Data Science Essentials: From Statistical Logic to Predictive Modeling
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Master the Art of Data-Driven Decision Making
In today’s digital economy, data is no longer just for “techies”—it is the language of leadership. Whether you are a student preparing for the global job market or a professional looking to upgrade your skills, the ability to turn raw data into actionable insights is your greatest competitive advantage.
This program is designed to bridge the gap between complex mathematics and real-world application. We focus on Statistical Intuition and Machine Learning Logic, ensuring you can solve problems and drive results without getting lost in overwhelming code.
Why This Program?
| Course Feature | Professional & Academic Benefit |
|---|---|
| Industry-Centric Case Studies | Solve cross-industry problems in Marketing, Finance, and HR. |
| Interactive Visual Sandboxes | Visualize complex math in real-time with intuitive sliders and tools. |
| Strategic Storytelling | Learn to present data-driven insights with clarity to any audience. |
The 5-Module Data Science Foundation
Module 1: Statistical Intuition
Master probability and distribution to validate business hypotheses and spot patterns.
Master probability and distribution to validate business hypotheses and spot patterns.
Module 2: Predictive Relationships
Build regression models to forecast sales, pricing trends, and business growth.
Build regression models to forecast sales, pricing trends, and business growth.
Module 3: Classification Logic
The science of “Yes/No” outcomes. Master risk assessment and customer behavior prediction.
The science of “Yes/No” outcomes. Master risk assessment and customer behavior prediction.
Module 4: Tree-Based Decisions
Map complex processes through “Split Points” to optimize workflows and strategic planning.
Map complex processes through “Split Points” to optimize workflows and strategic planning.
Module 5: Pattern Discovery
Learn Clustering to group data by similarities—perfect for market segmentation.
Learn Clustering to group data by similarities—perfect for market segmentation.
Unlock Your Potential
Get direct access to Skill Science Academy’s interactive tools and industry mentorship. Stand out in your university and career with data-backed confidence.
Empowering Future Leaders
Module 1 : Statistical Intuition & Decision Logic
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1
Basic Statistical Concepts : Histograms - Preview Lesson -
2
Basic Statistical Concepts : Bar Charts vs. Pie Charts - Preview Lesson
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3
Basic Statistical Concepts : Boxplot - Preview Lesson
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4
Probability & Distributions : The Normal Distribution
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5
Probability & Distributions : Mean, Median, Mode for Normal Distribution
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6
Probability & Distributions : Standard Deviation vs Standard Error
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7
Probability & Distributions : Estimating Population Parameter
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8
Probability & Distributions : p-values: Interpretation
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9
Probability & Distributions : p-values Calculation: Part 1 (Discrete Variables)
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10
Probability & Distributions : p-values Calculation: Part 2 (Continuous Variables)
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11
Statistical Testing : Hypothesis Testing - Null Hypothesis
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12
Statistical Testing : Alternative Hypothesis
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13
Statistical Testing : ANOVA
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14
Statistics Quiz Time! – Understanding Statistics Concepts -
15
🕵️♂️ The Data Detective’s Guide: Stats Made Simple -
16
Advance Stat Concepts : Central Limit Theorem
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17
Advance Stat Concepts : Bootstrapping
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18
Advance Stat Concepts : Confidence Intervals
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19
Advance Stat Concepts : QQ Plots & Quantile Normalization
Module 2 : Supervised Learning: Linear Regression
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20
Relationship in Data : Covariance - Part 1
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21
Relationship in Data : Covariance - Part 2
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22
Relationship in Data : Pearson’s Correlation - Part 1
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23
Relationship in Data : Pearson’s Correlation - Part 2
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24
Relationship in Data : R-squared
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25
Linear Regression : Least Square Method
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26
Linear Regression : Simple Linear Regression
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27
Linear Regression : Multiple Regression
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28
Linear Regression : General Linear Models (GLM) Unlocked (t-test and ANOVA)
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29
Linear Regression Case Study : Simple Linear Regression
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30
Linear Regression Case Study : t tests
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31
Linear Regression Model : Assumptions
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32
Linear Regression Model : Model Selection using Mallow’ Cp
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33
Linear Regression Model : Backward and Forward Selection
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34
Linear Regression Model : Residual Analysis
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35
Linear Regression Model : Influential Observations Analysis
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36
Linear Regression Model : Collinearity Diagnostics
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37
Linear Regression Case Study 2 : Multiple_Linear_Regression.ipynb
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38
Interview & CV - Guidance
Module 3 : Supervised Learning: Logistic Regression
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39
Logistics Regression : Expected Values - Discrete
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40
Logistics Regression : Expected Values - Continuous
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41
Logistics Regression : Test of Association
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42
Logistics Regression : Fisher's Exact Test
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43
Logistics Regression : The Mantel-Haenszel Test (Controlling for Confounders)
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44
Logistics Regression : Logs
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45
Logistics Regression : Odds and Log(Odds)
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46
Logistics Regression : Odds Ratios and Log(Odds Ratios)
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47
Logistics Regression : Probability vs Likelihood
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48
Logistics Regression : Maximum Likelihood
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49
Logistics Regression : Maximum Likelihood - Binomial Distribution
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50
Logistics Regression : Concept
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51
Logistics Regression : Coefficients - Continuous
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52
Logistics Regression : Coefficients - Discrete
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53
Logistics Regression : Reference Cell and Effect Cell Coding
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54
Logistics Regression : R Squared and p-value
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55
Logistics Regression : Concordant and Discordant Pairs
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56
Logistics Regression : Null Deviance and Residual deviance
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57
Logistics Regression : FAIQs
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58
Case Study 3 - Logistics Regression : Real World Case Study
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59
Model Validation : Cross Validation
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60
Model Performance : Confusion Matrix
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61
Model Metrics : Sensitivity, Specificity, Precision and Accuracy
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62
Model Diagnostics : Bias and Variance
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63
Model Evaluation : ROC and AUC
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64
Information Logic : Entropy
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65
Information Logic : Mutual Information
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66
Regularization : Ridge Regression
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67
Regularization : Lasso Regression
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68
Regularization : Elastic-Net Regression
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69
Regularization : Case Study 4
Module 4 : Unsupervised Machine Learning (Clustering & Dimension Reduction)
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70
Dimension Reduction : PCA (Principal Component Analysis)
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71
Dimension Reduction : MDS (Multidimensional Scaling) and PCoA (Principal Coordinate Analysis)
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72
Dimension Reduction : t-SNE
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73
Dimension Reduction : UMAP Dimension Reduction
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74
Dimension Reduction : Heatmap
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75
Case Study 5
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76
Clustering : K-Means
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77
Clustering : DBSCAN
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78
Clustering : Hierarchical
Module 5 : Supervised Machine Learning Algorithms
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79
Gradient Descent
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80
Stochastic Gradient Descent
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81
K - Nearest Neighbors
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82
Naive Bayes
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83
Gaussian Naive Bayes
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84
Decision Tree : One-Hot, Label, Target and K-Fold Target Encoding
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85
Decision Tree : Classification Tree
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86
Decision Tree : Regression Trees
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87
Decision Tree : Pruning
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88
Case Study 6
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89
Random Forest : Build, use and validate
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90
Random Forest : Missing Data and Clustering
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91
Case Study 7
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92
Boosting : AdaBoost
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93
Boosting : Gradient Boost
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94
Boosting : XGBoost
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95
Boosting : Cosine Similarity
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96
Boosting : CatBoost
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97
Case Study 8
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98
Support Vector Machine : Understanding
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99
Support Vector Machine : The Polynomial Kernel
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100
Support Vector Machine : The Radial Kernel (RBF)
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 get lifetime access to our premium digital learning vault, which includes:
🎬 AI-Powered Video Library: High-definition video lessons with crystal-clear AI narration for easy, anytime learning.
📊 Interactive Visual Sandboxes: Exclusive access to our proprietary "MasterPlaybook" tools to visualize complex math and data logic in real-time.
🐙 GitHub Repository Access: Lifetime access to our private GitHub repo containing all Python/R code templates and curated industry-grade datasets.
🚀 Project Documentation: Step-by-step guides and "Solution Notebooks" for your 10+ portfolio-ready projects.
📝 Expert Cheat Sheets: Rapid-reference digital guides for statistical formulas and core Machine Learning concepts.
🎬 AI-Powered Video Library: High-definition video lessons with crystal-clear AI narration for easy, anytime learning.
📊 Interactive Visual Sandboxes: Exclusive access to our proprietary "MasterPlaybook" tools to visualize complex math and data logic in real-time.
🐙 GitHub Repository Access: Lifetime access to our private GitHub repo containing all Python/R code templates and curated industry-grade datasets.
🚀 Project Documentation: Step-by-step guides and "Solution Notebooks" for your 10+ portfolio-ready projects.
📝 Expert Cheat Sheets: Rapid-reference digital guides for statistical formulas and core Machine Learning 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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