Data Science With Python

• To perform scientific and technical computing using SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave.
• Perform data analysis and manipulation using data structures and tools provided in Pandas package
• Gain an in-depth understanding of supervised learning and unsupervised learning models like linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline
• Use Scikit-Learn package for natural language processing
• How to use the matplotlib library of Python for data visualization
• Extract useful data from websites by performing web scraping using Python
• Integrate Python with Hadoop, Spark, and MapReduce

• The Analytics professionals, who are willing to work with Python
• The Software professionals, who are looking to switch their career in Analytics field
• IT professionals  interested in pursuing a career in analytics
• The Graduates, looking forward to build their career in the Analytics and Data Science
• The Experienced professionals,  who would like to harness the data science in their fields
• Anyone, who has  a genuine interest in the field of Data Science

1. The Data Science: An Overview

• Introduction to the Data Science
• Different Sectors Using Data Science
• The Purpose and Components of Python

2. Data Analytics Overview

• The Data Analytics Process
• Exploratory the Data Analysis (EDA)
• EDA-Quantitative Technique
• EDA - Graphical Technique
• The Data Analytics Conclusion or Predictions
• The Data Analytics Communication
• The Data Types for Plotting

3. Statistical Analysis and Business Applications

• Introduction to the Statistics
• About Statistical and Non-statistical Analysis
• The Major Categories of Statistics
• About the Statistical Analysis Considerations
• The Population and Sample
• What is the Statistical Analysis Process?
• The Data Distribution
• Dispersion

4. Python Environment Setup and Essentials

• About the Anaconda
• The Installation of Anaconda Python Distribution
• Data Types in the Python
• Basic Operators and Functions

5. What is Mathematical Computing with Python (NumPy)?

• An Introduction to the Numpy
• The Activity-Sequence it Right
• Class and Attributes of ndarray
• All About the Basic Operations
• Activity-Slice It
• Copy and Views
• About the Mathematical Functions of Numpy

6. The Scientific computing with Python (Scipy)

• Introduction to the SciPy
• About the SciPy Sub Package - Integration and Optimization
• What is SciPy sub package?
• Know About the SciPy Sub Package - Statistics, Weave and IO

7. The Data Manipulation with Pandas

• Introduction to the Pandas
• Understanding DataFrame
• The Missing Values
• The Data Operations
• About File Read and the Write Support
• What is Pandas Sql Operation?

8 . The Natural Language Processing with Scikit Learn

• NLP: An Overview
• What are NLP Applications?
• About NLP Libraries-Scikit
• The Extraction Considerations
• The Scikit Learn-Model Training and Grid Search

9. The Data Visualization in Python using matplotlib

• Introduction to the Data Visualization
• What are Line Properties?  
• (x,y) Plot and Subplots
• The Types of Plots

10. Web Scraping with BeautifulSoup

• Web Scraping and Parsing
• Understanding and Searching the Tree
• Know the Navigating options
• Know About Modifying the Tree
• How to Parse and Print the Document?

11. Python integration with Hadoop MapReduce and Spark

• Know Why Big Data Solutions are provided for Python?
• Describing Hadoop Core Components
• The Python Integration with HDFS using Hadoop Streaming
• The Python Integration with Spark using PySpark