COURSE OVERVIEW

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Data analytics with Python involves using the Python programming language and its ecosystem of libraries and tools to analyze, interpret, and visualize data. It’s a process that allows individuals and organizations to extract insights from data, make data-driven decisions, and solve complex problems.

Basics of Python:

  • Variables, data types, operators
  • Control structures (if-else, loops)
  • Functions and modules

    • NumPy:

  • Arrays, array operations
  • Indexing, slicing, broadcasting

    • Pandas:

  • Series, DataFrame basics
  • Data manipulation (filtering, sorting, merging)
  • Handling missing data, reshaping data

    • Matplotlib:

  • Line plots, scatter plots, histograms
  • Customizing plots: labels, colors, annotations

    • Seaborn:

  • Statistical visualization
  • Advanced plots: pair plots, heatmaps

    • Descriptive Statistics:

  • Measures of central tendency, variability
  • Distribution plots: box plots,
  • Pearson correlation coefficient
  • Heatmaps for correlation visualization

    • Hypothesis Testing:

  • T-tests, chi-square tests
  • ANOVA (Analysis of Variance)

    • Regression Analysis:

  • Linear regression: simple, multiple
  • Model evaluation: R-squared, adjusted R-squared

    • Data Cleaning Techniques:

  • Handling missing values, outliers
  • Data transformation: scaling, normalization

    • Feature Engineering:

  • Creating new features from existing data
  • Handling categorical variables: encoding techniques

    • Reshaping Data:

  • Pivot tables, melting data
  • Stack and unstack operations

    • Merging and Joining Data:

  • Concatenating, merging datasets
  • Join operations (inner, outer, left, right)

    • Handling Time Series Data:

  • Resampling, shifting
  • Rolling statistics, decomposition

    • Forecasting Techniques:

  • Moving average, ARIMA models
  • Seasonal decomposition

    • Supervised Learning:

  • Basics of classification and regression
  • Using Scikit-learn for ML tasks

    • Unsupervised Learning:

  • Clustering techniques: K- means, hierarchical
  • Dimensionality reduction: PCA, t-SNE

    • Text Preprocessing:

  • Tokenization, stemming, lemmatization
  • Stopword removal, text normalization

    • Basic NLP Tasks:

  • Sentiment analysis, text classification
  • Using NLTK and Scikit-learn for text analysis

    • Advanced Visualization:

  • Interactive visualizations using Plotly
  • Geographic data visualization with Folium

    • Network Analysis:

  • Analyzing and visualizing networks (nodes and edges)

    • Real-world Data Analysis Projects:

  • Applying data analysis techniques to solve practical problems
  • Working with datasets from various domains (finance, healthcare, social media)

    • Data Ethics:

  • Bias, fairness, transparency in data analysis
  • Privacy concerns in data handling