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Curriculum Breakdown
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Data Science Learning Pathway

Mastering Data Science: Comprehensive Course
Your Gateway to Multiple Roles
Skill and Tools You will Learn

Data visualization
Create clear and impactful charts, graphs, and dashboards to interpret and present complex data patterns and insights visually.

Python
Use the versatile Python programming language for coding, automation, statistical analysis, and integrating all major data science tools.

SQL
Query, filter, and manage structured data in relational databases, extracting relevant data sets for analysis and modeling.

Pandas
Manipulate, clean, and analyze data efficiently with the primary Python library for handling tabular data and DataFrames.

NumPy
Perform fast numerical computations, manage multidimensional arrays, and support scientific and statistical operations.

Scikit-learn
Apply essential machine learning algorithms for classification, regression, clustering, and model evaluation using this popular ML toolkit.

Deep Learning
Build and train advanced neural network models for tasks like image recognition, natural language processing, and artificial intelligence.

Power Query & DAX
Perform advanced data extraction, transformation, and calculations in Microsoft Power BI and Excel, streamlining raw data processing.

Model Evaluation & Optimization
Assess predictive models using metrics and validation techniques, and fine-tune performance for accurate, reliable results

OpenCV
Work with computer vision tasks, processing images and video for analysis, detection, and automation.

FastAPI
Develop fast, scalable web APIs for serving machine learning models and integrating data science workflows into applications.

R Language
Utilize R, a statistical programming language, for advanced analytics, data visualization, and specialized statistical modeling.

Matplotlib & Seaborn
Visualize and explore data through flexible plotting libraries, creating everything from basic graphs to advanced statistical plots.

Power BI & Tableau
Create interactive dashboards and business intelligence reports, empowering data-driven decisions in modern enterprises.

Data Processing & Feature Engineering
Prepare raw data for analysis by cleaning, transforming, and developing features that improve model performance and insights.
PIIT’s Full Stack Python program follows a carefully designed curriculum that covers the latest and most in-demand technologies used by employers.
Learn from expert instructors who bring both industry experience and teaching excellence to guide you through every concept with real-world examples.
The program emphasizes practical learning through real-time projects that enable you to apply concepts into building complete web applications.
PIIT trains you in both frontend and backend development, including Python frameworks like Django and Flask, as well as front-end technologies such as HTML, CSS, and JavaScript.
With options for online and offline classes, as well as flexible batch timings, PIIT accommodates working professionals and students alike.
- Dedicated placement support helps you prepare for interviews, build a strong resume, and connects you to top recruiters in the tech industry.
Upon successful completion, you receive a recognized certification from PIIT that adds credibility and boosts your career prospects.
Personalized mentoring ensures doubts are cleared promptly and learning obstacles are overcome effectively.
You gain practical knowledge not just in coding but also in best practices like version control, debugging, deployment, and security.
Training covers advanced Python topics, RESTful APIs, database management, and cloud deployment for comprehensive skill development.
Limited batch sizes ensure personalized attention and a collaborative learning environment.
Work on projects that replicate actual business scenarios, enhancing your portfolio and job readiness.
PIIT’s growing community of successful Certified offers networking and learning opportunities beyond the classroom.
The program is competitively priced with EMI options to make it accessible without compromising on quality.
Students receive ongoing updates, resources, and support even after course completion to help with skill upgrades and career growth.
Batch Schedule
Date | Course | Training Type | Batch |
---|---|---|---|
Mon, 18th August 2025 | Data Science | Classroom | 6 PM-8 PM |
Mon 18th August 2025 | Data Science | Online- Live Training | 6 PM-8 PM |
Sat, 22nd August 2025 | Data Science | Online- Live Training | 11 AM to 3 PM |
Sat, 22nd August 2025 | Data Science | Online- Live Training | 11 AM to 3 PM |
Frequently Asked Question
The curriculum includes Python programming, machine learning, data analysis, data visualization, SQL, statistics, big data tools, Tableau, Power BI, and hands-on real-world projects.
No prior coding knowledge is required. Most programs start from fundamentals, making them suitable for beginners as well as working professionals.
Data Science courses usually last between 3 to 8 months, depending on the institute, learning mode, and whether you choose weekday or weekend batches.
Yes, a globally recognized or industry-accredited Data Science certificate is awarded after successful completion of the course and assessments.
Most top institutes offer 100% placement support, including interview preparation, resume building, and direct referrals to hiring companies.
The course is ideal for final-year students, graduates from any discipline, career switchers, and professionals in non-technical roles seeking high-growth IT careers.
Most institutes provide flexible learning modalities—online live classes, classroom sessions, or blended formats—to suit diverse student needs.
Students work on multiple capstone, live, or client-based projects to ensure practical, job-ready skills aligned with industry demands.
Graduates can pursue roles such as Data Analyst, Data Scientist, Machine Learning Engineer, Business Intelligence Analyst, and more with companies across IT, finance, healthcare, and startups.
Enrollment is straightforward—fill out an online form or contact the institute.