Advanced Data Science With Python Course

Data Science with Python is a dynamic and rapidly evolving field that combines statistical analysis, machine learning, and programming to extract valuable insights and knowledge from large and complex datasets. Python, a versatile and user-friendly programming language, serves as a powerful tool for data scientists, providing a wide range of libraries and frameworks specifically designed for data manipulation, visualization, and modeling.

In this field, practitioners use Python to collect, clean, analyze, and visualize data, uncover patterns, build predictive models, and make informed decisions. With its extensive ecosystem, Python empowers data scientists to tackle real-world problems across various industries, from finance and healthcare to marketing and social sciences. By leveraging Python's flexibility and the abundance of open-source tools, data scientists can unleash the potential of data and drive innovation in today's data-driven world.

Benefits Of Advanced Data Science With Python Course

  • Rich Ecosystem of Libraries: Python provides a vast collection of libraries and frameworks specifically designed for data science, such as NumPy, pandas, Matplotlib, and scikit-learn.
  • Ease of Use and Readability: Python has a clean and readable syntax, making it beginner-friendly and easy to learn.
  • Broad Adoption in the Data Science Community: It is widely used by researchers, analysts, and data scientists across various industries. Its popularity ensures a strong support network and a vast array of online resources, tutorials, and libraries created by the community.
  • Powerful Data Manipulation and Analysis: Python's libraries, such as NumPy and pandas, provide efficient and flexible data structures and functions for data manipulation and analysis.
  • Integration with Machine Learning Libraries: Python serves as a bridge to popular machine learning libraries like scikit-learn, TensorFlow, and PyTorch.

Course Content

Advanced Data Wrangling with Pandas and Numpy
  • Pandas Data Frames and Series
  • Merging, Joining, and Concatenating Data Frames
  • Reshaping and Pivoting Data Frame
  • Handling Missing Data and Data Imputation
  • Hierarchical Indexing and Multi-level Indexing
  • Time Series Analysis with Pandas
  • NumPy arrays: Creation, indexing, and slicing
  • Basic operations with NumPy arrays: Arithmetic, broadcasting, and aggregations
  • Universal functions (ufuncs) in NumPy
  • Working with multi-dimensional arrays
  • Advanced Data Visualization with Plotly and Bokeh
  • Introduction to Plotly and Bokeh
  • Plotting with Plotly
  • Interactive Visualizations with Plotly
  • Creating Dashboards with Plotly
  • Creating Interactive Web Applications with Bokeh
  • Installing Matplotlib and setting up the environment
  • Anatomy of a Matplotlib figure: Figure, axes, and subplots
  • Basic plotting: Line plots, scatter plots, and bar plots
  • Customizing plot appearance: Colors, markers, line styles, labels, and titles
  • Probability Distributions
  • Bayesian Statistics
  • Markov Chain Monte Carlo (MCMC) Simulation
  • Hypothesis Testing with Python
  • ANOVA and MANOVA
  • Non-parametric Methods
  • Introduction to Time Series Analysis
  • Handling Time Series Data with Pandas
  • Time Series Visualization
  • Time Series Decomposition and Trend Analysis
  • Autoregressive Integrated Moving Average (ARIMA) models
  • Prophet
  • Introduction to Natural Language Processing
  • Text Processing with Python
  • Regular Expressions and Text Normalization
  • Sentiment Analysis and Text Classification
  • Topic Modeling with Latent Dirichlet Allocation (LDA)
  • Word Embeddings with Word2Vec
  • Advanced Regression Techniques
  • Regularization Techniques: Ridge, Lasso, Elastic Net
  • Gradient Boosting Machines (GBMs)
  • Support Vector Machines (SVMs)
  • Clustering Techniques: K-means, Hierarchical Clustering
  • Dimensionality Reduction Techniques: Principal Component Analysis (PCA), t-SNE
  • Introduction to Deep Learning
  • Neural Networks
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Autoencoders
  • Generative Adversarial Networks (GANs)
  • Introduction to Big Data and PySpark
  • RDDs, DataFrames, and Datasets
  • PySpark SQL and Spark MLlib
  • Distributed Computing with PySpark
  • Working with Spark on Cloud Platforms
  • Introduction to Power BI
  • Transforming Data with Power BI Desktop
  • Data Modeling with Power BI
  • DAX
  • Visualising data with reports
  • Introduction to the Power BI Service
  • Sharing & Collaboration Tools