Data Science FULL Course for Beginners in 27 HOURS - 2025 Edition (WsCube Tech)

Summary

This comprehensive 27-hour course by WsCube Tech provides a thorough introduction to Data Science for beginners. It covers essential mathematical and statistical foundations, delves into the intricacies of Machine Learning (including Supervised, Unsupervised, and Ensemble Learning), and explores the exciting field of Deep Learning and AI. The course emphasizes practical implementation and aims to equip learners with globally competitive skills for the future.

Key Takeaways

* **Foundation:** Data Science requires a strong understanding of mathematics, particularly statistics, including measures of central tendency, variability, probability, covariance, and correlation.

* **Machine Learning:** The course covers the entire ML pipeline, from data cleaning and feature selection to various supervised learning algorithms (regression, classification) and unsupervised learning techniques (clustering, association).

* **Advanced Topics:** Deep Learning, Neural Networks, Perceptrons, and their training processes (forward/backpropagation) are explained.

* **Practical Focus:** The course emphasizes practical implementation and aims to make learners job-ready and globally competitive.

* **WsCube Tech Offering:** WsCube Tech provides online and classroom training with live classes and hands-on projects by industry experts.

Detailed Notes

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00:00:00 - Data Science Full Course Intro

* Introduction to the comprehensive Data Science course.

* Emphasis on building globally competitive skills.

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00:01:31 - Data Science Maths in-depth

#### 1. Introduction to Statistics

* **Population & Sample:** Understanding the difference between a complete group and a subset.

* **Descriptive vs. Inferential Statistics:**

* Descriptive: Summarizing and describing data characteristics.

* Inferential: Making predictions or inferences about a population based on a sample.

* **Basic Statistical Measures:** Introduction to fundamental statistical concepts.

#### 2. Measure of Central Tendency

* **Median:** The middle value in a sorted dataset.

* **Mean:** The average of all values.

* **Mode:** The most frequently occurring value.

* **Measures of Variability:** Quantifying the spread or dispersion of data (e.g., range, variance, standard deviation).

* **Percentage, Percentiles, and Quartiles:** Understanding data distribution and position.

#### 3. Probability

* **Probability Distribution:** Describing the likelihood of different outcomes.

* **Normal Distribution (Gaussian Distribution):** A common bell-shaped probability distribution.

* **Advanced Statistical Concepts:** Deeper dives into statistical theories.

#### 4. Covariance and Correlation

* **Covariance:** Measuring the directional relationship between two variables.

* **Correlation:** Measuring the strength and direction of a linear relationship (often standardized).

* **Central Limit Theorem:** The distribution of sample means approximates a normal distribution as sample size increases.

* **Hypothesis Testing:** A statistical method to test assumptions about a population parameter.

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05:52:03 - Machine Learning Complete

#### 1. Introduction to Machine Learning (ML)

* What is Machine Learning and its importance.

#### 2. Roadmap to Learning Machine Learning

* Guidance on the learning path for ML.

#### 3. Types of Data and Variables in ML

* Understanding different data types (numerical, categorical) and variables.

#### 4. Data Cleaning

* **Identifying and Handling Missing Values:** Strategies for dealing with incomplete data.

* **Encoding Categorical Variables:**

* **One Hot Encoding & Dummy Variables:** Creating binary columns for categorical features.

* **Label Encoding:** Assigning numerical labels to categories.

* **Ordinal Encoding:** Encoding categories based on their inherent order.

* **Outlier Detection and Removal:** Identifying and handling extreme values that can skew results.

* **Feature Scaling:**

* **Standardization:** Scaling data to have zero mean and unit variance.

* **Normalization:** Scaling data to a fixed range (e.g., 0 to 1).

* **Handling Duplicate Data:** Identifying and removing redundant entries.

* **Data Type Transformation:** Converting data between different types.

#### 5. Feature Selection Techniques

* **Backward Elimination:** Iteratively removing features that have least significance.

* **Forward Elimination:** Iteratively adding features based on their significance.

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09:33:17 - Supervised Learning in ML

#### 1. Train Test Split in Dataset

* Dividing data into training and testing sets for model evaluation.

#### 2. Regression Analysis

* **Linear Regression Algorithm (Simple Linear):** Modeling the relationship between one independent and one dependent variable.

* **Multiple Linear Regression:** Modeling the relationship with multiple independent variables.

* **Polynomial Regression:** Modeling non-linear relationships using polynomial terms.

#### 3. Cost Function in Regression

* Measuring the error between predicted and actual values.

#### 4. R Squared Score & Adjusted R Squared in Regression Analysis

* Metrics for evaluating the goodness of fit of a regression model.

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12:15:30 - Classification in ML

#### 1. Classification

* Predicting a categorical outcome.

#### 2. Logistic Regression

* **Binary Classification (Practical):** Predicting one of two outcomes.

* **Binary Classification with Multiple Inputs (Practical):** Using multiple features for binary prediction.

* **Binary Classification with Polynomial Inputs (Practical):** Using polynomial features for binary prediction.

* **Multiclass Classification (Practical):** Predicting one of several categories.

#### 3. Confusion Matrix

* A table summarizing classification performance.

#### 4. Imbalanced Dataset Handling

* Techniques to address datasets where one class is significantly more represented than others.

#### 5. Naive Bayes Algorithm

* A probabilistic classifier based on Bayes' theorem.

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15:18:11 - Non-Linear Supervised Algorithm in ML

#### 1. Non-Linear Supervised Algorithms

* **Decision Tree (Classification):** Tree-like structure for making classification decisions.

* **Decision Tree (Regression):** Tree-like structure for predicting continuous values.

* **K-Nearest Neighbors (Classification - KNN):** Classifying data points based on their nearest neighbors.

#### 2. Hyperparameter Tuning

* Optimizing model parameters that are not learned from data.

#### 3. Cross-Validation

* A technique to evaluate model performance by training and testing on different subsets of the data.

#### 4. Unsupervised Learning (Brief Mention)

* Introduction to learning from unlabeled data.

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19:01:23 - Clustering in ML

#### 1. Clustering

* Grouping similar data points together without prior labels.

#### 2. K-means Clustering

* An iterative algorithm to partition data into 'k' clusters.

#### 3. Hierarchical Clustering

* Building a hierarchy of clusters.

#### 4. DBSCAN Clustering Algorithm

* Density-based spatial clustering of applications with noise.

#### 5. Silhouette Score

* A metric to evaluate the quality of clusters.

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20:52:57 - Association in ML

#### 1. Association

* Discovering relationships or associations between items in a dataset.

#### 2. Association Rule Learning

* Finding interesting relationships (rules) between variables.

#### 3. Apriori Algorithm

* A classic algorithm for mining frequent itemsets and association rules.

#### 4. Frequent Pattern Growth Algorithm (FP-Growth)

* An alternative and often more efficient algorithm for frequent pattern mining.

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22:15:34 - Ensemble Learning in ML

#### 1. Ensemble Learning

* Combining multiple machine learning models to improve predictive performance.

#### 2. Voting Classifiers/Regressors

* **Max Voting:** Each model gets one vote, and the majority wins (classification).

* **Averaging:** Averaging predictions from multiple models (regression).

* **Weighted Average Voting:** Assigning weights to different models before averaging.

* **Practical Implementation for Regression & Classification.**

#### 3. Bagging

* **Bootstrap Aggregating:** Training multiple models on different bootstrap samples of the data.

* **Random Forest:** An ensemble of decision trees built using bagging and random feature selection.

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23:25:50 - Deep Learning & AI Complete

#### 1. Deep Learning Overview

* **Introduction to Deep Learning:** What it is and its relationship with ML and AI.

* **Neurons, Neural Networks, and Types of Deep Learning Networks:** Basic building blocks and architectures.

#### 3. Perceptrons

* **Single Layer Perceptron:** The simplest form of a neural network.

* **Multilayer Perceptron (Artificial Neural Networks):** Networks with multiple hidden layers.

#### 4. Training Process

* **Forward Propagation:** The process of passing input through the network to get an output.

* **Backpropagation:** The algorithm used to update network weights based on the error.

* **Activation Functions:** Non-linear functions applied to neuron outputs (e.g., ReLU, Sigmoid).

* **Loss Functions:** Quantifying the error of the model's predictions.

* **Optimizers:** Algorithms that adjust network weights to minimize the loss function (e.g., Adam, SGD).

#### 5. Practical Applications

* Examples of real-world Deep Learning applications.

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**About WsCube Tech:**

* Vernacular Upskilling platform.

* Offers online and classroom training in Python, Machine Learning, and Data Science.

* Focuses on hands-on projects by industry experts.

* Aims to make learners globally competitive.

* Contact: +91-7878985501

* Website: http://wscubetech.com

* Social Media: Links provided for Facebook, Twitter, Instagram, LinkedIn, YouTube.