The Complete Data Science Journey: Analyze, Model, Predict — and Deploy AI
- Description
- Curriculum
- FAQ
- Reviews
- What You’ll Learn
- End-to-End Data Science Pipeline: Master data cleaning (Pandas), visualization (Matplotlib/Seaborn), statistical modeling, and machine learning (scikit-learn).
- Real-World Projects: Build predictive models (e.g., sales forecasting, customer churn analysis) and deploy solutions.
- Advanced Topics: Neural networks (CNNs), reinforcement learning, and optimization techniques for complex datasets.
- Industry Applications: Case studies in finance, healthcare, and e-commerce to contextualize techniques.
- Data Wrangling: Cleaning, transforming, and visualizing data using Python with Pandas and Matplotlib
- Who This Course Is For
✔ Absolute Beginners: No prior math/coding experience required (starts from scratch). ✔ Career Changers: Transitioning to data roles (e.g., analysts → data scientists). ✔ Professionals: Seeking live mentorship over self-paced courses (e.g., vs. Coursera/IBM). ✔ Students: Building portfolios for internships/jobs with hands-on projects.
- Course Format & Schedule
- Teaching Method: Live Zoom classes (interactive) with PowerPoint slides and shared Jupyter notebooks.
- Materials: Lifetime access to PPTs (PDF), datasets, and cheat sheets (e.g., Pandas functions, ML algorithms).
- Projects:
- Beginner: Exploratory data analysis (EDA) on retail datasets.
- Intermediate: Predictive model for student grades (like Walkthrough).
- Advanced: CNN-based image classifier or NLP chatbot.
- Duration: 9 months (weekends: Sat-Sun, 2-hour sessions).
- Assessment: Weekly quizzes + capstone project (GitHub submission).
- Outcome: Certificate + portfolio (e.g., 5+ projects).
Data Analysis Using Python
Module 1: Introduction to Data Analysis and Python
Module 2: Python Essentials for Data Analysis
Module 3: Working with Data Structures
Module 4: Introduction to NumPy
Module 5: Data Analysis with pandas
Module 6: Importing and Exporting Data
Module 7: Data Cleaning and Preparation
Module 8: Data Wrangling and Merging
Module 9: Data Visualization
Module 10: Group Operations and Aggregations
Mastering Data Science: From Statistics to ML Algorithms
Introduction to Statistical Concepts
Probability & Distributions
Statistical Inference & Testing
Advanced Concepts - Statistics
Relationship in Data
Linear Regression
Case Study1 - Simple Linear Regression & t-test
Linear Regression Model Process
Case Study 2 - Linear Regression
Logistics Regression
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77
Expected Values - Discrete -
78
Expected Values - Continuous
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79
Test of Association
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80
Fisher's Exact Test
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81
The Mantel-Haenszel Test (Controlling for Confounders)
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82
Logs
-
83
Odds and Log(Odds)
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84
Odds Ratios and Log(Odds Ratios)
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85
Probability vs Likelihood
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86
Maximum Likelihood
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87
Maximum Likelihood of Binomial Distribution
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88
Logistics Regression
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89
Coefficients -
90
Effect Coding and Reference Cell Coding
-
91
R-squared and p-value of logistic regression
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92
Concordant and Discordant Pairs
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93
Saturated models and deviance statistics
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94
Deviance Residuals
Case Study 3
Machine Learning Concepts
Regularization
Case Study 4
Unsupervised Learning - Dimension Reduction
Case Study 5
Unsupervised Learning - Clustering
General Optimization Algorithm Concepts
Supervised Learning Concepts
Decision Tree Concepts
Case Study 6
Random Forest Model
Case Study 7
Boosting Concepts
Case Study 8
Support Vector Machine Concepts
Neural Networks Explained: From Zero to Hero
Introduction to Neural Networks
Backpropagation: Theory and Implementation
Activation Functions in Practice
Feedforward Networks
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Advanced Architectures
Transformer Networks
Linear Algebra Essentials
Reinforcement Learning Integration
PyTorch Deep Learning Projects – From Fundamentals to Transformers
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153
Start with PyTorch – Learn the basics with hands-on, beginner-friendly notebooks.
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154
Build Your First Neural Net – Code real models using PyTorch + Lightning.
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155
Handle Complex Data – Work with networks that take multiple inputs and outputs.
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156
Master CNNs – Create image-based models from scratch.
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157
Implement LSTMs – Tackle sequential data like time series and text.
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158
Create Word Embeddings – Learn how machines understand language.
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159
Explore Autoencoders – Compress and reconstruct data with unsupervised learning.
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160
Code a Transformer – Build a simplified ChatGPT-style model step by step.
Is this course suitable for complete beginners?
Absolutely! We start from scratch – no math or coding experience needed. Just bring curiosity
Can I pay in installments?
A: Yes! Choose:
• Full payment (5% discount)
• 2-month interest-free installments
• Full payment (5% discount)
• 2-month interest-free installments
How do I get my certificate?
Automatically downloadable from your student dashboard after completing all projects or will be sent to your email ID.
How flexible is the schedule?
Weekend Batch: Sat - Sun (1.5 hr/day as per your convenience)
Is this course online or offline?
This is primarily an online learning program – no need to visit any physical center! Engage, Ask, Learn - Live! However, if you're based in Vadodara and prefer in-person training, we’re happy to arrange offline batches. Just WhatsApp us at +91 92743 73121 to discuss options.
Will I get access to course material after finishing the course?
Yes! You’ll get lifetime access to:
• All PPT slides (PDF format)
• Python/R code templates
• Project guides & datasets
• Cheat sheets (stats formulas, key concepts)
• All PPT slides (PDF format)
• Python/R code templates
• Project guides & datasets
• Cheat sheets (stats formulas, key concepts)
How do I enroll and get access to the course?
Purchase: Create an account (or log in), complete the payment, and gain access to the course materials.
Live Sessions: After payment, our team will contact you within 24–48 hours to schedule your live training batches.
Live Sessions: After payment, our team will contact you within 24–48 hours to schedule your live training batches.
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