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Data Science with Python

Data science with python
12
Mar

Objectives:

 Know what Data Science is, Discuss the roles and responsibilities of a Data Scientist, List various applications of Data Science, Understand how Data Science and Big Data work together, Explore Data Science as a discipline, Understand how and why Data Science is gaining importance and Understand what Python is and what problems it resolves.

 

Detailed Coverage:

  • Introduction to Data Science
  • Different Sectors Using Data Science
  • Purpose and Components of Python
  • Data Analytics Overview
  • Data Analytics Process
  • 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
  • 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
  • Python: Environment Setup and Essentials
  • Anaconda
  • Data Types with Python
  • Basic Operators and Functions
  • Mathematical Computing with Python (Numpy)
  • Introduction to Numpy
  • Activity- Sequence it Right
  • Class and Attributes of ndarray
  • Basic Operations
  • Copy and Views
  • Mathematical Functions of Numpy
  • 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
  • Data Manipulation with Pandas
  • Introduction to Pandas
  • Understanding DataFrame
  • View and Select Data Demo
  • Missing Values
  • Data Operations
  • File Read and Write Support
  • Activity- Sequence it Right
  • Pandas Sql Operation
  • Machine Learning with Scikit–Learn
  • Machine Learning Approach
  • How it Works
  • Supervised Learning Model Considerations
  • Scikit-Learn
  • Supervised Learning Models
  • Unsupervised Learning Models
  • Pipeline
  • Model Persistence and Evaluation
  • Natural Language Processing (NLP) with Scikit-Learn
  • NLP Overview
  • NLP Applications
  • NLP Libraries-Scikit
  • Extraction Considerations
  • Scikit Learn-Model Training and Grid Search
  • Data Visualization in Python using Matplotlib
  • Introduction to Data Visualization
  • Line Properties
  • (x,y) Plot and Subplots
  • Types of Plots
  • Web Scraping
  • 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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Course Content

Total learning: / 1 quiz Time: 3 days

Instructor

4.5

2 rating

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  • Kanika

    Very well done, the combination of whiteboard lesson and hands on code was really good.

    The data science course is well-structured and the course material eAge offers is superb. The mentor was incredibly helpful and the real-time data science projects build my confidence in cracking the job interview
  • Naren Kumar

    Good training for data scientist course

    Great place to start learning. So many useful references. The overall experience was very satisfying.
Free

Money-Back Guarantee, Condition Applied...

Includes

  • Real time virtual classess
  • Pre course reading material
  • Suppliment resources
  • Language: English
  • Certificate of completion

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Your Dream Course Is Only A Step Away




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Your Dream Course Is Only A Step Away




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