Data Science With Python

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The Data Science with Python course has been designed to provide in-depth knowledge of the various libraries and packages that are required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using Python. The data science with python course is based on the live projects, demonstrations, assignments, and the case studies to provide a hands-on as well as practical experience to the aspirants.

Moreover, the course insights on PROC SQL and other statistical procedures such as: PROC MEANS, PROC FREQ, etc. along with the advanced analytics techniques to have a clear vision of decision tree, regression and clustering.

After completing the Data Science with Python training the candidates would be able to:

  • 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
Target audience
  • 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
Prerequisites

There are no prerequisites for this course. The Python basics course included with this course provides an additional coding guidance.

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

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