Data Science With Python

Python for Data Science Certification Training Course

Python for Data Science Certification Training Course

This Data Science with Python course will start your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the basic concepts of Python programming and gain in-depth knowledge in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, start your career with this interactive, hands-on course and become master in python.

What are the course objectives?

The Data Science with Python course will furnish you with deep knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning and natural language processing using Python.

Python programming has surpassed Java as the top language used to introduce US students to programming and computer science, and 46 percent of data science jobs list Python as a required skill.

What skills will you learn?

This Python for Data Science training course will enable you to:
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  • Gain a proper understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing.
  • Install the basic Python environment and other auxiliary tools and libraries
  • Understand the required concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
  • Able to Perform high-level mathematical computing using the NumPy package
  • Able to Perform scientific and technical computing using the SciPy package
  • Perform data analysis and manipulation using data structures with Pandas package
  • become expertise in machine learning using the Scikit-Learn package
  • Gain a proper understanding of supervised learning and unsupervised learning models 
  • Use the Scikit-Learn package for natural language processing
  • Use the matplotlib library of Python for data visualization
  • Extract useful data from websites by using web scrapping using Python
  • Integrate Python with Hadoop, Spark and MapReduce

Who should take this Python for Data Science course?

There is a highly demand for skilled data scientists across all industries that make this course suited for participants at all levels of experience. We recommend this Data Science with Python Course basicially for the following professionals:

  • Analytics professionals who want to become expert in Python
  • Software professionals who looking to get into the field of analytics
  • IT professionals interested in pursuing a career in python programming
  • Graduates looking to build a career in data science with python
  • Experienced professionals who would like to become data science expert in their fields
  • Anyone who want to build a career in the field of data science

Prerequisites: There is no defined qualification for this Data Science with Python course. The Python basics course included with this program provides additional coding guidance to your programming career.

Python for Data Science Certification Course Outline:

Lesson 01 - Data Science Overview

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

Lesson 02 - Data Analytics Overview

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

Lesson 03 - Statistical Analysis and Business Applications

  • Introduction to Statistics
  • Statistical and Non-statistical Analysis
  • Major Categories of Statistics
  • Statistical Analysis Considerations
  • Population and Sample
  • Statistical Analysis Process
  • Data Distribution
  • Dispersion
  • Histogram
  • Testing
  • Correlation and Inferential Statistics

Lesson 04 - Python Environment Setup and Essentials

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

Lesson 05 - Mathematical Computing with Python (NumPy)

  • Introduction to Numpy
  • Activity-Sequence it Right
  • Demo -Creating and Printing an nd array
  • Class and Attributes of  an array
  • Basic Operations
  • Activity-Slice It
  • Copy and Views
  • Mathematical Functions of Numpy

Lesson 06 - Scientific computing with Python (Scipy)

  • Introduction to SciPy
  • SciPy Sub Package - Integration and Optimization
  • SciPy sub package
  • Demo - Calculate Eigenvalues and Eigenvector
  • SciPy Sub Package - Statistics, Weave and IO

Lesson 07 - Data Manipulation with Pandas

  • Introduction to Pandas
  • Understanding DataFrame
  • View and Select Data Demo
  • Missing Values
  • Data Operations
  • File Read and Write Support
  • Pandas Sql Operation

Lesson 08 - Machine Learning with Scikit–Learn

  • Machine Learning Approach
  • How it Works
  • Supervised Learning Model Considerations
  • Scikit-Learn
  • Pipeline
  • Model Persistence and Evaluation

Lesson 09 - Natural Language Processing with Scikit Learn

  • NLP Overview
  • NLP Applications
  • NLP Libraries-Scikit
  • Extraction Considerations
  • Scikit Learn-Model Training and Grid Search

Lesson 10 - Data Visualization in Python using matplotlib

  • Introduction to Data Visualization
  • Line Properties
  • (x,y) Plot and Subplots

Lesson 11 - Web Scraping with BeautifulSoup

  • Web Scraping and Parsing
  • Understanding and Searching the Tree
  • Navigating options
  • Demo - Navigating a Tree
  • Modifying the Tree
  • Parsing and Printing the Document

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