DataScience Training in Bangalore

Course Name : DataScience Python

Course Contents

• Module 1 : Data Science Overview
  • Introduction to Data Science.
  • Different Sectors Using Data Science.
  • Purpose and Components of Python.
• Module 2 : Introduction to Data Science
  • 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.
• Module 3: 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.
• Module 4: Python Environment Setup and Essentials
  • Anaconda.
  • Installation of Anaconda Python Distribution (contd.)
  • Data Types with Python
  • Basic Operators and Functions
• Module 5: Mathematical Computing with Python (NumPy)
  • Introduction to Numpy.
  • Activity-Sequence it Right.
  • Demo 01-Creating and Printing an nd-array & Knowledge Check.
  • Class and Attributes of nd-array.
  • Basic Operations
  • Activity-Slice It
  • Copy and Views
  • Mathematical Functions of Numpy
• Module 6: 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
• Module 7: Data Manipulation with Pandas
  • Introduction to Pandas.
  • Understanding DataFrame.
  • View and Select Data Demo.
  • Missing Values & Data Operations.
  • Pandas Sql Operation.
• Module 8: Machine Learning with Scikit–Learn
  • Machine Learning Approach.
  • Understand data sets and extract its features.
  • Identifying problem type and learning model how it works.
  • Train, test and optimizing the model.
  • Supervised Learning Model Considerations.
  • Scikit-Learn.
  • Supervised Learning Models - Linear Regression.
  • Supervised Learning Models - Logistic Regression.
  • Unsupervised Learning Models.
  • Pipeline, Model Persistence and Evaluation.
• Module 9: Natural Language Processing with Scikit Learn
  • NLP Overview & it's applications.
  • NLP Libraries-Scikit.
  • Extraction Considerations.
  • Scikit Learn-Model Training and Grid Search
• Module 10: Data Visualization in Python using matplotlib
  • Introduction to Data Visualization.
  • Line Properties.
  • (x,y) Plot and Subplots.
  • Types of Plots.
• Module 11: Web Scraping with BeautifulSoup
  • Web Scraping and Parsing.
  • Understanding and Searching the Tree.
  • Navigating options.
  • Demo 3 Navigating a Tree & Modifying the Tree.
  • Parsing and Printing the Document.
• Module 12: Python integration with Hadoop MapReduce and Spark
  • Why Big Data Solutions are Provided for Python.
  • Hadoop Core Components.
  • Python Integration with HDFS using Hadoop Streaming.
  • Demo 01 - Using Hadoop Streaming for Calculating Word Count.
  • Python Integration with Spark using PySpark.
  • Demo 02 - Using PySpark to Determine Word Count