What is Data Science? [Video]

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Data Science Series Topics:
1. Introduction to Data Science

What is Data Science?
The role of a Data Scientist
The Data Science lifecycle
Applications of Data Science in various industries
2. Data Science Tools & Environment

Overview of popular tools: Python, R, Jupyter Notebooks
Setting up your environment for data science projects
Introduction to libraries: NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow
3. Data Wrangling and Preprocessing

Collecting and importing data
Data cleaning: handling missing values, outliers, and duplicates
Data transformation and normalization
Feature scaling and encoding categorical variables
4. Exploratory Data Analysis (EDA)

Descriptive statistics (mean, median, standard deviation)
Data visualization: histograms, box plots, scatter plots, and heatmaps
Identifying trends and patterns in data
Correlation analysis
5. Probability & Statistics for Data Science

Basics of probability theory
Statistical distributions: normal, binomial, and Poisson
Hypothesis testing (t-tests, chi-squared tests)
P-values, confidence intervals, and statistical significance
6. Data Visualization

Introduction to data visualization techniques
Visualizing data with Matplotlib, Seaborn, and Plotly
Creating interactive visualizations
Best practices for creating effective data stories
7. Machine Learning Foundations

Supervised vs unsupervised learning
Key algorithms: Linear Regression, Decision Trees, K-Nearest Neighbors (KNN), K-means clustering
Model evaluation metrics: accuracy, precision, recall, F1-score
Cross-validation and hyperparameter tuning
8. Feature Engineering

What is feature engineering?
Creating new features from existing data
Feature selection techniques
Dimensionality reduction (PCA)
9. Supervised Learning Algorithms

Linear regression and logistic regression
Decision Trees and Random Forests
Support Vector Machines (SVM)
Naive Bayes classifier
10. Unsupervised Learning Algorithms

K-Means Clustering
Hierarchical clustering
DBSCAN
Principal Component Analysis (PCA)
11. Natural Language Processing (NLP)

Introduction to NLP and text data
Tokenization, stemming, and lemmatization
Sentiment analysis and text classification
Word embeddings (Word2Vec, GloVe)
12. Deep Learning Basics

Introduction to Neural Networks
Understanding deep learning and its applications
Training deep learning models with TensorFlow/Keras
Convolutional Neural Networks (CNNs) for image data
13. Time Series Analysis

Introduction to time series data
ARIMA and SARIMA models
Forecasting trends and seasonality
Working with time series data in Python
14. Model Deployment & Monitoring

Model deployment strategies: on-premise, cloud
Introduction to MLOps (Machine Learning Operations)
Monitoring and maintaining models in production
Tools for model deployment: Flask, Docker, AWS Sagemaker
15. Data Science Projects

End-to-end data science project examples
Kaggle competition walkthroughs
Real-world case studies (customer segmentation, fraud detection, etc.)

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