1. Programming Foundations (Week 1)
- •Introduction to Python for data science
- •Working in Anaconda and Jupyter Notebook
- •Basic data types, variables, and control flow
- •Writing simple programs for data handling
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-Recruiters prefer to hire Inventateq Students - Join Inventateq NowPlacements in Data Science: 1,342
Data Science Training in Texas with Certification
Learn data science in Texas with Python, R, SQL, Jupyter Notebook, Pandas, NumPy, Scikit-learn, TensorFlow, Keras, PyTorch, Tableau, Power BI, Git, GitHub, Docker, and cloud tools used in real projects. This course is built for learners who want practical data analysis, machine learning, and deployment skills, not just theory.
4.7/5 from 1,432 reviews
Hands-on training with Python, SQL, Pandas, NumPy, and Jupyter Notebook
Machine learning practice with Scikit-learn, TensorFlow, Keras, and PyTorch
Data visualization work using Tableau and Power BI
Project-based learning with assignments, certification, and interview preparation
Placement support for data analyst, data scientist, BI, and ML roles in Texas
Job Interview
Guarantee Program
14,200+ (Placed)
Freshers to IT
7,100+ (Placed)
Non-IT to Tech
5,800+ (Placed)
Career Gap Fillers
6,400+ (Placed)
Upskilling Success
24,999₹
In 90 Days + Placement
Course Fee:₹24,999
Duration:60 Days
Mode:Classroom & Online
Free Session
1 Hour Training Session
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Placement Assistance for Data Science Professionals in Texas
Learning data science is only one part of the job search. Students also need a clear resume, project proof, and interview practice that matches the roles companies hire for in Texas. Inventateq supports that process with practical placement guidance tied to data analyst, data scientist, BI, and machine learning roles.
Our Signature Career Support:
- Resume preparation focused on data science roles
- Portfolio and project guidance using Python, SQL, and Tableau
- Mock interviews for analyst, BI, and machine learning roles
- Career mentoring for fresher and experienced candidates
- Support with interview readiness and job application strategy
Texas Data Science Salary Insights
Data science roles in Texas are hired across analytics, finance, operations, business intelligence, and product teams. Pay grows with experience as you move from reporting and analysis into machine learning, data engineering, and leadership roles.
Data Science Average Salary by Experience
Texas Data Science Salary Insights
Data science roles in Texas are hired across analytics, finance, operations, business intelligence, and product teams. Pay grows with experience as you move from reporting and analysis into machine learning, data engineering, and leadership roles.
Data Science Average Salary by Experience
Why Students Choose Our Data Science Course in Texas?
4.7/5 Google Rating | 1,432+ Verified Reviews
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Success Result: Our students are competing at global levels. Watch their placement journey here.
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About Inventateq Data Science Training Institute in Texas
Inventateq teaches data science the practical way: learn the tools, practice the concepts, and work through assignments that reflect real data work. The syllabus moves from Python, SQL, and visualization into machine learning, deep learning, cloud tools, and deployment, so students can build job-ready skills step by step.
We stand apart through our commitment to:
- Learn Python, SQL, Pandas, and NumPy with guided practice
- Work on machine learning and deep learning using Scikit-learn, TensorFlow, Keras, and PyTorch
- Build dashboards and reports with Tableau and Power BI
- Get mentor support while solving assignments and projects
- Attend classes in a format that fits weekday or weekend schedules
Live Online
Remote Learning
AI Online Live Classes
Live online classes make the same data science training accessible to learners across Texas. You can follow the modules in real time, ask questions during class, and complete assignments with mentor guidance from anywhere.
Live interactive classes with practical coding demonstrations
Recorded sessions available for revision and practice
Weekly assignments with mentor feedback and guidance
Real projects covering Generative AI, LLMs, and Agentic AI
Online career guidance and interview preparation support
Data Science Training Program
Fresh graduates
Good for students who want to move into analytics or entry-level data roles with practical tools and projects.
Working professionals
Useful for professionals who want to shift into data analyst, BI, or machine learning roles.
Non-programmers
Suitable for learners who are new to coding and want a clear start with Python, SQL, and notebooks.
Analysts and reporting users
Fits people already working with Excel or reports who want deeper data skills and automation.
Career switchers
Ideal for anyone in Texas looking to build a data science profile with hands-on training and placement support.
Quick Highlights of Inventateq Data Science Course
Course Duration
Mode: Classroom and live online training
Schedule: Weekday and weekend batches available
Format: Theory, practicals, assignments, and certification
Support: Free demo classes available before joining
No prior data science experience is required to begin.
Data Science Curriculum in Texas
W1
2. Data Structures and Core Libraries (Week 2)
- •Using Pandas for data frames and data cleaning
- •Working with NumPy for numerical operations
- •Loading, exploring, and transforming datasets
- •Handling missing values and basic preprocessing
3. SQL and Database Basics (Week 3)
- •Writing SQL queries for data extraction
- •Using MySQL and PostgreSQL concepts
- •Working with tables, joins, filters, and aggregations
- •Understanding database use in analytics projects
4. Data Visualization and Reporting (Week 4)
- •Creating visual reports with Tableau
- •Building dashboards in Power BI
- •Using Excel for analysis and reporting tasks
- •Presenting trends and insights clearly
5. Statistics for Data Science (Week 5)
- •Descriptive statistics for data understanding
- •Probability basics for data analysis
- •Distributions and correlation concepts
- •Using statistics to support model decisions
6. Machine Learning Fundamentals (Week 6)
- •Introduction to Scikit-learn
- •Supervised and unsupervised learning basics
- •Model training, testing, and evaluation
- •Classification and regression workflow
7. Advanced Machine Learning (Week 7)
- •Feature engineering and model tuning
- •Improving model performance with practical methods
- •Working with real datasets and case exercises
- •Comparing different machine learning approaches
8. Deep Learning (Week 8)
- •Neural network basics
- •Using TensorFlow and Keras
- •Understanding model layers and training
- •Practical deep learning problem solving
9. PyTorch and Model Building (Week 9)
- •Introduction to PyTorch
- •Building and testing neural models
- •Training workflow and parameter adjustment
- •Hands-on model development practice
10. Big Data Tools (Week 10)
- •Introduction to Apache Spark
- •Overview of Apache Hadoop
- •Working with large-scale data processing concepts
- •Understanding where big data tools fit in projects
11. Cloud and ML Deployment (Week 11)
- •Using AWS SageMaker for machine learning workflows
- •Google Cloud Vertex AI overview
- •Introduction to Docker for packaging work
- •Basics of deployment and model sharing
12. Project Work and Career Prep (Week 12)
- •Applying Python, SQL, and ML tools in a project
- •Using Git and GitHub for version control
- •Preparing assignment-based project output
- •Resume preparation and interview readiness
Rated 4.9/5
Why Inventateq for Data Science Training in Texas?
Inventateq focuses on job-oriented data science learning with practical tool usage from the first module. Students work through Python, SQL, visualization, machine learning, and deployment topics in a sequence that matches real project workflows.
Why Students Trust Inventateq Texas
- Experienced trainers who explain concepts clearly
- Updated curriculum built around current data tools
- Supportive learning environment for beginners and switchers
- Practical assignments that build confidence
- Placement-oriented training with interview preparation
Build Data Science Skills That Support Real Career Growth
Learners finish the course with practical experience across analysis, modeling, visualization, and deployment basics. The training is designed to help students speak confidently about tools, projects, and outcomes in interviews.
Work With Real Data Tools
You practice Python, SQL, Pandas, NumPy, and Jupyter Notebook in a structured way. That gives you the base needed for analysis and model-building tasks.
Build Machine Learning Projects
The syllabus moves into Scikit-learn, TensorFlow, Keras, and PyTorch. You learn how models are trained, tested, and improved with actual practice.
Create Business Dashboards
Tableau, Power BI, and Excel are used to present insights clearly. This helps you show business-facing results, not just code.
Handle Larger Data Work
Apache Spark and Hadoop introduce you to big data concepts. You understand how data processing scales beyond small datasets.
Prepare for Deployment Work
Docker, AWS SageMaker, Google Cloud Vertex AI, and Snowflake basics show how data science connects to production environments.
Enter Interviews More Prepared
Resume preparation, assignments, and mock interview support help you present your project experience better. That makes the course useful for job applications in Texas.
Certification for Data Science Training
The certification confirms that you have completed practical training in data analysis, machine learning, visualization, and deployment basics. It supports your profile when applying for analyst, BI, and data science roles.
Python, Pandas, NumPy, and Jupyter Notebook
Earn this certificate upon successful completion of our training program.
SQL, MySQL, and PostgreSQL
Validate your skills with recognized industry credentials.
Scikit-learn, TensorFlow, Keras, and PyTorch
Earn this certificate upon successful completion of our training program.
Tableau, Power BI, and Git/GitHub
Validate your skills with recognized industry credentials.
Detailed Insights: Data Science Training in Texas
Students Frequently Asked Questions
Is this data science course beginner-friendly?
Yes, the course is designed for beginners as well as career switchers. It starts with programming, notebooks, and data handling before moving into machine learning and deployment topics. You do not need prior data science experience to begin.
Will I work on practical projects?
Yes, the course is project-based and assignment-driven. You will work with Python, SQL, visualization tools, and machine learning libraries in hands-on exercises. The goal is to help you build useful project experience for interviews.
Do you provide placement support?
Yes, placement support is part of the training process. You get help with resume preparation, project presentation, mock interviews, and job readiness. The support is aimed at roles such as Data Analyst, BI Analyst, and Junior Data Scientist.
Can someone from a non-technical background join?
Yes, non-technical learners can join if they are willing to practice regularly. The course covers the basics in a clear sequence and then builds up to advanced topics. Mentor guidance helps you move from fundamentals to job-oriented skills.
Is live online training available in Texas?
Yes, live online training is available for students in Texas. You can attend the same practical sessions, ask questions live, and complete assignments with mentor support. This works well for learners who prefer studying from home or while working.
How long does the course take?
The course is structured across multiple weeks with a clear progression from basics to projects and deployment. The exact pace can vary based on batch mode and learning schedule. Weekday and weekend options help students manage the duration more comfortably.
Which tools are included in the training?
The course includes Python, R, SQL, Anaconda, Jupyter Notebook, Pandas, NumPy, Scikit-learn, TensorFlow, Keras, PyTorch, Tableau, Power BI, Git, GitHub, Docker, and cloud tools such as AWS SageMaker and Google Cloud Vertex AI. These tools are used across analysis, machine learning, reporting, and deployment topics. That makes the training practical for real data science work.
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