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Data Science Training Course Curriculum:
1: Introduction to Data Science
What is Data Science?
History and Evolution of Data Science
Role of Data Scientists
Data Science Process and Lifecycle
Data Science Tools and Technologies
2: Data Collection and Preprocessing
Data Sources and Types
Data Collection Methods
Data Cleaning and Data Quality
Handling Missing Data
Data Transformation and Normalization
3: Exploratory Data Analysis (EDA)
Data Visualization Techniques
Descriptive Statistics
Data Distribution and Outliers
Correlation and Covariance
EDA with Python/R Libraries
4: Data Wrangling and Feature Engineering
Feature Selection and Importance
Feature Scaling and Transformation
Handling Categorical Variables
Dimensionality Reduction Techniques
Data Wrangling with Pandas (Python) or dplyr (R)
5: Machine Learning Fundamentals
Introduction to Machine Learning
Supervised, Unsupervised, and Reinforcement Learning
Train-Test Split and Cross-Validation
Model Evaluation Metrics
Overfitting and Underfitting
6: Regression Analysis
Linear Regression
Polynomial Regression
Regularization (L1, L2)
Model Evaluation for Regression
7: Classification Algorithms
Logistic Regression
Decision Trees and Random Forests
k-Nearest Neighbors (KNN)
Support Vector Machines (SVM)
Evaluation Metrics for Classification
8: Clustering Techniques
K-Means Clustering
Hierarchical Clustering
Density-Based Clustering
Evaluation Metrics for Clustering
9: Natural Language Processing (NLP)
Text Preprocessing
Bag-of-Words Model
TF-IDF and Word Embeddings
Sentiment Analysis
NLP Libraries (NLTK, spaCy)
10: Time Series Analysis
Time Series Components
Time Series Visualization
ARIMA Model
Seasonal Decomposition
Forecasting Techniques
11: Deep Learning Fundamentals
Introduction to Neural Networks
Activation Functions
Loss Functions
Backpropagation and Gradient Descent
Building Neural Networks with TensorFlow/Keras or PyTorch
12: Model Deployment and Interpretability
Model Deployment Options (Flask, Docker, Cloud Platforms)
Model Interpretability Techniques (SHAP, LIME)
Ethics and Bias in AI
13: Big Data and Spark
Introduction to Big Data and Hadoop
Apache Spark and its Ecosystem
Spark DataFrames and RDDs
Spark MLlib for Machine Learning
14: Data Visualization and Storytelling
Data Visualization Principles
Interactive Data Visualization Tools (Matplotlib, Seaborn, Plotly)
Dashboard Creation (Tableau, Power BI)
Data Science Training Course Curriculum:
1: Introduction to Data Science What is Data Science? History and Evolution of Data Science Role of Data Scientists Data Science Process and Lifecycle Data Science Tools and Technologies 2: Data Collection and Preprocessing Data Sources and Types Data Collection Methods Data Cleaning and Data Quality Handling Missing Data Data Transformation and Normalization 3: Exploratory Data Analysis (EDA) Data Visualization Techniques Descriptive Statistics Data Distribution and Outliers Correlation and Covariance EDA with Python/R Libraries 4: Data Wrangling and Feature Engineering Feature Selection and Importance Feature Scaling and Transformation Handling Categorical Variables Dimensionality Reduction Techniques Data Wrangling with Pandas (Python) or dplyr (R) 5: Machine Learning Fundamentals Introduction to Machine Learning Supervised, Unsupervised, and Reinforcement Learning Train-Test Split and Cross-Validation Model Evaluation Metrics Overfitting and Underfitting 6: Regression Analysis Linear Regression Polynomial Regression Regularization (L1, L2) Model Evaluation for Regression 7: Classification Algorithms Logistic Regression Decision Trees and Random Forests k-Nearest Neighbors (KNN) Support Vector Machines (SVM) Evaluation Metrics for Classification 8: Clustering Techniques K-Means Clustering Hierarchical Clustering Density-Based Clustering Evaluation Metrics for Clustering 9: Natural Language Processing (NLP) Text Preprocessing Bag-of-Words Model TF-IDF and Word Embeddings Sentiment Analysis NLP Libraries (NLTK, spaCy) 10: Time Series Analysis Time Series Components Time Series Visualization ARIMA Model Seasonal Decomposition Forecasting Techniques 11: Deep Learning Fundamentals Introduction to Neural Networks Activation Functions Loss Functions Backpropagation and Gradient Descent Building Neural Networks with TensorFlow/Keras or PyTorch 12: Model Deployment and Interpretability Model Deployment Options (Flask, Docker, Cloud Platforms) Model Interpretability Techniques (SHAP, LIME) Ethics and Bias in AI 13: Big Data and Spark Introduction to Big Data and Hadoop Apache Spark and its Ecosystem Spark DataFrames and RDDs Spark MLlib for Machine Learning 14: Data Visualization and Storytelling Data Visualization Principles Interactive Data Visualization Tools (Matplotlib, Seaborn, Plotly) Dashboard Creation (Tableau, Power BI)Introduction
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