# Data Science

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## Introduction to Data Science

Data science is a multidisciplinary field that employs various techniques to extract insights and knowledge from data. A well-structured data science course module covers the essential aspects of data collection, cleaning, analysis, and visualization. This guide aims to provide an overview of what you can expect from a comprehensive data science course module.

## Duration - 6 Months

- Overview of data science
- Importance and applications of data science
- Data science lifecycle
- Setting up the Python environment (Anaconda, Jupyter Notebook, PyCharm)

- Python syntax and structure
- Basic data types and variables
- Control flow (if statements, loops)
- Functions and modules

- Lists, tuples, and sets
- Dictionaries
- Comprehensions (list, dictionary, set)

- Reading data from CSV, Excel, JSON, and SQL databases
- Web scraping with BeautifulSoup and Scrapy
- Accessing APIs with requests

- Introduction to relational databases
- SQL basics (SELECT, INSERT, UPDATE, DELETE)
- Connecting Python to SQL databases using SQLAlchemy

- Handling missing values
- Removing duplicates
- Data transformation (scaling, normalization)

- DataFrames and Series
- Indexing, slicing, and filtering
- Aggregation and grouping

- Objectives of EDA
- Tools and libraries (Pandas, NumPy, Matplotlib, Seaborn)

- Creating plots and charts with Matplotlib
- Advanced visualizations with Seaborn
- Interactive visualizations with Plotly

- Summary statistics (mean, median, mode)
- Measures of dispersion (variance, standard deviation)
- Correlation and covariance

- Basic probability concepts
- Probability distributions (normal, binomial, Poisson)

- Hypothesis testing
- Confidence intervals
- t-tests, chi-square tests, ANOVA

- Simple linear regression
- Multiple linear regression
- Logistic regression

- Overview of machine learning
- Supervised vs. unsupervised learning
- Model evaluation and selection

- Classification algorithms (decision trees, random forest, k-nearest neighbors)
- Regression algorithms (linear regression, polynomial regression)

- Clustering algorithms (k-means, hierarchical clustering)
- Dimensionality reduction (PCA, t-SNE)

- Basics of neural networks
- Building neural networks with TensorFlow and Keras

- Image classification and processing
- Building and training CNN models

- Overview of big data technologies
- Working with Hadoop and Spark

- Generating reports with Jupyter Notebook
- Using BI tools (Tableau, Power BI)

- Designing and implementing a data science project
- Collecting, cleaning, and analyzing data
- Building predictive models
- Visualizing and presenting results