Data Science Training in Vizag • Softenant Technologies

Data Science Course in Vizag | Training in Visakhapatnam

Build a complete data science workflow with Python, SQL, statistics and machine learning. At Softenant Technologies in Akkayyapalem, Visakhapatnam, practical training connects data cleaning, exploration, model evaluation, visualisation and project documentation through classroom and instructor-led online options.

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Mentor-Led LearningPractical explanations and doubt clarification
Hands-on ProjectsML + Big Data cases
Placement SupportResume, portfolio and interview guidance

Data Science Course in Visakhapatnam – Details

Course Mode

Classroom training in Visakhapatnam, with online, weekday, and weekend batch options.

Batch Schedule

Flexible timings for students, freshers, and working professionals. Contact us for the latest Data Science batch dates.

Certification

Course completion certificate, resume preparation, interview practice, and portfolio guidance for entry-level analytics roles.

Tools Covered

Python, SQL, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, machine learning workflows, and data visualization.

Projects

Hands-on projects in data cleaning, exploratory analysis, dashboards, predictive models, and business problem solving.

Offline and Online

Choose classroom training in Vizag or live online sessions with mentor support, practical tasks, and flexible batch options.

Comprehensive Data Science Curriculum

Learn step-by-step from fundamentals to advanced data science workflows and build a strong portfolio through hands-on projects, tools, and interview preparation.

Module 1: Introduction to Data Science +
  • Data science lifecycle and business problem solving
  • Roles: Data Analyst, Data Scientist, ML Engineer
  • Tools, workflows, and project expectations
Module 2: Python Fundamentals for Data Science +
  • Syntax, operators, loops, functions, and OOP basics
  • Working with files, exceptions, and reusable code
  • Problem solving with Python programs
Module 3: NumPy and Pandas +
  • Arrays, indexing, vectorized operations, and reshaping
  • Series and DataFrames for structured analysis
  • Filtering, grouping, joins, and aggregations
Module 4: Data Cleaning and Preprocessing +
  • Handling missing values, duplicates, and inconsistent records
  • Encoding, scaling, transformation, and feature preparation
  • Outlier detection and data quality checks
Module 5: SQL for Data Analysis +
  • SELECT queries, filtering, sorting, and aggregation
  • Joins, subqueries, views, and case-based analysis
  • Using SQL with reporting and analytics datasets
Module 6: Statistics for Data Science +
  • Descriptive statistics, probability, and distributions
  • Sampling, hypothesis testing, and confidence intervals
  • Correlation, covariance, and statistical inference
Module 7: Exploratory Data Analysis +
  • Pattern detection, trend analysis, and segmentation
  • EDA with Python notebooks and business questions
  • Communicating findings through structured analysis
Module 8: Data Visualization and Storytelling +
  • Matplotlib and Seaborn for chart creation
  • Visualization best practices and storytelling techniques
  • Dashboard thinking for decision-making reports
Module 9: Machine Learning Foundations +
  • Supervised vs unsupervised learning
  • Train-test split, model workflow, and evaluation basics
  • Scikit-learn introduction and practical experimentation
Module 10: Regression and Classification +
  • Linear regression, logistic regression, KNN, and decision trees
  • Accuracy, precision, recall, F1-score, and confusion matrix
  • Model comparison and performance interpretation
Module 11: Clustering and Unsupervised Learning +
  • K-means, hierarchical clustering, and dimensionality reduction overview
  • Customer segmentation and grouping use cases
  • Practical evaluation of unsupervised outputs
Module 12: Feature Engineering and Model Tuning +
  • Feature selection, feature creation, and transformation
  • Cross-validation, hyperparameter tuning, and pipelines
  • Improving model quality and reducing overfitting
Module 13: Big Data and Advanced Topics +
  • Big data concepts, Spark overview, and scalable workflows
  • Introduction to NLP, time series, and recommendation ideas
  • Understanding deployment, monitoring, and production thinking
Module 14: Power BI, Projects, and Business Reporting +
  • Interactive dashboard fundamentals and KPI reporting
  • Case studies combining SQL, Python, and visualization
  • Presenting insights for business and management stakeholders
Module 15: Capstone, Resume, and Interview Preparation +
  • End-to-end capstone project with documentation and presentation
  • Portfolio building, GitHub/project walkthrough support
  • Resume preparation, mock interviews, and placement guidance
Module 16: Deep Learning Fundamentals +
  • Neural network basics, activation functions, and model training flow
  • Working with TensorFlow/Keras for beginner-friendly deep learning
  • Understanding overfitting, validation, and practical model improvement
Module 17: Natural Language Processing +
  • Text cleaning, tokenization, vectorization, and feature extraction
  • Sentiment analysis, text classification, and chatbot use-case basics
  • Building simple NLP projects for portfolio and interview discussion
Module 18: Time Series Forecasting +
  • Trend, seasonality, moving averages, and forecasting concepts
  • Sales, demand, finance, and operations forecasting examples
  • Evaluating forecast accuracy and explaining business impact
Module 19: Model Deployment and MLOps Basics +
  • Saving models, creating prediction workflows, and API deployment overview
  • Introduction to Streamlit/Flask apps for sharing ML results
  • Monitoring, versioning, and production-readiness fundamentals
Module 20: Final Portfolio and Mock Interview +
  • Prepare final project documentation, dashboards, and GitHub presentation
  • Practice project explanation, technical questions, and scenario-based answers
  • Resume polishing and role-specific interview preparation for data jobs

Why Choose Our Data Science Training?

Mentor-Led Learning

Mentor-led learning with practical explanations and doubt clarification.

Hands-on Projects

Real-world projects in data analysis, ML models, and big data processing.

Placement Support

Resume, portfolio and mock-interview guidance for entry-level data roles.

Course Completion Certificate

Course completion certificate from Softenant Technologies.

Small Batch Sizes

Limited seats per batch so every student gets personal attention and mentoring.

Affordable Pricing

Quality training at competitive prices with flexible payment options and student discounts.

Ready to Start Your Data Science Journey?

Join our next batch in Vizag and take the first step toward an exciting career in a high-demand field.

Data Science Portfolio Practice Ideas

1. Training Enquiry Conversion Analysis

Use a synthetic dataset of course enquiries to analyse enquiry sources and build a simple conversion classifier. Use only information available before the conversion outcome.

Portfolio evidence: Cleaning notebook, baseline comparison, precision and recall results, and a short explanation of false positives and false negatives. Refer to the experiment design guide when defining the evaluation.

2. Daily Sales Forecast for a Vizag Retail Scenario

Use public or synthetic daily sales data to compare a simple seasonal forecast with another forecasting method. Keep the most recent period separate for testing.

Portfolio evidence: Dated dataset, forecast chart, error comparison and discussion of stockouts or unusual trading days. Follow the forecasting project guide for baseline and backtesting principles.

3. Customer Segmentation for a Retail Campaign

Use synthetic transaction data to create recency, frequency and monetary-value features. Compare simple rule-based segments with clustering and explain whether the resulting groups are useful.

Portfolio evidence: Feature definitions, segment profiles, cluster evaluation and a proposed campaign brief. Clearly separate measured findings from suggested business actions.

Tools and Technologies Covered in Data Science Training

Build practical skills with the most-used tools for data cleaning, analysis, machine learning, visualization, and business reporting workflows.

Python

Programming

SQL

Queries & Databases

Pandas & NumPy

Data Processing

Matplotlib

Visualization

Scikit-learn

ML Models

TensorFlow

Deep Learning

Jupyter

Notebooks

Power BI

Dashboards

Data Science Learning Milestones

Follow these milestones from reliable data preparation to reproducible portfolio work.

  • Prepare reliable data: clean records, join tables and document missing values and assumptions.
  • Investigate a question: use statistics and visualisations to explain patterns and uncertainty.
  • Evaluate a model: compare against a simple baseline and test on data excluded from training.
  • Present the work: provide a reproducible notebook, dataset description, results and limitations.
Focus: practical execution, project explanation, and confidence for entry-level data roles.

Career Opportunities After This Course

Data Scientist
Modeling • Insights • Decision making
Data Analyst
Reporting • KPI dashboards
Machine Learning Engineer
ML pipelines • Deployment basics
AI Specialist
AI use-cases • Applied solutions

Role requirements and salaries vary by employer, experience and location. Course completion does not guarantee a particular role or salary.

How Data Science is Transforming Industries

Healthcare

Predictive analytics supports early detection and personalized treatment planning.

Finance

Fraud detection, risk management and algorithmic trading powered by data science.

Retail

Segmentation, demand forecasting and personalized marketing driven by data.

Manufacturing

Predictive maintenance and supply chain optimization improve efficiency and reduce cost.

Education

Learning analytics helps institutes track student progress, personalize support, and improve outcomes.

Logistics

Route optimization, delivery forecasting, and demand planning help teams reduce delays and costs.

What Our Students Say

The hands-on projects helped me gain practical experience, and the placement support was excellent. I landed a data scientist role within a month of completing the course.

RK
Ravi KumarData Scientist

The trainers were knowledgeable, and the course content matched current industry workflows. I moved from marketing into a data analysis role with confidence.

PS
Priya SharmaData Analyst

I came from a non-technical background, but the structured training and project practice helped me understand machine learning step by step.

AR
Anil ReddyML Engineer

Trusted by Softenant Technologies Students

Read Our Google Reviews

Why Softenant Stands Out for Data Science Training in Vizag

Mentor-Led Training

Learn with classroom guidance, doubt clarification, and step-by-step support for Python, SQL, statistics, and machine learning.

Hands-On Projects

Practice data cleaning, EDA, dashboards, model building, and deployment-ready workflows using real business-style datasets.

Portfolio Readiness

Build project explanations, GitHub visibility, resume points, and interview stories that help freshers present skills clearly.

Placement Support

Get resume review, mock interviews, role guidance, and next-batch support for Data Analyst, Data Scientist, and ML roles.

Frequently Asked Questions

What are the current fees, duration and batch options? +

Contact Softenant Technologies at +91 9393969628 for the current fee, duration and available classroom or online batches. Ask about practical sessions, project support and any separate software, cloud or examination costs.

Is this Data Science course available in Visakhapatnam? +

Yes. Softenant offers Data Science training in Vizag and Visakhapatnam, with classroom and flexible online learning options.

Do I need prior programming experience to enroll? +

No. We start from basics and move to Python, SQL, statistics, machine learning, and big data step-by-step.

Will I work on real Data Science projects? +

Yes. You will practice data cleaning, exploratory analysis, visualization, predictive models, and portfolio-ready project workflows.

Is placement assistance provided? +

Yes. We provide resume support, mock interviews, portfolio guidance, job alerts, and placement guidance.

Where can I join Data Science training in Visakhapatnam? +

Softenant Technologies provides Data Science training in Visakhapatnam. See the contact section for the full training-centre address, and ask about current classroom, weekday, weekend and instructor-led online batch options.

What does the Data Science course in Visakhapatnam cover? +

The course covers Python, SQL, statistics, data cleaning, visualization, machine learning, model evaluation and guided portfolio projects. Contact the institute to confirm the latest batch syllabus and schedule.

Should I choose Data Science or Data Analytics? +

Data Analytics usually emphasises reporting, dashboards and explaining business performance. Data Science adds statistical modelling, prediction and model evaluation. Compare the Data Analytics course if your immediate goal is SQL, dashboards and business reporting.

How can I judge whether a machine learning project is useful? +

Check the problem definition, data quality, baseline, evaluation method and errors. A useful project explains where the model works, where it fails and which information was available when predictions were made.

Do I need to complete a separate Python course first? +

The Data Science curriculum includes Python fundamentals. Learners who want additional programming practice can explore Python Training in Vizag before or alongside their data science preparation.

Join the Next Batch in Vizag

Get weekly updates, offers, and course schedules. We’ll guide you to become job-ready.

Contact Us

Flat No: 101, Geetha Mansion, II, Jn, opp. Andhra Bank, Akkayyapalem, Visakhapatnam, AP 530016
Phone: 9393 9696 28 • Email: info@softenant.com

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