Data Science course in Vijayawada FAQs

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The data science training in Vijayawda is 6 months long. Where the class durations are of 2 hours from Monday to Saturday.

Yes, data science is a great career in 2023. Demand for data scientists and machine learning professionals has been growing rapidly in recent years and is expected to continue. 

According to India Today, there will be around 11 million job opportunities in the field of data science solely in India by the year 2026.

Any students after thier 12th boards are eligible for the data science training in Vijayawda.

The fees of the data science training offered by codegnan is ₹75,000 for both online and offline classes. However you can avail a 25% discount at get the training at only ₹60,000. 

Upon completion of the course you will receive an authorized certification from codegnan. 

There are no as such prerequisites of this data science training in Vijayawada. Anyone who has passed their 12th boards is eligible to enrol for the course. 

No, knowledge of coding is not necessary for this data science training in Vijayawada. However, knowing the basics of Python language will definitely be an plus for you.

The job opportunities after this data science training from codegnan are countless. You can land jobs in high paying roles like data scientist, data analyst, Al/ ML engineer, enterprise data architect, data engineer and so on. 

Yes, codeganan’s data science training in Vijayawda is conducted both in online and offline modes.

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