Data Science With Python

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Data Science Syllabus:

Statistics Essentials for Analytics

• Comprehending the Data 
• The Applications of Probability                             
• Inference from Statistics                                      
• Clustering of Data                                           
• Verifying the Data
• Modeling Regression
• Data Science Overview
• Data Science
• Data Scientists
• Examples of Data Science
• Python for Data Science
• Data Analytics Overview
• An Overview of Data Science
• Procedures for Data Visualization
•  Data Wrangling, Data Exploration, and Model Selection.
• EDA, or exploratory data analysis
• Hypothesis Building and Testing
• Plotting
• Hypothesis Building and Testing
• Statistical Analysis and Business Applications
• Overview of Statistics
• Statistical and Non-Statistical Analysis
• Some Common Terms Used in Statistics
• Data Distribution: Central Tendency, Percentiles, Dispersion
• Histogram
• Bell Curve
• Hypothesis Testing
• Chi-Square Test
• Correlation Matrix
• Inferential Statistics
• Data Visualization in Python using Matplotlib
• Overview of Visualization of Data
• Python Libraries
• Plots
• Features of Matplotlib
• Plotting Line Properties with (x, y)ü
• Managing Colors and Line Patterns
• Set Properties for the Legend, Labels, and Axis
• Alpha and Annotation
• Several Plots
• Subplots
• Types of Plots and Seaborn
• Python: Environment Setup and Essentials
• Overview of the Anaconda
• Anaconda Python Distribution Installation for Windows, Mac OS, and Linux
• Installation of Jupyter Notebooks
• Jupyter Notebook Overview
• Differential Assignment
• Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
• Creating, accessing, and slicing tuples
• Creating, accessing, and slicing lists
• Creating, viewing, accessing, and modifying dicts
• Establishing and utilizing set operations
• Basic Operators: *, +, and in
• Functions
• Control Flow
• Mathematical Computing with Python (NumPy)
• Overview of NumPy
• Features, Uses, and Types of ndarray
• Class and Attributes of ndarray Object
• Basic Operations: Concept and Examples
• Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
• Copy and Views
• Functions Universal (ufunc)
• Manipulation of Shape
• Broadcasting
• Scientific computing with Python (Scipy)
• SciPy and its Characteristics
• SciPy sub-packages
• SciPy sub-packages –Integration
• SciPy sub-packages – Optimize
• Linear Algebra
• SciPy sub-packages – Statistics
• SciPy sub-packages – Weave
• SciPy sub-packages – I O
• Data Science with Python Web Scraping
• Web Scraping
• Common Data/Page Formats on The Web
• The Parser
• value of Objects
• Knowing the Tree
• Searching the Tree
• Navigating options
• Modifying the Tree
• Parsing Only Part of the Document
• Printing and Formatting
• Encoding
• Data Manipulation with Python (Pandas)
• Overview to Pandas
• Data Structures
• Series
• DataFrame
• Missing Values
• Data Operations
• Data Standardization
• Pandas File Read and Write Support
• SQL Operation
• Machine Learning with Python (Scikit–Learn)
• Overview of Machine Learning
• Method of Machine Learning
• How Learning Models, Both Supervised and Unsupervised, Function
• Scikit-Learn
• Supervised Learning Models – Linear Regression
• Supervised Learning Models: Logistic Regression
• K Nearest Neighbors (K-NN) Model
• Unsupervised Learning Models: Clustering
• Unsupervised Learning Models: Dimensionality Reduction
• Pipeline
• Model Persistence
• Model Evaluation – Metric Functions
• Natural Language Processing with Scikit-Learn
• NLP Overview
• NLP Approach for Text Data
• NLP Environment Setup
• NLP Sentence analysis
• NLP Applications
• Major NLP Libraries
• Scikit-Learn Approach
• Scikit – Learn Approach Built – in Modules
• Scikit – Learn Approach Feature Extraction
• Bag of Words
• Extraction Considerations
• Scikit – Learn Approach Model Training
• Scikit – Learn Grid Search and Multiple Parameters
• Pipeline
• Python integration with Hadoop, MapReduce and Spark
• Need for Integrating Python with Hadoop
• Big Data Hadoop Architecture
• MapReduce
• ClouderaQuickStart VM Set Up
• Apache Spark
• Resilient Distributed Systems (RDD)
• PySpark
• Spark Tools
• PySpark Integration with Jupyter Notebook