Data Science With Python Training In Chennai

Automation Minds located in ECR and OMR provides Data Science with Python training in Chennai. Learn analytics from data manipulation to predictive modeling – using Python. Get certified in 6 weeks.

In this Python Certification Training, you’ll become an expert in analytics techniques using the Python data science tool. Python Training institute in Chennai for Data Science offers a comprehensive learning foundation that you can build your analytics career on.

DATA SCIENCE WITH PYTHON TRAINING COURSES IN CHENNAI

  1. Introduction to Data Science with Python
  2. Scientific Distributions Used in Python for Data Science
  3. Machine Learning
  4. Practical Applications of Machine Learning

PYTHON DATA SCIENCE TOPICS

Learn to use Python as your Data Science tool of choice This course teaches you Python as a tool for data science, and specifically for implementing an advanced Machine Learning algorithm with Python

  • Critical Python programming skills.
  • Accessing, transforming and manipulating data.
  • Improving data quality for reporting and analytics.
  • Fundamentals of statistics and analytics.
  • Working with Hadoop, Hive, Pig and SAS.
  • Exploring and visualizing data.
  • Essential communication skills.
  • Machine learning and predictive modeling techniques.
  • How to apply these techniques to distributed and in-memory big data sets.
  • Pattern detection.
  • Experimentation in business.
  • Optimization techniques.
  • Time series forecasting.

 

GETTING STARTED WITH PYTHON

  • Python Overview
  • About Interpreted Languages
  • Advantages/Disadvantages of Python pydoc.
  • Starting Python
  • Interpreter PATH
  • Using the Interpreter
  • Running a Python Script
  • Python Scripts on UNIX/Windows
  • Python Editors and IDEs.
  • Using Variables
  • Keywords
  • Built-in Functions
  • StringsDifferent Literals
  • Math Operators and Expressions
  • Writing to the Screen
  • String Formatting
  • Command Line Parameters and Flow Control.

 

SEQUENCES AND FILE OPERATIONS

  • Lists
  • Tuples
  • Indexing and Slicing
  • Iterating through a Sequence
  • Functions for all Sequences
  • Using Enumerate()
  • Operators and Keywords for Sequences
  • The xrange() function
  • List Comprehensions
  • Generator Expressions
  • Dictionaries and Sets.

 

DEEP DIVE – FUNCTIONS SORTING ERRORS AND EXCEPTION HANDLING

  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values. Sorting
  • Alternate Keys
  • Lambda Functions
  • Sorting Collections of Collections
  • Sorting Dictionaries
  • Sorting Lists in Place
  • Errors and Exception Handling
  • Handling Multiple Exceptions
  • The Standard Exception Hierarchy
  • Using Modules
  • The Import Statement
  • Module Search Path
  • Package Installation Ways.

 

REGULAR EXPRESSIONSIT’S PACKAGES AND OBJECT ORIENTED PROGRAMMING IN PYTHON

  • The Sys Module
  • Interpreter Information
  • STDIO
  • Launching External Programs
  • PathsDirectories and Filenames
  • Walking Directory Trees
  • Math Function
  • Random Numbers
  • Dates and Times
  • Zipped Archives
  • Introduction to Python Classes
  • Defining Classes
  • Initializers
  • Instance Methods
  • Properties
  • Class Methods and DataStatic Methods
  • Private Methods and Inheritance
  • Module Aliases and Regular Expressions.

 

DEBUGGING, DATABASES AND PROJECT SKELETONS

  • Debugging
  • Dealing with Errors
  • Using Unit Tests
  • Project Skeleton
  • Required Packages
  • Creating the Skeleton
  • Project Directory
  • Final Directory Structure
  • Testing your Setup
  • Using the Skeleton
  • Creating a Database with SQLite 3
  • CRUD Operations
  • Creating a Database Object.

 

MACHINE LEARNING USING PYTHON

  • Introduction to Machine Learning
  • Areas of Implementation of Machine Learning
  • Why Python
  • Major Classes of Learning Algorithms
  • Supervised vs Unsupervised Learning
  • Learning NumPy
  • Learning Scipy
  • Basic plotting using Matplotlib
  • Machine Learning application

 

SUPERVISED AND UNSUPERVISED LEARNING

  • Classification Problem
  • Classifying with k-Nearest Neighbours (kNN)

 

ALGORITHM

  • General Approach to kNN
  • Building the Classifier from Scratch
  • Testing the Classifier
  • Measuring the Performance of the Classifier.
  • Clustering Problem
  • What is K-Means Clustering
  • Clustering with k-Means in Python and an

APPLICATION EXAMPLE.

  • Introduction to Pandas
  • Creating Data Frames
  • GroupingSorting
  • Plotting Data
  • Creating Functions
  • Converting Different Formats
  • Combining Data from Various Formats
  • Slicing/Dicing Operations.

SCIKIT AND INTRODUCTION TO HADOOP

  • Introduction to Scikit-Learn
  • Inbuilt Algorithms for Use
  • What is Hadoop and why it is popular
  • Distributed Computation and Functional Programming
  • Understanding MapReduce Framework Sample MapReduce Job Run.

HADOOP AND PYTHON

  • PIG and HIVE Basics
  • Streaming Feature in Hadoop
  • Map Reduce Job Run using Python
  • Writing a PIG UDF in Python
  • Writing a HIVE UDF in Python
  • Pydoop and MRjob Basics.

PYTHON PROJECT WORK

  • Real world project
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