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Placements in Data Science: 1,342
Data Science Training in Irving with Certification
Learn data science in Irving with practical training on Python, R, SQL, Jupyter Notebook, Pandas, NumPy, Scikit-learn, Tableau, Power BI, Git, Docker, and cloud tools like AWS SageMaker and Google Cloud Vertex AI. You will work through the same core skills used for data analysis, machine learning, and reporting in real projects.
4.7/5 from 1,432 reviews
Hands-on data science course in Irving with practical lab work
Python, R, SQL, Pandas, NumPy, and Scikit-learn covered clearly
Tableau, Power BI, Excel, Git, and GitHub used in training
Machine learning foundations with TensorFlow, Keras, and PyTorch
Resume help, interview preparation, and certification support included
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
Get Job with our Guaranteed Placement Support Program
Placement Assistance for Data Science Professionals in Irving
Learning data science is only useful when it leads to clear job readiness. Inventateq focuses on the practical steps that help you move from course completion to interviews for roles like Data Analyst, Junior Data Scientist, BI Developer, and Data Engineer in Irving.
Our Signature Career Support:
Resume preparation for data science and analytics roles
Portfolio guidance using Python, SQL, and dashboard work
Mock interviews for analyst and junior data scientist roles
Mentor feedback on projects, communication, and problem solving
Placement guidance aligned to data analytics and machine learning jobs
Data Science Salary Insights in Irving
Irving has demand across analytics, reporting, business intelligence, and cloud-based data teams. With stronger project work and tool depth, salaries typically rise from analyst-level roles into senior data and machine learning positions.
Data Science Average Salary by Experience
Data Science Salary Insights in Irving
Irving has demand across analytics, reporting, business intelligence, and cloud-based data teams. With stronger project work and tool depth, salaries typically rise from analyst-level roles into senior data and machine learning positions.
Data Science Average Salary by Experience
Why Students Choose Our Data Science Course in Irving?
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 Irving
Inventateq teaches data science the practical way, with direct work on Python, SQL, Pandas, NumPy, Scikit-learn, Tableau, Power BI, and machine learning tools. The course is built around assignments, practical exercises, and project work so learners understand how to use the tools, not just read about them.
We stand apart through our commitment to:
Learn data science through practical Python, SQL, and analytics work
Build confidence with Pandas, NumPy, and Jupyter Notebook tasks
Work on machine learning basics with Scikit-learn and TensorFlow
Get mentor support while solving course assignments and projects
Attend flexible batches with placement guidance and interview prep
Live Online
Remote Learning
AI Online Live Classes
The live online experience is structured for Irving learners who want flexibility without losing trainer support. Sessions are interactive, practical, and built around the same data science tools and project exercises as the classroom format.
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 a structured start in data science and analytics.
Working professionals
Useful for people moving from reporting, support, or operations into data roles.
Career changers
Fits non-technical learners who want a clear path into analytics work.
Excel and reporting users
Ideal for people already handling data and wanting to move into Python, SQL, and BI.
Aspiring ML learners
Helpful for learners who want a foundation before machine learning roles.
Quick Highlights of Inventateq Data Science Course
Tools covered: Python, R, SQL, Tableau, Power BI, Git
Career support: Resume and interview guidance included
Flexible batches are available for learners in Irving.
Data Science Curriculum in Irving
1. Data Science Foundations and Environment Setup (Week 1)
W1
•Introduction to data science and the role it plays in analytics work
•Setting up the workflow with Anaconda and Jupyter Notebook
•Using Python basics for data handling and problem solving
•Starting with practical work inside a notebook-based environment
2. Python for Data Analysis (Week 2)
W2
•Working with Python for analysis tasks
•Using Pandas for data frames and tabular data
•Using NumPy for numerical operations
•Cleaning and preparing data for further analysis
3. SQL and Database Querying (Week 3)
W3
•Using SQL to extract and filter data
•Working with MySQL and PostgreSQL for structured data
•Running queries for reporting and analysis
•Understanding how databases support data science workflows
4. Data Visualization and Reporting (Week 4)
W4
•Creating charts and dashboards with Tableau
•Building business reports in Power BI
•Using Excel for quick analysis and presentation
•Turning raw data into clear visual insights
5. Statistics and Core Analytics (Week 5)
W5
•Applying statistical thinking to data problems
•Understanding patterns, trends, and variation in data
•Preparing data for analysis and interpretation
•Using analytical methods to support business decisions
6. Machine Learning Fundamentals (Week 6)
W6
•Introducing Scikit-learn for model building
•Learning basic supervised learning concepts
•Working through model training and evaluation
•Connecting machine learning with real data preparation
7. Advanced ML Libraries and Deep Learning (Week 7)
W7
•Using TensorFlow, Keras, and PyTorch in training tasks
•Understanding how different frameworks support model development
•Practicing model workflow concepts at a beginner-to-intermediate level
•Comparing tools for different machine learning use cases
8. Big Data and Distributed Processing (Week 8)
W8
•Introduction to Apache Spark for large-scale data work
•Understanding Apache Hadoop in the data ecosystem
•Seeing how big data tools support analytics pipelines
•Learning where distributed processing fits in real projects
9. Data Engineering and Storage Tools (Week 9)
W9
•Working with MongoDB and relational database concepts
•Using Snowflake for modern cloud data storage awareness
•Understanding data movement across systems
•Connecting storage choices to analytics and reporting needs
10. Version Control and Collaboration (Week 10)
W10
•Using Git and GitHub to track project changes
•Managing code versions during project work
•Sharing work clearly with mentors or team members
•Building habits that support professional collaboration
11. Deployment and Cloud Exposure (Week 11)
W11
•Introduction to Docker for packaging applications
•Getting exposure to AWS SageMaker and Google Cloud Vertex AI
•Understanding how model workflows move beyond local notebooks
•Learning the basics of cloud-based data science delivery
Rated 4.9/5
Why Inventateq for Data Science Training in Irving?
Inventateq keeps the course focused on practical data science skills that are useful in interviews and on the job. Learners work through Python, SQL, visualization, machine learning, and cloud tools in a structured way that matches real industry tasks.
Why Students Trust Inventateq Irving
Trainers explain concepts clearly and stay focused on hands-on learning
The syllabus covers tools commonly used in data and analytics roles
Students get support through assignments, practice, and projects
The environment is practical and suitable for beginners and career changers
Placement guidance helps learners prepare for real interview steps
Build Real Data Science Skills That Lead to Job-Ready Outcomes
By the end of the course, learners have practical exposure to data analysis, reporting, and machine learning workflows. The goal is to make you comfortable using the tools and presenting your work clearly.
Analyze Data with Python and SQL
Learn to clean, query, and organize data using the core tools employers expect. This builds the foundation for analyst and junior data science work.
Create Dashboards and Reports
Use Tableau, Power BI, and Excel to turn data into readable business visuals. These skills help when presenting insights to managers and teams.
Build Machine Learning Foundations
Work with Scikit-learn and related libraries to understand model training and evaluation. You gain the base needed for entry-level ML tasks.
Handle Real Project Work
Assignments and guided exercises help you apply what you learn instead of only memorizing concepts. That makes your portfolio stronger for interviews.
Use Modern Development Tools
Practice with Jupyter Notebook, Git, GitHub, and Docker so your workflow looks professional. These tools are part of how data teams collaborate and share work.
Prepare for Career Growth
Resume help, interview practice, and role guidance support your next step into analytics or data science jobs. The training is built around moving toward employment.
Certification for Data Science Training
This certification validates that you have completed practical training in Python, SQL, analytics libraries, visualization tools, and machine learning basics. It helps show employers that you have learned job-relevant tools through structured training.
Python, Pandas, NumPy, and Jupyter Notebook
Earn this certificate upon successful completion of our training program.
SQL and database query skills
Validate your skills with recognized industry credentials.
Tableau and Power BI reporting skills
Earn this certificate upon successful completion of our training program.
Scikit-learn and machine learning fundamentals
Validate your skills with recognized industry credentials.
Detailed Insights: Data Science Training in Irving
Students Frequently Asked Questions
Is this data science course in Irving suitable for beginners?
Yes, it is suitable for beginners who are ready to learn step by step. The course starts with the core tools and moves into analytics and machine learning in a structured order. If you are from a non-technical background, mentor support helps you follow the pace.
Will I work on hands-on projects during the course?
Yes, the training is practical and assignment-driven. You will use Python, SQL, dashboards, and machine learning tools in guided exercises and project work. This helps you build confidence before interviews.
Do you provide placement assistance after the course?
Yes, placement support is part of the training approach. You get help with resume preparation, mock interviews, and positioning your skills for data-related roles. The support is meant to help you move from learning to job search.
Can I take the course online if I am not in Irving?
Yes, the course is available through live online training. That makes it accessible to learners who want the same practical sessions without coming to the classroom. You still get mentor interaction and support during the training.
What job roles can I target after completing this course?
You can target roles such as Data Analyst, Junior Data Scientist, Business Intelligence Analyst, Machine Learning Engineer, and Data Science Intern. As your experience grows, the path can move toward senior data and analytics roles. The course is designed to support that progression.
How long does the training take?
The curriculum is structured across multiple modules, starting with foundations and moving into advanced tools and deployment exposure. The exact pace depends on the batch mode and schedule. You can discuss the batch option that fits your availability.
Which tools are covered in this data science training?
The course covers Python, R, SQL, Anaconda, Jupyter Notebook, Pandas, NumPy, Scikit-learn, TensorFlow, Keras, PyTorch, Tableau, Power BI, Excel, Spark, Hadoop, Git, GitHub, Docker, AWS SageMaker, and Google Cloud Vertex AI. These tools give you coverage across analysis, modeling, reporting, and deployment basics.
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