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Data Analytics using Python Training in Chandigarh




Data is the lifeblood of an organization. Competency in programming is an essential skill for successfully extracting information and knowledge from data. The goal of this course is to introduce learners to the basics of programming in Python and to give a working knowledge of how to use programs to deal with data.
In this course, we will first cover the basics of programming and then focus on using Python on the entire data management process from data acquisition to analysis of data big data and small data. This is an intensive hands-on course that will equip and reward learners with proficiency in data management skills.

What you'll learn

a.Become familiar with working with relational databases, using SQL based languages such as MySql, dealing with formatted data (XML, JSON, etc.).
b. Use Python to work with and analyze data from databases as well as from the web.
c. Use Big Data processing frameworks like Hadoop and MapReduce.

Course Detail

Duration: 6 Months / 6 Weeks Version: Latest
Regular: 2 Hours per day WeekEnds: 2 - 3 Hours per day
Weekdays: Monday - Friday Weekend: : Saturday and Sunday
Online Training: Available

Introduction to Data Analytics

  • Why Analytics..?
  • Traditional Data Management and Analytical tools
  • Types of Analytics
  • Hind sight, ore sight and insight
  • Dimensions and measures

Introduction to Python

  • Basics of Python
  • Starting with python interpreter
  • Control flow statements
  • List,tuple,Dictionary and other data structures
  • Lambda with mapper,reduce and filter
  • List and dictionary comprehension

Advanced concepts

  • Writing functions
  • File operations in python
  • Error handling and raise exceptions
  • What is a module?
  • Sys module and regular expressions
  • Working with classes
  • Standard libraries

Data science packages in python

  • Numpy
  • Scipy
  • Pandas and matplotlib
  • Seaborn
  • Scikit

Web scraping and Database handling

  • Scraping Webpages
  • Beautifulsoup package
  • Real time project with web scraping
  • Handling database with python

Introduction to Machine learning

  • What is machine learning?
  • Introduction to statistics
  • Types of Learning
  • Hypothesis space,inductive bias
  • Evaluation of hypothesis

Machine Learning Algorithms

  • Linear regression algorithm
  • Decision tree algorithm
  • Linear regression vs Logistic regression
  • KNN algorithm
  • Clustering with k-means
  • Building models and evalution with Scikit
Basics of Programming . Knowledge of C/C++ an Asset.
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