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Beginner
Data Science & Analytics with Python
Master data science with Python. Learn data cleaning, visualization, statistics, exploratory data analysis (EDA), and predictive modeling with real projects.
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Course Description
Data Science is one of the most in-demand skills across industries like finance, healthcare, e-commerce, and technology. This course takes learners from data fundamentals to advanced analytics using Python.
You’ll learn NumPy, Pandas, Matplotlib, and Seaborn for data manipulation and visualization. Then, you’ll dive into statistics, hypothesis testing, machine learning basics, and predictive analytics.
With live mentor-led coding workshops for project guidance and pre-recorded tutorials for technical deep dives, this hybrid course ensures learners not only understand theory but also apply skills to real-world projects.
By the end, you’ll have built multiple projects, including a capstone predictive analytics project.
Target Audience
- Beginners wanting to start a career in data science
- Python learners moving into analytics
- Students preparing for internships/jobs in AI/ML
- Professionals upskilling for data-driven roles
Prerequisites
- Python programming basics (variables, loops, functions, OOP)
- Basic math & statistics recommended
Learning Outcomes
By completing this course, learners will:
- Use NumPy & Pandas for data cleaning and manipulation
- Visualize data with Matplotlib & Seaborn
- Perform statistical analysis and hypothesis testing
- Conduct exploratory data analysis (EDA)
- Build predictive models using Scikit-Learn
- Apply real-world analytics in domains like business, finance, and marketing
- Present insights with data storytelling techniques
Live Components
- Weekly mentor-led EDA & ML project reviews
- Group data analytics projects
- Capstone project presentation with feedback
Pre-recorded Access
- Tutorials for NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn
- Ready-to-use datasets & Jupyter notebooks
- Revision videos for each module
Learning Path
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Instructor
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Amar Deep Rao
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Satendra Patel | Robotics Instructor
Hey there! Learn with me in a gamified way.
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Biography
“
I am a Robotics Teacher with experience in teaching STEM concepts through hands-on, engaging lessons designed for young learners. I specialize in simplifying complex topics into practical, easy-to-understand activities that emphasize creativity, critical thinking, and real-world problem solving. I have successfully taught 200+ students aged 10–13 across India, the US, UK, and Canada using platforms such as Arduino, Raspberry Pi Pico, and Micro:bit. Along with teaching, I also contribute to curriculum design to make learning accessible and impactful.
Passionate about making technology learning exciting and future-ready, I am committed to preparing students through interactive methods and practical projects. My student-centric and detail-oriented teaching approach ensures that every learner feels supported and inspired.
Educational Qualification – Pursuing B.Tech in Computer Science and Engineering from Lovely Professional University with a CGPA of 8.16
Experience – 1+ year in Moonpreneur, delivered live online robotics sessions
Instructor
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Vaseel Ahmad
Robotics Instructor
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Biography
“
I am an educator at viLab with experience in teaching Robotics, AI and Coding. I specialize in simplifying complex technical concepts into engaging, hands-on lessons that make learning fun and practical for young learners. My classes emphasize creativity, critical thinking, and real-world problem solving, and I have successfully taught students across Lucknow using Arduino UNO and Pictograph.
Passionate about making technology education exciting and inclusive, I am committed to preparing students to become future-ready through interactive teaching methods and practical projects.
Educational Qualification – Pursuing B.Tech from Bansal Institute of Engineering and Technology, with a focus on coding skills.
Experience – Teaching experience in the field of Robotics and Coding, guiding students in building projects and developing problem-solving abilities.
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What’s Included
Everything you need to know about this course
Lectures
40 ClassesDuration
15 weeksLevel
BeginnerLanguage
English / HindiWhat you need / Requirement
Audio/Video Essentials
Headphone or Speakers for clear audio, Webcam & Microphone.
Software/Tools
PDF Reader (for notes and study materials).
Internet Connection
Stable Internet with at least 2 Mbps speed for smooth video streaming and interactive content.
Device
Smartphone, Tablet, Laptop or Desktop Computer.
Learning Path
Your Journey. Your Growth.
A structured path designed to take you from basics to mastery with clarity, confidence, and real-world impact.
01
Beginner
Start with fundamental concepts and build a strong foundation.
02
Intermediate
Expand your knowledge and start building practical projects.
03
Advanced
Dive deeper into specialized areas and master complex techniques.
04
Master
Achieve expert-level proficiency and innovate with your skills.
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YOUR ROADMAP
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A clear, step-by-step milestone path to take you from the basics to building real-world AI solutions.
01
Introduction to Data Science & Python
Your journey begins with learning what Data Science is and how Python is used to analyze and understand data. You’ll get familiar with essential libraries like NumPy, pandas, and matplotlib.
01
02
02
Collecting, Cleaning & Organizing Data
You’ll learn how to import datasets, fix missing or incorrect values, format data properly, and prepare it for analysis — the most important step in real-world data work.
03
Data Visualization & Interpretation
Time to make the data speak! You’ll create charts, graphs, and dashboards to identify patterns and trends using matplotlib and seaborn — turning numbers into clear insights.
03
04
04
Exploring Data Patterns & Insights
You’ll learn how to find relationships in data using statistics, correlation, grouping, and summaries. This helps you understand what the data is really telling you.
05
Build Predictive Models with Scikit-Learn
You’ll build your first machine learning models to make predictions — like forecasting sales or classifying categories — and evaluate their accuracy to ensure reliability.
05
06
06
Capstone Project & Portfolio Presentation
Finally, you’ll complete a real Data Science project — analyzing a dataset and presenting your findings. You’ll save your code and results in a portfolio that can be shown in interviews or presentations.
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Got Questions?
Frequently Asked Questions
Everything you need to know about Data Science & Analytics with Python
A hands‑on path from data fundamentals to advanced analytics using Python, covering NumPy, Pandas, Matplotlib, Seaborn, statistics, hypothesis testing, ML basics, and predictive analytics.
Python basics (variables, loops, functions, OOP) and comfort with basic math/statistics; Jupyter/Colab familiarity helps but isn’t mandatory.
Modular lessons that move from data manipulation and visualization to EDA, statistics, and ML basics, with projects in each module and a capstone at the end.
Yes. Post doubts in the Doubts section or during live classes; mentors reply asynchronously or schedule a live 1:1/Group session if a detailed discussion is required, and doubts are typically resolved within 24–48 hours.
Certificate programs generally issue a completion certificate upon successfully finishing all required modules and assessments; external “certifications” (industry exams) are distinct and may require separate testing with a third party body.
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