Data Analytics with Python


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