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Data Science with Python Training in Nepal

Data Science with Python Training

Duration: Duration: 1.5 Months Career Option: Data Science

Data Science with Python Training in Nepal

This training course is designed for students familiar with Python programming environment and anyone wishing to learn data analysis using Python. Broadway offers standard Data Science with Python Training in Nepal with an objective of producing qualified and competent data scientists. After the successful completion of this training the students will be able to implement the necessary steps to use Python for data analysis purpose.

Course Highlights

  • Basic introduction to Python programming environment
  • Understand fundamental Python programming techniques
  • Learn data manipulation and cleaning techniques using Python
  • Understand overall statistical analyses techniques using Python
  • Learn scientific data libraries (such as Pandas) in Python
  • Understand applied plotting, charting and data representation in Python
  • Learn applied machine learning in Python
  • Project Work

Benefits of Data Science with Python training at Broadway Infosys Nepal

  • Data scientists with expertise in Python programming as instructors
  • Opportunity to expand network with Python programmers and data scientists
  • Regular data analysis exercises to test specific Python skills aligning with data science
  • Interactive learning environment with well equipped training labs
  • Internship/job placement opportunity in relevant  position in reputed companies
  • Regular interaction with industry experts and data scientists
  • Affordable and reasonable cost of training

 

Courses Outline :- Data Science with Python Training in Nepal
  • Environment setup
  • The python programming language
  • What is program?
  • What is debugging?
  • Values and types
  • Variables
  • Variable names and keywords
  • Operators and operands
  • Expressions and statements
  • Interactive mode and script mode
  • Order of operations
  • String operations
  • Comments
  • Debugging
  • Function calls
  • Type conversion functions
  • Math functions
  • Composition
  • Adding new functions
  • Definitions and uses
  • Flow of execution
  • Parameters and arguments
  • Variables and parameters are local
  • Stack diagrams
  • Fruitful functions and void functions
  • Why functions?
  • Importing with from
  • Debugging
  • Modulus operator
  • Boolean expressions
  • Logical operators
  • Conditional execution
  • Alternative execution
  • Chained conditionals
  • Nested conditionals
  • Recursion
  • Stack diagrams for recursive functions
  • Infinite recursion
  • Keyboard input
  • Debugging
  • Return values
  • Incremental development
  • Composition
  • Boolean functions
  • More recursion
  • Leap of faith
  • Checking types
  • Debugging
  • Multiple assignments
  • Updating variables
  • The while statement
  • Break
  • Debugging
  • For loop
  • A string is a sequence
  • Len
  • Traversal with a for loop
  • String slices
  • Strings are immutable
  • Searching
  • Looping and counting
  • String methods
  • The in operator
  • String comparison
  • Debugging
  • A list is a sequence
  • Lists are mutable
  • Traversing a list
  • List operations
  • List slices
  • List methods
  • Map, filter and reduce
  • Deleting elements
  • Lists and strings
  • Objects and values
  • Aliasing
  • List arguments
  • Debugging

 

  • Dictionary as a set of counters
  • Looping and dictionaries
  • Reverse lookup
  • Dictionaries and lists
  • Memos
  • Global variables
  • Long integers
  • Debugging
  • Tuples are immutable
  • Tuple assignment
  • Tuples as return values
  • Variable-length argument tuples
  • Lists and tuples
  • Dictionaries and tuples
  • Comparing tuples
  • Sequences of sequences
  • Debugging
  • Usage
  • Union, Difference
  • availables methods
  • Introduction
  • Exceptions versus Syntax Errors
  • Raising an Exception
  • The AssertionError Exception
  • The try and except Block: Handling Exceptions
  • The else Clause       
  • Persistence
  • Reading and writing
  • Format operator
  • Filenames and paths
  • Catching exceptions
  • Databases
  • Writing modules
  • Debugging
  • Introduction, Application and Usage
  • reader and writer
  • DictReader and DictWriter
  • Simple CSV processing using functional functional programming
  • User-defined types
  • Attributes
  • Real World Example
  • Instances as return values
  • Objects are mutable
  • Copying
  • Debugging
  • Object-oriented features
  • The self
  • Printing objects
  • The init method
  • The __str__ method
  • Other special methods
  • Operator overloading
  • Type-based dispatch
  • Polymorphism
  • @staticmethod
  • Debugging
  • Introduction
  • checking callable or not
  • decorators
  • creating and using decorators
  • Introduction
  • Example
  • Class attributes
  • Private, Protected and Public
  • Multiple Inheritance
  • Class diagrams
  • Debugging
  • Data encapsulation
  • Installing Git
  • status, log, commit push, pull commands 
  • Branch, Tags and Multiple remote concept and Implementation
  • checkout, reset, rebase, merge concept
  • Gitlab vs Github vs Bitbucket
  • Trello, Slack, Jira
  • Advanced Strings, Date & Time
  • Python os, re, sys
  • GUI basics: Tkinter, Tcl/Tk
  • Comprehensions: List, Dictionary
  • CSV, Json, XML, SQLite with Python
  • Data Science/Visualization: pandas, matplotlib
  • Jupyter NoteBook

 

 

  • Prelude
  • The problem landscape
  • Defining Data Science
  • Demystifying Data Science, Decision Science, AI, ML and DL        
  • Overview of Data Scientist's Toolbox
  • Python  - Quick recap
  • Python 2.7.x or 3.x ?
  • Installation and setup
  • Data types, functions and important packages
  • Data manipulation & Data Engineering
  • Data Visualization
  • Overview of the Machine Learning methodology
  • Exploratory Data Analysis (EDA)
    • Univariate Analysis
    •  Bivariate Analysis
  • Feature Engineering
  • Introduction to Statistics
  • Statistical Inference, Probability Distributions
  • Hypothesis Testing
  • Introduction to Machine Learning
  • Supervised Machine Learning [Linear Regression/ Logistic Regression/ Decision Trees]
  • Unsupervised Machine Learning [Clustering/Association]
  • Evaluating Machine Learning models
  • Regularization and Hyperparameter tuning
  • Ensemble Modelling  [Bagging/Boosting]
  • Project use-case overview
  • Defining the problem statement
  • Business solution blueprint development
  • Explore & Define the machine learning use-case
  • Exploratory Data Analysis & Feature Engineering
  • Approach for model development, evaluation and optimization
  • Storyboarding  - Connecting the dots

Up Coming Schedule

19th Aug, 2018 Sunday
6:30 AM - 8:00 AM

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