
Data Science with Python Certification Training Course Agenda
Lesson 1: Data Science Overview
- Data Science
- Data Scientists
- Examples of Data Science
- Python for Data Science
Lesson 2: Data Analytics Overview
- Introduction to Data Visualization
- Processes in Data Science
- Data Wrangling, Data Exploration, and Model Selection
- Exploratory Data Analysis or EDA
- Data Visualization
- Plotting
- Hypothesis Building and Testing
Lesson 3: Statistical Analysis and Business Applications
- Introduction to 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
Lesson 4: Python: Environment Setup and Essentials
- Introduction to Anaconda
- Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux
- Jupyter Notebook Installation
- Jupyter Notebook Introduction
- Variable 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
- Creating and using operations on sets
- Basic Operators: ‘in’, ‘+’, ‘*’
- Functions
- Control Flow
Lesson 5: Mathematical Computing with Python (NumPy)
- NumPy Overview
- Properties, Purpose, 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
- Universal Functions (ufunc)
- Shape Manipulation
- Broadcasting
- Linear Algebra
Lesson 6: 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
Lesson 7: Data Manipulation with Python (Pandas)
- Introduction to Pandas
- Data Structures
- Series
- DataFrame
- Missing Values
- Data Operations
- Data Standardization
- Pandas File Read and Write Support
- SQL Operation
Lesson 8: Machine Learning with Python (Scikit–Learn)
- Introduction to Machine Learning
- Machine Learning Approach
- How Supervised and Unsupervised Learning Models Work
- 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
Lesson 9: 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
Lesson 10: Data Visualization in Python using Matplotlib
- Introduction to Data Visualization
- Python Libraries
- Plots
- Matplotlib Features:
✓ Line Properties Plot with (x, y)
✓ Controlling Line Patterns and Colors
✓ Set Axis, Labels, and Legend Properties
✓ Alpha and Annotation
✓ Multiple Plots
✓ Subplots
- Types of Plots and Seaborn
Lesson 11: Data Science with Python Web Scraping
- Web Scraping
- Common Data/Page Formats on The Web
- The Parser
- Importance of Objects
- Understanding the Tree
- Searching the Tree
- Navigating options
- Modifying the Tree
- Parsing Only Part of the Document
- Printing and Formatting
- Encoding
Lesson 12: Python integration with Hadoop, MapReduce and Spark
- Need for Integrating Python with Hadoop
- Big Data Hadoop Architecture
- MapReduce
- Cloudera QuickStart VM Set Up
- Apache Spark
- Resilient Distributed Systems (RDD)
- PySpark
- Spark Tools
- PySpark Integration with Jupyter Notebook
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