Contents
- Introduction to Data Analytics with Python
- Importance of data analytics
- Overview of the data analytics process
- Role of Python in data analytics
- Setting Up Your Data Analytics Environment
- Installing Python and essential libraries
- Introduction to Jupyter Notebooks
- Exploratory Data Analysis (EDA) with Pandas
- Loading and exploring datasets
- Data cleaning and preprocessing
- Descriptive statistics and data summarization
- Data Visualization with Matplotlib and Seaborn
- Basic plotting techniques
- Advanced visualization options
- Creating meaningful visualizations for analysis
- Statistical Analysis with NumPy and SciPy
- Introduction to statistical concepts
- Hypothesis testing and confidence intervals
- Correlation and regression analysis
- Time Series Analysis
- Handling time series data with Pandas
- Time series visualization and decomposition
- Forecasting and trend analysis
- Machine Learning for Data Analytics
- Introduction to machine learning concepts
- Supervised and unsupervised learning
- Using scikit-learn for machine learning tasks
- Introduction to Data Mining
- Overview of data mining techniques
- Association rule mining
- Clustering and classification
- Text Analytics and Natural Language Processing (NLP)
- Basics of text processing
- Sentiment analysis
- Named entity recognition
- Data Ethics and Privacy
- Ethical considerations in data analytics
- Ensuring privacy and responsible data handling
- Compliance with data protection regulations
