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
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
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