The Data Science Course with Tableau Online at Online IT Guru is designed to help learners gain a deep understanding of data science concepts and data visualization using Tableau. This comprehensive training program combines core data science skills with the power of Tableau to turn raw data into actionable insights.
Whether you are a fresh graduate, working professional, or an aspiring data analyst, this course provides practical, hands-on experience through live projects, real-world case studies, and interactive assignments.
Learn to collect, clean, analyze, and visualize data effectively while preparing for globally recognized certifications. The Data Science Course ensures you gain the technical and analytical skills sought after in today’s job market.
Why Choose an Online IT Guru for a Data Science Course with Tableau?
At Online IT Guru, we bring together the best features of live online training and self-paced learning to give you flexibility and quality. Our Data Science Course with Tableau provides:
- 60+ hours of live instructor-led sessions
- 2 real-time industry projects
- 35+ assignments and hands-on exercises
- Access to downloadable resources and lifetime LMS
- 24x7 technical support
- Certification guidance aligned with latest industry standards
- Placement assistance with top recruiters
The program is tailored to ensure you are job-ready with both data science and data visualization expertise.
Who Should Enroll in a Data Science Online Course with Tableau?
This course is ideal for:
- Software developers looking to switch to data science
- Business analysts seeking data visualization skills
- Statisticians and mathematicians wanting to apply their skills in data science
- Fresh graduates aiming for a data science career
- IT professionals and database administrators expanding into analytics
- Managers and decision-makers wanting data-driven insights
Prerequisites for Data Science Course
No prior experience in data science is required. However, knowledge of basic programming (preferably Python), statistics, Online IT Guru and mathematics will be helpful. The course starts from fundamentals and progresses to advanced concepts.
Key Features of Data Science Course
1. Lifetime Access
Get lifetime access to LMS containing recorded sessions, study materials, assignments, and installation guides.
2. Assignments and Case Studies
Reinforce your learning through topic-wise assignments and industry-based case studies.
3. 24x7 Support
Round-the-clock technical support from experienced professionals to resolve all queries.
4. Certification Assistance
Structured curriculum and practice tests to help you clear certifications with ease.
5. Job Assistance
Dedicated placement team connects your profile with companies hiring data science professionals.
Data Science Course Syllabus
Module 1: Introduction to Data Science
This module lays the foundation for your data science journey. You will gain a clear understanding of what data science is and how it is transforming industries globally. The module starts with an overview of data science and its applications, showcasing how organizations leverage data-driven insights to make smarter decisions. From healthcare and finance to retail and entertainment, data science is revolutionizing business processes and customer experiences.
Next, you’ll learn about the data science lifecycle, which outlines the sequence of steps involved in solving business problems using data. This includes problem definition, data collection, data preparation, modeling, evaluation, and deployment. Understanding this lifecycle is crucial, as it forms the backbone of any data science project.
Finally, the module discusses various business problems solved by data science. These could include customer churn prediction, fraud detection, recommendation systems, inventory forecasting, and more. Real-world case studies will help you appreciate the value data science brings to solving complex challenges.
Module 2: Python for Data Science
Python is the go-to programming language for data scientists due to its simplicity and rich ecosystem of libraries. This module introduces you to core Python programming concepts, including variables, loops, functions, and object-oriented programming principles.
You will also master essential data structures and libraries that are vital for data science. Libraries like NumPy help with numerical computations, Pandas simplifies data manipulation and analysis, while Matplotlib is used for creating basic visualizations. These tools enable you to handle data efficiently and extract meaningful insights.
Module 3: Data Acquisition and Cleaning
In this module, you will dive into the critical steps of obtaining and preparing data for analysis. You will learn how to import and export data from different file formats and databases, such as CSV, Excel, SQL, and web APIs.
Next, the module focuses on data preprocessing techniques, which include cleaning, transforming, and normalizing data. You’ll address common challenges such as handling missing values, detecting outliers, and converting data into a format suitable for analysis. This step ensures your dataset is clean and reliable, which is essential for building accurate models.
Module 4: Statistics and Mathematics for Data Science
A strong grasp of statistical concepts and mathematical principles is vital for data science. This module covers descriptive statistics, which helps you summarize data using measures like mean, median, mode, and standard deviation. You will also explore inferential statistics to make predictions and inferences about populations based on sample data.
The module delves into probability theory, teaching you how to quantify uncertainty and model random events. Concepts such as probability distributions, Bayes’ theorem, and conditional probability are covered.
Finally, you will learn about hypothesis testing, a critical tool for making data-driven decisions. This includes formulating null and alternative hypotheses, performing t-tests, and interpreting p-values to determine statistical significance.
Module 5: Machine Learning Algorithms
This module introduces you to the heart of data science — machine learning. You will explore supervised learning algorithms, such as regression (for predicting continuous variables) and classification (for categorizing data into predefined classes). Popular algorithms like linear regression, logistic regression, decision trees, and random forests will be discussed in depth.
In the unsupervised learning section, you’ll learn about clustering techniques like k-means, and dimensionality reduction methods such as PCA (Principal Component Analysis). These techniques help in identifying patterns and structures in unlabeled data, and in reducing the complexity of datasets.
Module 6: Introduction to Tableau
Data visualization is a key part of data science as it helps communicate insights effectively. This module introduces Tableau, one of the most powerful and widely used business intelligence tools. You will learn about Tableau’s architecture and interface, and how to navigate its features with ease.
You will also practice connecting Tableau to various data sources, such as spreadsheets, databases, and cloud services. Finally, you’ll create basic charts and visualizations like bar charts, line graphs, and pie charts, which help in presenting data clearly and concisely.
Module 7: Advanced Tableau Visualizations
This module takes your Tableau skills to the next level. You’ll learn to design compelling dashboards and stories that combine multiple charts and visual elements into interactive reports.
Key features such as parameters, filters, and calculated fields will allow you to add flexibility and dynamic behavior to your visualizations. You will also explore advanced charts, including heat maps (for density visualization), scatter plots (for relationship analysis), and tree maps (for hierarchical data).
Module 8: Integration of Data Science with Tableau
Here, you will discover how to integrate data science models with Tableau to deliver end-to-end solutions. This includes preparing data models for visualization, ensuring the output from machine learning algorithms is ready for business users to explore visually.
You will practice sharing insights using dashboards, making it easy for stakeholders to interact with the data and derive conclusions. The module also covers interactive reporting, allowing users to drill down into data, apply filters, and customize views based on their needs.
Module 9: Real-Time Projects
The final module is project-based, giving you the opportunity to apply everything you’ve learned. You will work on an end-to-end data science project, involving data collection, cleaning, analysis, model building, and creating a Tableau dashboard for visualization.
Additionally, you will gain exposure to model deployment and monitoring, learning how to put your machine learning models into production and track their performance over time. This ensures that your solutions continue to deliver value in real-world settings.
This Data Science Course with Tableau offers a comprehensive pathway from beginner to advanced levels. Whether you are a fresh graduate, a working professional looking to upskill, or someone transitioning into data science, this course equips you with the practical knowledge to succeed. By integrating machine learning and Tableau, the course ensures you are not just able to analyze data, but also present it in a form that drives decision-making.
Data Science Projects
Project 1: Predictive Analytics for Retail
Build a machine learning model to predict sales and visualize the results in Tableau dashboard to drive business decisions.
Project 2: Customer Segmentation
Perform customer clustering using unsupervised learning and create interactive Tableau dashboards for stakeholder reporting.
Training Options
Live Online Training
- Instructor-led live sessions
- Flexible schedules
- Access to recordings and study materials
- Certification support
Corporate Training
- Custom course content
- Full-day schedules and flexible timings
- Live or self-paced learning options
Benefits of Data Science Online Course with Tableau
- Gain job-ready skills in Python, Machine Learning, and Tableau
- Practical exposure through real-time projects
- Preparation for data science and Tableau certifications
- Flexible learning modes: live, self-paced, and corporate
- Global placement support
Job Roles After Completing Data Science Course
Data Scientist
A Data Scientist is a professional who extracts meaningful insights from large volumes of structured and unstructured data. This role requires expertise in programming (often Python or R), statistics, machine learning, and data visualization. Data scientists design predictive models, run experiments, and communicate results through dashboards and reports. Skills from the Data Science Course — like Python, machine learning algorithms, and Tableau visualizations — are directly relevant for this career.
Key skills: Python, machine learning, statistics, data wrangling, data visualization, model deployment.
Data Analyst
A Data Analyst focuses on processing, analyzing, and interpreting data to help businesses make informed decisions. They clean and organize data, create reports, and identify trends. Unlike data scientists, data analysts typically work more with descriptive analytics rather than predictive models. Tableau is a critical tool for data analysts to create interactive reports and dashboards.
Key skills: SQL, Excel, Python (Pandas), data cleaning, Tableau dashboards, business reporting.
Business Intelligence (BI) Developer
A BI Developer designs and develops strategies for businesses to quickly find the information they need to make decisions. They build data pipelines, create data warehouses, and develop reports and dashboards. Tableau is often at the center of their work to visualize and share business data. BI developers collaborate closely with database administrators and business stakeholders.
Key skills: ETL tools, data modeling, SQL, Tableau, data warehouse design, dashboard creation.
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models at scale. This role focuses on developing algorithms, optimizing model performance, Online IT Guru and integrating models into production systems. The job bridges the gap between data science and software engineering. Strong Python skills, knowledge of machine learning algorithms, and an understanding of data pipelines are essential.
Key skills: Python (scikit-learn, TensorFlow), machine learning algorithms, model deployment, APIs, cloud platforms, monitoring ML models.
Data Visualization Specialist (Tableau)
A Data Visualization Specialist focuses on converting complex datasets into meaningful, easy-to-understand visual narratives. They create dashboards, reports, and interactive visual tools using Tableau (and sometimes other BI tools). Their work ensures that data insights are accessible to non-technical audiences.
Key skills: Tableau (advanced visualizations, dashboards, calculated fields, parameters), storytelling with data, UX for dashboards, basic data prep.
Analytics Consultant
An Analytics Consultant works with client organizations to identify business challenges and provide data-driven solutions. Data Science Course blends technical expertise with business acumen. Analytics consultants often use a combination of data science techniques and visualization tools like Tableau to deliver actionable recommendations.
Key skills: Data analysis, business consulting, data storytelling, Tableau, machine learning basics, stakeholder communication.
Frequently Asked Questions
1. Do you provide certification for the Data Science Course with Tableau?
Yes. Upon course completion, we provide a certificate that you can share on LinkedIn or in your resume.
2. Is Tableau included as part of the Data Science Course?
Yes, Tableau is integrated in the curriculum with hands-on dashboards and visualizations.
3. Can I get a job after completing this Data Science Course?
We offer placement assistance and share your profile with hiring partners globally.
4. What if I miss a class?
You will have access to the class recording and can also attend another live session in a different batch.
5. Do I need coding experience for this course?
No prior coding experience is mandatory. We cover Python fundamentals during the course.
6. How long will I have access to the course materials?
You will have lifetime access to the LMS and all resources.
7. What projects will I work on during the course?
You will work on projects such as predictive analytics and customer segmentation with Tableau dashboards.
8. Can I pay the fee in installments?
Yes. We offer flexible payment options including installment plans.
9. Do you provide a free demo for this course?
Yes. You can attend a free demo session before enrolling.
10. Is this course suitable for fresh graduates?
Yes. The course starts from fundamentals, making it ideal for freshers aiming to build a career in data science.
Conclusion
Enrolling in the Data Science Course with Tableau Online at Online IT Guru is a strategic step toward becoming a data-driven professional. With a strong focus on hands-on learning, practical case studies, and job-ready skills, this course helps you master both core data science techniques and advanced data visualization using Tableau.