1. Data Science Foundations and Setup (Week 1)
- •Introduction to the data science field and course flow
- •Working with Python and R for analytics tasks
- •Setting up Anaconda and Jupyter Notebook
- •Understanding how data science projects are structured
6840+ Job Posting Available
-Recruiters prefer to hire Inventateq Students - Join Inventateq NowPlacements in Data Science: 1,342
Data Science Training in San Francisco with Certification
Learn data science in San Francisco with Python, SQL, Jupyter Notebook, Pandas, NumPy, Scikit-learn, TensorFlow, Tableau, Power BI, Git, GitHub, Docker, and cloud tools used in the curriculum. Build practical skills in analytics, machine learning, and model deployment through guided training and project work.
4.7/5 from 1,432 reviews
Hands-on data science course in San Francisco
Learn Python, SQL, Pandas, NumPy, and Scikit-learn
Work on guided projects and practical assignments
Get trained on Tableau, Power BI, Git, and Docker
Career support for data analyst and data science roles
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 San Francisco
Learning data science is only part of the goal. You also need support to present your skills clearly, handle interviews, and move into roles such as Data Analyst, Business Intelligence Analyst, Junior Data Scientist, and Data Science Intern in San Francisco.
Our Signature Career Support:
- Resume preparation for data science and analytics roles
- Portfolio guidance using project work and dashboards
- Mock interviews focused on Python, SQL, and ML basics
- Support for fresher and experienced candidate job profiles
- Placement mentoring aligned to data analyst and data scientist roles
Data Science Salary Insights in San Francisco
San Francisco hires for data science across tech, product, finance, operations, and analytics teams. Pay grows with stronger Python, SQL, machine learning, visualization, and deployment skills.
Data Science Average Salary by Experience
Data Science Salary Insights in San Francisco
San Francisco hires for data science across tech, product, finance, operations, and analytics teams. Pay grows with stronger Python, SQL, machine learning, visualization, and deployment skills.
Data Science Average Salary by Experience
Why Students Choose Our Data Science Course in San Francisco?
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 San Francisco
Inventateq teaches data science through practical use of Python, SQL, Pandas, NumPy, Scikit-learn, TensorFlow, Tableau, Power BI, Spark, Hadoop, Docker, and cloud platforms. The course follows a job-oriented structure, so you learn the tools in the same order you use them in real analytics and machine learning work.
We stand apart through our commitment to:
- Learn Python, SQL, and core data analysis tools step by step
- Build machine learning and dashboard projects with guided support
- Practice with notebook-based workflows in Jupyter Notebook and Anaconda
- Get mentor support for assignments, projects, and interview prep
- Choose classroom or live online training based on your schedule
Live Online
Remote Learning
AI Online Live Classes
The live online data science option lets San Francisco learners join classes from home without losing mentor interaction. You still get the same structured flow, practical examples, and project support across Python, SQL, machine learning, and visualization topics.
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
A good fit if you want to start with data analysis, Python, and SQL for entry-level roles.
Working professionals
Useful for professionals moving into analytics, reporting, or machine learning work.
Non-programmers
Suitable if you are beginning from scratch and want a structured path into data science.
Analysts and BI users
Helpful if you already use Excel, Tableau, or Power BI and want stronger technical skills.
Career switchers
Ideal if you want a practical route into data analyst or data scientist roles in San Francisco.
Quick Highlights of Inventateq Data Science Course
Course Duration
Mode: Classroom and live online training options are available.
Training style: Theory, practicals, assignments, and certification are part of the process.
Learning flow: Topics move from fundamentals to machine learning and deployment.
Batch options: Weekday and weekend batches help working professionals and students.
No prior data science experience is required to start the course.
Data Science Curriculum in San Francisco
W1
2. SQL and Data Handling (Week 2)
- •Using SQL for querying and working with databases
- •Introduction to MySQL and PostgreSQL
- •Basic data extraction and preparation tasks
- •Connecting data from source systems for analysis
3. Python for Data Analysis (Week 3)
- •Python syntax for data work
- •Using Pandas for data frames and cleaning
- •Using NumPy for numerical operations
- •Reading, transforming, and organizing datasets
4. Visualization and Reporting (Week 4)
- •Building charts and summaries with Tableau
- •Creating business reports with Power BI
- •Using Excel for fast analysis and reporting
- •Presenting insights in a clear, decision-ready format
5. Statistics and Core Analytics (Week 5)
- •Understanding statistical ideas used in analysis
- •Working with distributions, variance, and correlation
- •Applying analysis methods to real datasets
- •Using data patterns to support business questions
6. Machine Learning Basics (Week 6)
- •Introduction to supervised and unsupervised learning
- •Training models with Scikit-learn
- •Splitting data and checking model performance
- •Understanding common model evaluation methods
7. Supervised Learning Models (Week 7)
- •Working with regression and classification tasks
- •Applying algorithms for prediction problems
- •Comparing model results and tuning basic parameters
- •Using real examples to understand model behavior
8. Advanced Machine Learning Tools (Week 8)
- •Using TensorFlow, Keras, and PyTorch
- •Understanding how neural network workflows fit in
- •Exploring model training concepts at a practical level
- •Working on ML tasks beyond basic algorithms
9. Big Data Tools (Week 9)
- •Introduction to Apache Spark
- •Overview of Apache Hadoop
- •Understanding large-scale data processing concepts
- •Seeing how big data tools fit into analytics pipelines
10. Databases and Data Storage (Week 10)
- •Working with MongoDB for non-relational data
- •Using database systems in data projects
- •Managing structured and unstructured data sources
- •Linking storage choices to project requirements
11. Version Control and Collaboration (Week 11)
- •Using Git and GitHub for code tracking
- •Managing project changes and collaboration
- •Organizing notebooks, scripts, and code files
- •Working in a way that supports team-based projects
12. Deployment and Cloud Tools (Week 12)
- •Using Docker for packaging workflows
- •Introduction to AWS SageMaker
- •Introduction to Google Cloud Vertex AI
- •Understanding how model deployment fits in production
Rated 4.9/5
Why Inventateq for Data Science Training in San Francisco?
Inventateq focuses on practical data science training that builds job-ready skills in Python, SQL, machine learning, and visualization. The teaching is structured around the tools and workflows you will actually use in analytics and data science roles.
Why Students Trust Inventateq San Francisco
- Trainers explain data science topics with real examples and clear steps
- The curriculum covers both analysis and machine learning tools
- Students get support through assignments, projects, and revisions
- Training stays practical and focused on job use, not just definitions
- Flexible classroom and online options make the course easier to complete
Build Practical Data Science Skills That Support Real Career Moves
This course gives you structured practice with analysis, machine learning, dashboards, and deployment basics. You finish with a clearer understanding of how to work on data projects from raw data to usable results.
Work With Real Data Tools
Learn to use Python, SQL, Pandas, NumPy, and Jupyter Notebook for everyday data tasks. These tools form the base of the course and help you handle data cleaning, analysis, and reporting confidently.
Create Models and Evaluate Results
Move into Scikit-learn and related ML tools to build and test models. You learn how common prediction workflows work and how to compare results in a practical setting.
Build Dashboards and Reports
Use Tableau, Power BI, and Excel to present findings clearly. This helps you turn raw data into reports that can be used by business teams.
Understand Big Data Workflows
Get introduced to Spark and Hadoop so you know how larger datasets are handled. This gives you a broader view of data engineering and analytics systems.
Use Modern Development Practices
Work with Git, GitHub, Docker, and cloud tools as part of the project flow. These are important for collaboration, packaging, and deployment readiness.
Prepare for Job Roles
Training is aligned to roles such as Data Analyst, Junior Data Scientist, BI Analyst, and ML Engineer. That makes the course useful for both entry-level and growth-stage careers.
Certification for Data Science Training
The certification validates that you have completed structured training in data science tools, analysis methods, and project-based learning. It helps show employers that you have worked with the core software used in data analyst and data scientist roles.
Python, SQL, and Jupyter Notebook
Earn this certificate upon successful completion of our training program.
Pandas, NumPy, and Scikit-learn
Validate your skills with recognized industry credentials.
Tableau, Power BI, and Excel
Earn this certificate upon successful completion of our training program.
Docker, Git, and GitHub
Validate your skills with recognized industry credentials.
Detailed Insights: Data Science Training in San Francisco
Students Frequently Asked Questions
Is this data science course suitable for beginners?
Yes, it is suitable for beginners who want a structured start in data science. The course begins with Python, SQL, and data handling basics before moving into machine learning and deployment topics. Mentors guide you through the practical parts so you can keep up even if you are new to the field.
What tools will I learn in this data science training?
You will work with Python, R, SQL, Anaconda, Jupyter Notebook, Pandas, NumPy, Scikit-learn, TensorFlow, Keras, PyTorch, Tableau, Power BI, Excel, Spark, Hadoop, Git, GitHub, Docker, and cloud tools. The focus is on tools used in real data science and analytics work. You also learn how these tools fit together in one workflow.
Will I get hands-on projects in the course?
Yes, the course is practical and includes project work, assignments, and guided exercises. You will use the tools in real scenarios instead of only studying concepts. That helps you build a portfolio and understand how data science work is done from start to finish.
Does Inventateq provide placement assistance for data science roles?
Yes, placement support is part of the training process. Inventateq helps with resume preparation, mock interviews, and guidance for job roles such as Data Analyst, Junior Data Scientist, BI Analyst, and ML Engineer. The support is aimed at helping you present your skills clearly to employers.
Can working professionals join the online batch from San Francisco?
Yes, the live online batch is available for learners in San Francisco who need flexibility. It follows the same practical training structure as classroom learning and includes mentor interaction. This is useful if you want to learn without traveling to a physical center.
How long does the data science training take?
The course follows a structured curriculum across multiple weeks of training. It covers foundations, SQL, Python analysis, visualization, machine learning, big data exposure, and deployment basics. The exact pace can vary by batch mode and schedule.
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