Data Science

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About Course

A Data Science course is designed to introduce students to the techniques and tools used to analyze and extract insights from data. It integrates various fields such as statistics, machine learning, data analysis, and programming. Below is a typical overview of what a Data Science course might cover:

Overview: Data Science Training equips participants with the essential knowledge and skills needed to analyze, interpret, and model complex data. This comprehensive program covers core concepts of data analysis, machine learning, statistical methods, and data visualization, enabling learners to transform raw data into actionable insights. Whether you’re a beginner aiming to understand the fundamentals or an experienced professional looking to expand your skill set, this training provides the tools needed to succeed in the rapidly growing field of data science.

Skills Gained from the Course:

  • Technical Skills: Programming (Python), Data manipulation (pandas, NumPy), Machine learning, Data visualization 
  • Analytical Skills: Data analysis, statistical testing, hypothesis testing
  • Problem-Solving: Using data-driven approaches to solve complex problems
  • Communication Skills: Presenting data-driven insights to non-technical stakeholders

What Will You Learn?

  • Student will learn the basics of Python, Machine Learning, Statistics for data science,Natural language Processing and deep learning

Course Content

Module 1: Python Basics
1. Introduction to Python - Fundamentals (Variables, Keywords, Type Casting) - Data Types and Operators 2. Program Control Flow - Conditional Statements (if, elif, else) - Iterative Statements (for, while) - Transfer Statements (break, pass, continue) 3. Functions - Built-in Functions, User-defined Functions, Lambda Functions,Decorator and Generator 4. Modules and Packages - Importing and Using Modules (os, sys, math, regex,calender etc.) 5. File Handling and Exception Handling 6. Object-Oriented Programming (OOP) - Classes and Objects - Inheritance, Polymorphism, Encapsulation - Keywords: `this` vs. `super`, Public, Protected, Private

Module 2: Python for Data Science
1. Numerical Computation - Numpy 2. Data Manipulation - Pandas 3. Data Visualization - Matplotlib & Seaborn

Module 3: Statistics for Data Science
Descriptive Statistics 1. Central Tendency: Mean, Median, Mode 2. Dispersion: Range, Variance, Standard Deviation, IQR 3. Distribution Shape: Skewness, Kurtosis 4. Visualization Techniques: Histograms, Box Plots, Scatter Plots Inferential Statistics 1. Probability Distributions: Normal, Binomial, Poisson 2. Hypothesis Testing - t-test, Chi-Square Test, ANOVA 3. Correlation and Causation - Pearson and Spearman Correlation Coefficients 4. Confidence Intervals and p-Values

Module 4: Machine Learning Basics
1. Introduction to ML, AI, DL, and Data Science 2. Types of Machine Learning - Supervised, Unsupervised, Reinforcement Learning 3. ML Project Life Cycle - Problem Definition, Data Collection, Data Preparation, Model Training, Evaluation, Deployment Exploratory Data Analysis (EDA) 1. Univariate Analysis: Histograms, Probability Density Functions (PDF), Cumulative Distribution Functions (CDF) 2. Bivariate Analysis: Scatter Plots, Correlation Matrix 3. Multivariate Analysis: Pair Plots, 3D Scatter Plots Feature Engineering 1. Handling Missing Values and Outliers 2. Categorical Encoding - One-Hot Encoding, Label Encoding 3. Normalization and Standardization Feature Selection 1. Correlation Analysis 2. Heat Maps

Module 5: Supervised Machine Learning Techniques
1. Regression - Simple and Multiple Linear Regression - Polynomial Regression - Lasso and Ridge Regression - Performance Metrics: R2 Score, Adjusted R2 Score 2. Classification - Logistic Regression - Naive Bayes - Decision Trees (ID3, CART Algorithms) - k-Nearest Neighbors (k-NN) - Support Vector Machines (SVM) - Performance Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC 3. Ensemble Techniques - Bagging: Random Forest - Boosting: AdaBoost, Gradient Boosting, XGBoost, CatBoost - Stacking: Combining multiple models (base learners) using another model (meta-learner) to improve prediction accuracy.

Module 6: Unsupervised Machine Learning Techniques
1. Clustering - k-Means Clustering - Hierarchical Clustering - DBSCAN 2. Dimensionality Reduction - Principal Component Analysis (PCA)

Module 7: Natural Language Processing (NLP)
1. Text Preprocessing - Tokenization, Stop Words Removal, Stemming, Lemmatization 2. Feature Extraction - Bag of Words, n-grams, TF-IDF 3. Word Embeddings - Word2Vec, Avg word2vec 4. Advanced NLP Models - Recurrent Neural Networks (RNN) - LSTM and GRU - Attention Mechanisms and Transformers

Module 8: Deep Learning
Artificial Neural Networks (ANN) 1. Building Blocks of Neural Networks - Neurons, Activation Functions, Weight Initialization 2. Training Neural Networks - Forward Propagation, Backpropagation, Gradient Descent 3. Regularization Techniques - Dropout, Batch Normalization Convolutional Neural Networks (CNN) 1. Convolution and Pooling Layers 2. Popular Architectures - AlexNet, VGGNet, ResNet, DenseNet 3. Applications - Image Classification, Object Detection,Object Segmentation,OCR Recurrent Neural Networks (RNN) 1. RNN Architecture and Applications 2. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) 3. Sequence Modeling Applications - Time Series Forecasting, Language Modeling

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