Learn Data Science Tutorial With Python

Overview

• Data Science is rapidly growing, aiding organizations in decision-making, problem-solving, and understanding behavior.
• Python and R are common languages used in Data Science.
• This tutorial covers fundamentals of Data Science and Python programming.

Python Libraries for Data Science

• Pandas: For data manipulation.
• NumPy: For numerical computing.
• Matplotlib: For data visualization.
• Seaborn: For advanced data visualization.
• Scikit-learn: For machine learning models.

Data Loading Techniques

• Importing data for analysis:
  • CSV files using Pandas.
  • Excel files.
  • JSON files.
  • SQL databases.
  • Web scraping using BeautifulSoup.
  • MongoDB.

Data Preprocessing

• Steps to clean and prepare data:
  • Handling missing data with Pandas.
  • Removing duplicates.
  • Scaling and normalizing data.
  • Data aggregation and grouping.
  • Feature selection using Sklearn.
  • Encoding categorical data.
  • Outlier detection using Z-score and Interquartile Range.
  • Managing imbalanced datasets.

Data Analysis

• Techniques to derive insights:
  • Exploratory Data Analysis (EDA).
  • Univariate and multivariate analysis.
  • Correlation calculations.
  • Various statistical tests: T-tests, ANOVA, Mann-Whitney, Chi-Square, PCA.

Data Visualization

• Enhancing data understanding via visuals:
  • Using Matplotlib:
    • Line charts, bar plots, histograms, heatmaps, box plots, scatter plots, pie charts, 3D plots.
  • Using Seaborn:
    • Pair plots, count plots, violin plots, strip plots, KDE plots, joint plots, reg plots.
  • Interactive Visualization:
    • Using Plotly and Bokeh for dynamic visuals.

Machine Learning

• Developing data-driven models:
  • Understanding algorithms like Linear Regression, Naive Bayes, KNN, etc.
  • Importance of Machine Learning in transforming raw data into insights and predictions.

Additional Resources

• Reference articles and courses related to Data Science and Python.
• Courses on platforms like GeeksforGeeks for further learning.