Data Analytics with Python Course in Mysuru Overview


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