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we train you to get hired.
- We are here to show you how to learn and work with data from scratch. You might be a college student, job-seeking, or an IT professional, but through the course, you understand how data gets accumulated, cleaned, processed, and utilized for making decisions. In Visakhapatnam, students are joining Data Science Classes in Visakhapatnam in order to secure a placement in well-paying tech roles or shift their career. With live sessions, live projects, and professional trainers, the course offers you the right platform to get job-ready in data science. You will gain skills that employers actually need, during the course. Course
- The primary goal of this course is to make you feel at ease in using data science tools and solving real-world issues. It is not a theory course. Data Science Classes in Vizag gives you real practical knowledge by enabling you to solve real-world projects and business data. You can know how to prepare for data role interviews and technical tests.
- What You Will Learn in This Course:
- This program prepares you for careers requiring not only technical knowledge but actual business problem-solving.
How to use Python programming to handle data.
Handling libraries like NumPy, Pandas, and Matplotlib.
Cleaning and preparing dirty data for analysis.
Visualisation with Tableau and Power BI.
Using machine learning algorithms like regression, classification, clustering.
Advanced algorithms like Random Forest, XGBoost, and SVM.
Deep learning using TensorFlow and Keras.
Model deployment with Flask and Docker.
How data science is used in finance, healthcare, and e-commerce.
Data Science Course in Visakhapatnam
- This course has some effective features through which both fresher and professional pick up data science skills of ease. Data Science Coaching in Visakhapatnam does not comprise videos; it is based on projects, highly interactive, and industrial.
- Every module will be relevant to a practical scenario, and because of that you learn not only the concept but implement the same on the projects. What Makes This Course Special
Novice to Pro Voyage: You start with basics like Python and proceed to subjects like AI and Deep Learning.
Practical Projects: Every topic of the course has exercises, mini-projects, and live datasets.
Live and Pre-recorded Classes: Live teacher sessions are at your disposal and you even get pre-recorded videos for practice.
LMS Access: Lifetime access to Learning Management System (LMS) in which you will be provided with course material, assignments, and quizzes.
Tool-Based Learning: Learn with top-class tools and libraries such as Python, Pandas, SQL, Power BI, Tableau, Scikit-learn, TensorFlow, and AWS.
Placement Support: Resume building, mock interview, job opening referral, and application support are covered in the course.
Flexible Timings: Choose weekday, weekend, or fast-track batches based on your convenience.
Capstone Project: As a course culmination, you will undertake a capstone project utilizing real industry data to illustrate your entire learning experience.
- Anyone who desires to build a career as a data scientist can join this course. There is no need to graduate in computer science. Simple analytical thinking and passion towards data-driven problem-solving will be helpful in taking Data Science Coaching in Vizag. Both freshers and working professionals who desire to move their career into data can join this course. Who Can Join This Course:
Freshers/Students: Students of B.Tech, BCA, B.Sc, M.Sc, MCA, and MBA who wish to have a career in data.
Working Professionals: Software, IT support staff, testing, or even non-technology professionals who would like to shift to a career in data science.
Entrepreneurs: Entrepreneurs looking to leverage data for better decision-making or for automating things.
Career Changers: Sales, HR, or operations people who wish to shift to a career in technology.
Freelancers: Individuals who wish to offer data analytics or report services to customers.
- The scope after Data Science Course in Visakhapatnam is huge and growing day by day. Almost all industries today are data-driven, and companies need skilled experts to manage data to increase decisions, predict trends, and automate operations.
- What Can You Do After This Course:
Job: Search for jobs like Data Analyst, Business Analyst, Junior Data Scientist, ML Engineer, and Data Engineer.
Freelancing or Consulting: Employ your skills as a freelancer in data visualization, reporting, or analytics projects.
Higher Education: In case you are going to pursue a master's or diploma in AI/Data Science, then this course will be sufficient.
Entrepreneurship: Put your acquired skills into practice to develop data-driven apps of your choice or provide data services to companies.
Advanced Certifications: Post-course, you'll be adequately equipped for Microsoft, Google, IBM, and TensorFlow global certifications.
- Freshers after this course can expect decent joining cheques, if they possess decent practical knowledge. The course provides a decent technical base for which companies prefer it. After completing Data Science Training in Visakhapatnam, you can find a job in junior positions with decent learning and growth.
- Average Salary Range After the Course:
- Things that influence salary:
- The course covers all the above.
Data Analyst 4.2 to 6 LPA.
Junior Data Scientist 6 to 8 LPA.
ML Intern (Stipend) 15,000 to 30,000 per month.
Data Engineer Trainee 5 to 7 LPA.
Number of hands-on projects in your portfolio.
Internships completed during or after the course.
Your proficiency in Python, SQL, and ML tools.
Your interview task and tech round performance.
- This course not only helps you in launching your career but also directs your long-term development in the profession. If you keep learning after this course and acquire experience as well, you can shift to high-challenging, senior positions within 24 years.
- Career Path You Can Follow After This Course:
- Skills That Help You Grow After the Course:
- This course opens several doors if you are consistent and inquisitive.
Data Analyst - Business Analyst - Data Scientist
Data Scientist - Senior Data Scientist - Data Science Manager
ML Engineer - AI Engineer - AI Architect
Data Engineer - Big Data Engineer - Cloud Data Engineer
Cloud skills (AWS, Azure).
Deep learning (CNN, RNN, transformers).
Big data tools (Hadoop, Spark).
SQL + NoSQL databases.
GitHub profile with live projects.
- In Visakhapatnam, data professionals are increasingly sought after by industries as they are adopting data-driven approaches. Data Science Training in Vizag is being well-popularized as it offers quality training in a city where technology jobs are on the rise. Working professionals as well as freshers can best suit the course.
- Why This Course Is Popular:
The course follows industry-focused content and trends.
Trainers are highly experienced and from prominent tech companies.
Course fee is economical compared to metros.
Live case studies utilized for training.
Day batches as well as weekend batches for the convenience of everyone.
Excellent placement support with interview skills.
Many students from Visakhapatnam are taking this course as a stepping stone for IT professionals.
- After this course, you can perform different job roles based on your interest and knowledge. Every job role includes different tasks, which are explained and practiced through this course with practical exercises.
- Common Job Responsibilities After This Course:
- Tasks you will be Ready For:
Data Analyst: Clean data, look at trends, create reports.
Data Scientist: Train and build models, solve business problems.
ML Engineer: Build and deploy scalable machine learning systems.
Data Engineer: Deploy data pipelines, maintain data flow running.
BI Developer: Automate dashboards and business reports.
Working with raw data in Python and SQL.
Data visualization for business reporting.
Building machine learning models.
Presenting data insights to non-technical teams.
Deploying the solutions to cloud and web applications.
The course properly sets you up for these actual tasks.
- Data is the nucleus of every business now. This course job-prep's you for working with industries who are huge on data to expand their business. Industries in Visakhapatnam are looking for knowledgeable professionals in data for industries.
- Industries Hiring After This Course:
- Even small businesses and start-ups in Visakhapatnam now are creating in-house data teams. This course keeps you at par with such job postings.
IT & Software Development.
Banking and Finance.
Healthcare & Pharmaceuticals.
Logistics and Supply Chain.
Retail & E-commerce.
Manufacturing and IoT businesses.
- There are a couple of certificates given by the course that become useful while getting job postings. These certificates show that you've completed the course and worked on real-time projects.
- Certificates in the Course:
- These certificates are proof that not only are you skilled but that you're skilled in making your skills work for you in real-life problems too.
Course Completion Certificate.
Project Certificate (with GitHub link).
Internship Certificate (if you pursue it as part of the course).
Career-readiness certificate (after mock interviews).
- This is not a course of tool learning. This is about developing self-confidence in problem-solving with data. We give mentorship, real-time exposure, and complete placement assistance. Data Science Training Institute in Visakhapatnam is popular among Visakhapatnam students and working professionals.
- Why Choose Our Course:
Experienced trainers with 10+ years of experience from leading MNCs.
Course designed as job outcome project-based.
Career counseling such as mock interviews and resume feedback.
Access to vibrant alumni network and job alerts.
Real-world exposure to the likes of Python, Tableau, TensorFlow, and cloud infrastructure.
- Well-packaged modules split the course that takes you from beginner to advanced levels step by step. Practicals and real-world projects are also included in each module of the course so that you not only learn the topic but also learn how to implement it on a real-world basis. The modules are designed keeping in view the suggestions of the industry experts and hiring partners so that Data Science Course in Vizag becomes job-oriented as per the requirements of the job market.
- Course Modules:
Introduction to Data Science.
Python for Data Science.
Data Analysis with NumPy and Pandas.
Data Visualization.
Statistics for Data Science.
SQL for Data Management.
Machine Learning Algorithms.
Deep Learning Fundamentals.
Natural Language Processing (NLP).
Model Deployment.
Capstone Project.
- When you complete this course, you'll have certificates that verify your training and competence. The certificates come in useful while job hunting and boost your curriculum vitae. The course you're pursuing also prepares you for formal qualifications from major technology firms like Google, Microsoft, and IBM.
- You May Also Read:
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CURRICULUM & PROJECTS
Data Science Training Program
- Introduction To Python
- Installation and Working with Python
- Understanding Python variables
- Python basic Operators
- Understanding the Python blocks.
- Version Control with Git & GitHub
- Python Keyword and Identiers
- Python Comments, Multiline Comments.
- Python Indentation
- Understating the concepts of Operators
- Introduction To Variables
- Variables, expression condition and function
- Global and Local Variables in Python
- Packing and Unpacking Arguments
- Type Casting in Python
- Byte objects vs. string in Python
- Variable Scope
- Python Data Type
- Declaring and using Numeric data types
- Using string data type and string operations
- Understanding Non-numeric data types
- Understanding the concept of Casting and Boolean.
- Strings
- List
- Tuples
- Dictionary
- Sets
- Control Structure & Flow
- Statements if, else, elif
- How to use nested IF and Else in Python
- Loops
- Loops and Control Statements.
- Jumping Statements Break, Continue, pass
- Looping techniques in Python
- How to use Range function in Loop
- Programs for printing Patterns in Python
- How to use if and else with Loop
- Use of Switch Function in Loop
- Elegant way of Python Iteration
- Generator in Python
- How to use nested Loop in Python
- Use If and Else in for and While Loop
- Examples of Looping with Break and Continue Statement
- How to use IN or NOT IN keyword in Python Loop.
- Python Function, Modules and Packages
- Python Syntax
- Function Call
- Return Statement
- Arguments in a function Required, Default, Positional, Variable-length
- Write an Empty Function in Python pass statement.
- Lamda/ Anonymous Function
- *args and **kwargs
- Help function in Python
- Scope and Life Time of Variable in Python Function
- Nested Loop in Python Function
- Recursive Function and Its Advantage and Disadvantage
- Organizing python codes using functions
- Organizing python projects into modules
- Importing own module as well as external modules
- Understanding Packages
- Random functions in python
- Programming using functions, modules & external packages
- Map, Filter and Reduce function with Lambda Function
- More example of Python Function
- List
- What is List.
- List Creation
- List Length
- List Append
- List Insert
- List Remove
- List Append & Extend using + and Keyword
- List Delete
- List related Keyword in Python
- List Revers
- List Sorting
- List having Multiple Reference
- String Split to create a List
- List Indexing
- List Slicing
- List count and Looping
- List Comprehension and Nested Comprehension
- Tuple
- What is Tuple
- Tuple Creation
- Accessing Elements in Tuple
- Changing a Tuple
- Tuple Deletion
- Tuple Count
- Tuple Index
- Tuple Membership
- TupleBuilt in Function (Length, Sort)
- Dictionary
- Dict Creation
- Dict Access (Accessing Dict Values)
- Dict Get Method
- Dict Add or Modify Elements
- Dict Copy
- Dict From Keys.
- Dict Items
- Dict Keys (Updating, Removing and Iterating)
- Dict Values
- Dict Comprehension
- Default Dictionaries
- Ordered Dictionaries
- Looping Dictionaries
- Dict useful methods (Pop, Pop Item, Str , Update etc.)
- Sets
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Frozen Sets
- Strings
- What is Set
- Set Creation
- Add element to a Set
- Remove elements from a Set
- PythonSet Operations
- Python Exception Handling
- Python Errors and Built-in-Exceptions
- Exception handing Try, Except and Finally
- Catching Exceptions in Python
- Catching Specic Exception in Python
- Raising Exception
- Try and Finally
- Python File Handling
- Opening a File
- Python File Modes
- Closing File
- Writing to a File
- Reading from a File
- Renaming and Deleting Files in Python
- Python Directory and File Management
- List Directories and Files
- Making New Directory
- Changing Directory
- Python Database Interaction
- Basic SQL, DDL and DML commands
- SQL Database connection using
- Creating and searching tables
- Reading and Storing cong information on database
- Programming using database connections
- Reading an excel
- Working With Excel
- Reading an excel le using Python
- Writing to an excel sheet using Python
- Python| Reading an excel le
- Python | Writing an excel le
- Adjusting Rows and Column using Python
- ArithmeticOperation in Excel le.
- Play with Workbook, Sheets and Cells in Excel using Python
- Creating and Removing Sheets
- Formatting the Excel File Data
- More example of Python Function
- Complete Understanding of OS Module of Python
- Check Dirs. (exist or not)
- How to split path and extension
- How to get user prole detail
- Get the path of Desktop, Documents, Downloads etc.
- Handle the File System Organization using OS
- How to get any les and folders details using OS
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- Data Analysis and Visualization using Pandas.
- Read data from Excel File using Pandas More Plotting, Date Time Indexing and writing to les
- How to get record specic records Using Pandas Adding & Resetting Columns, Mapping with function
- Using the Excel File class to read multiple sheets More Mapping, Filling Nonvalues
- Exploring the Data Plotting, Correlations, and Histograms
- Getting statistical information about the data Analysis Concepts, Handle the None Values
- Reading les with no header and skipping records Cumulative Sums and Value Counts, Ranking etc
- Reading a subset of columns Data Maintenance, Adding/Removing Cols and Rows
- Applying formulas on the columns Basic Grouping, Concepts of Aggre gate Function
- Complete Understanding of Pivot Table Data Slicing using iLoc and Loc property (Setting Indices)
- Under sting the Properties of Pivot Table in Pandas Advanced Reading CSVs/HTML, Binning, Categorical Data
- Exporting the results to Excel Joins
- Python | Pandas Data Frame Inner Join
- Under sting the properties of Data Frame Left Join (Left Outer Join)
- Indexing and Selecting Data with Pandas Right Join (Right Outer Join)
- Pandas | Merging, Joining and Concatenating Full Join (Full Outer Join)
- Pandas | Find Missing Data and Fill and Drop NA Appending Data Frame and Data
- Pandas | How to Group Data How to apply Lambda / Function on Data Frame
- Other Very Useful concepts of Pandas in Python Data Time Property in Pandas (More and More)
- Data Analysis and Visualization using NumPy
- Introduction to NumPy Numerical Python
- Importing NumPy and Its Properties
- NumPy Arrays
- Creating an Array from a CSV
- Operations an Array from a CSV
- Operations with NumPy Arrays
- Two-Dimensional Array
- Selecting Elements from 1-D Array
- Selecting Elements from 2-D Array
- Logical Operation with Arrays
- Indexing NumPy elements using conditionals
- NumPys Mean and Axis
- NumPys Mode, Median and Sum Function
- NumPys Sort Function and More
- Data Analysis and Visualization using MatPlotLib
- Bar Chart using Python MatPlotLib
- Column Chart using Python MatPlotLib
- Pie Chart using Python MatPlotLib
- Area Chart using Python MatPlotLib
- Scatter Plot Chart using Python MatPlotLib
- Play with Charts Properties Using MatPlotLib
- Export the Chart as Image
- Understanding plt. subplots () notation
- Legend Alignment of Chart using MatPlotLib
- Create Charts as Image
- Other Useful Properties of Charts.
- Complete Understanding of Histograms
- Plotting Different Charts, Labels, and Labels Alignment etc.
- Introduction to Data Visualization with Seaborn
- Introduction to Seaborn
- Making a scatter plot with lists
- Making a count plot with a list
- Using Pandas with seaborn
- Tidy vs Untidy data
- Making a count plot with a Dataframe
- Adding a third variable with hue
- Hue and scattera plots
- Hue and count plots
- Visualizing Two Quantitative Variables
- Visualizing a Categorical and a Quantitative Variable
- Customizing Seaborn Plots
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- Foundation for AI: Learn traditional ML models, evaluation, and workflows.
- Introduction to ML, AI, and Deep Learning
- Types of ML (Supervised, Unsupervised, Reinforcement)
- ML Pipeline: Data Cleaning, Feature Engineering
- Common ML Algorithms: Linear, Logistic, DT, RF, SVM, KNN
- Model Evaluation: Accuracy, Precision, Recall, F1, ROC-AUC
- Overfitting, Underfitting, Cross-Validation
- Hands-on Project: Titanic Dataset (or similar)
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- Understand the inner workings of neural networks and train them with Keras.
- Introduction to Neural Networks & Deep Learning
- Activation Functions (ReLU, Sigmoid, Tanh)
- Feedforward Neural Network
- Backpropagation & Gradient Descent
- Learning Rate, Schedulers & Optimizers (SGD, Adam, RMSProp)
- Softmax, Cross-Entropy Loss
- Keras Basics: Sequential API & Functional API
- Fully Connected Layer Forward/Backward Pass
- Regularization Dropout, Batch Normalization
- Data Preprocessing & Data Augmentation
- Weight Initialization Strategies
- Babysitting Learning: Overfit detection, TensorBoard Monitoring
- Hands-on: MLP on MNIST / Tabular data (e.g. HR Analytics)
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- Master CNNs, object detection, segmentation, and deployment.
- Basics of Images, Image Preprocessing
- Convolution: 2D Conv, Forward & Backward
- Pooling, Padding, Stride, Transposed Conv
- CNN Architectures: LeNet, AlexNet, VGG, ResNet
- GPU vs CPU for DL
- Transfer Learning: Inception, MobileNet, fine-tuning
- Semantic Segmentation using UNet
- Object Detection YOLO, SSD, Region Proposal
- Bounding Box Regressor
- Siamese Networks for Similarity Search
- Hands-on:
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- Train text models from scratch and with BERT.
- Introduction to NLP and Use Cases
- Preprocessing: Tokenization, Lemmatization, Stopwords, Normalization
- Feature Extraction: BOW, TF-IDF, N-Grams
- Word Embeddings: Word2Vec, GloVe, Dense Vectors
- POS Tagging, Named Entity Recognition
- RNN, LSTM Forward Pass and BPTT
- Advanced LSTM Applications + Architectures
- Attention Mechanism + Encoder-Decoder
- Transformers, BERT, Hugging Face Pipelines
- NLP Evaluation Metrics: BLEU, ROUGE
- Hands-on:
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- Take models from notebooks to real-world applications.
- Saving & Loading Models (Pickle, Joblib, Keras)
- Flask vs FastAPI Serving ML models
- Streamlit/Gradio for Web Apps
- Hosting Models on Hugging Face Spaces, Streamlit Cloud
- MLflow Intro Model Tracking & Versioning
- Hands-on:
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- Build, evaluate, and deploy a mini AI project end-to-end.
- Project Selection: Tabular, CV, or NLP
- Data Collection/Exploration
- Preprocessing + Feature Engineering
- Model Training & Tuning
- Evaluation & Interpretation
- App Creation (Streamlit/Gradio)
- Deployment + Final Presentation/Submission
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