About the course

The Data Science with Python course is designed to bestow an inside and out information of the various libraries and modules required to perform data analysis, data visualization, web scraping, machine learning, and NLP with help of Python language.

What are the requirements?

No prior knowledge of Statistics the language of python or analytic techniques is required.

What I am going to get from this course

  • Gain top to bottom knowledge of data science process, data wrangling, data exploration, data visualization, hypothesis building, and testing.
  • Learn to Install the required Python environment and other tools and libraries
  • Comprehend the fundamental ideas of Python programming like information composes, tuples, records, dicts, basic operators, and functions.
  • Perform high-level mathematical computing utilizing NumPy package and its substantial library of mathematical functions
  • Perform logical and specialized computing using SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave.

Who is the target audience?

  • Analytics professionals who are interested to work with Python
  • IT Professionals looking for a career switch in the field of data analysis
  • Graduates looking to build a career in Analytics and Data Science

Curriculum


Module 1: Data Science Overview

  • 1.1  Introduction to Data Science Preview
  • 1.2  Different Sectors Using Data Science Preview
  • 1.3  Purpose and Components of Python

Module 2: Data Analytics Overview

  • 2.1  Data Analytics Process
  • 2.2  Knowledge Check
  • 2.3  Exploratory Data Analysis(EDA)
  • 2.4  EDA-Quantitative Technique
  • 2.5  EDA - Graphical Technique Preview
  • 2.6  Data Analytics Conclusion or Predictions
  • 2.7  Data Analytics Communication Preview
  • 2.8  Data Types for Plotting
  • 2.9  Data Types and Plotting

Module 3: Statistical Analysis and Business Applications

  • 3.1  Introduction to Statistics
  • 3.2  Statistical and Non-statistical Analysis
  • 3.3  Major Categories of Statistics
  • 3.4  Statistical Analysis Considerations
  • 3.5  Population and Sample
  • 3.6  Statistical Analysis Process
  • 3.7  Data Distribution
  • 3.8  Dispersion
  • 3.9  Knowledge Check
  • 3.10  Histogram
  • 3.11  Knowledge Check
  • 3.12  Testing
  • 3.13  Knowledge Check
  • 3.14  Correlation and Inferential Statistics

Module 4: Python Environment Setup and Essentials

  • 4.1  Anaconda
  • 4.2  Installation of Anaconda Python Distribution (contd.)
  • 4.3  Data Types with Python
  • 4.4  Basic Operators and Functions

Module 5: Mathematical Computing with Python (NumPy)

  • 5.1  Introduction to Numpy
  • 5.2  Activity-Sequence it Right
  • 5.3  Demo 01-Creating and Printing an ndarray
  • 5.4  Knowledge Check
  • 5.5  Class and Attributes of ndarray
  • 5.6  Basic Operations
  • 5.7  Activity-Slice It
  • 5.8  Copy and Views
  • 5.9  Mathematical Functions of Numpy

Module 6: Scientific computing with Python (Scipy)

  • 6.1  Introduction to SciPy
  • 6.2  SciPy Sub Package - Integration and Optimization
  • 6.3  Knowledge Check
  • 6.4  SciPy sub package
  • 6.5  Demo - Calculate Eigenvalues and Eigenvector
  • 6.6  Knowledge Check
  • 6.7  SciPy Sub Package - Statistics
  • 6.8   Weave and IO

Module 7: Data Manipulation with Pandas

  • 7.1  Introduction to Pandas
  • 7.2  Knowledge Check
  • 7.3  Understanding DataFrame
  • 7.4  View and Select Data Demo
  • 7.5  Missing Values
  • 7.6  Data Operations
  • 7.7  Knowledge Check
  • 7.8  File Read and Write Support
  • 7.9  Knowledge Check-Sequence it Right
  • 7.10  Pandas Sql Operation

Module 8: Machine Learning with Scikit–Learn

  • 8.1  Machine Learning Approach
  • 8.2  Supervised Learning Model Considerations
  • 8.3  Knowledge Check
  • 8.4  Scikit-Learn
  • 8.5  Knowledge Check
  • 8.6  Supervised Learning Models - Linear Regression
  • 8.7  Supervised Learning Models - Logistic Regression
  • 8.8  Unsupervised Learning Models
  • 8.9  Pipeline
  • 8.10  Model Persistence and Evaluation

Module 9: Natural Language Processing with Scikit Learn

  • 9.1  NLP Overview
  • 9.2  NLP Applications
  • 9.3  Knowledge check
  • 9.4  NLP Libraries-Scikit
  • 9.5  Extraction Considerations
  • 9.6  Scikit Learn-Model Training and Grid Search

Module 10: Data Visualization in Python using matplotlib

  • 10.1  Introduction to Data Visualization
  • 10.2  Knowledge Check
  • 10.3  Line Properties
  • 10.4  (x
  • 10.5  y) Plot and Subplots

Module 11: Web Scraping with BeautifulSoup

  • 11.1  Web Scraping and Parsing
  • 11.2  Knowledge Check
  • 11.3  Understanding and Searching the Tree Preview12:56
  • 11.4  Navigating options
  • 11.5  Demo3 Navigating a Tree
  • 11.6  Knowledge Check
  • 11.7  Modifying the Tree
  • 11.8  Parsing and Printing the Document

Module 12: Python integration with Hadoop MapReduce and Spark

  • 12.1  Why Big Data Solutions are Provided for Python
  • 12.2  Hadoop Core Components
  • 12.3  Python Integration with HDFS using Hadoop Streaming
  • 12.4  Demo 01 - Using Hadoop Streaming for Calculating Word Count
  • 12.5  Knowledge Check
  • 12.6  Python Integration with Spark using PySpark
  • 12.7  Demo 02 - Using PySpark to Determine Word Count
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