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Data Science with Python Training in Nepal

Data Science with Python Training

Duration: Duration: 2.5 Months Career Option: Data Science

Data Science with Python in Nepal

Broadway is the leading IT training institute offering Data Science with Python training in Nepal to make the students familiar with the core concept of spreadsheet and developing the data presentation skills. Data Science has evolved as one of the most demanding and promising career paths as a skilled professional. Broadway Infosys will help you access and collect data, understand it, process it, extract value from it, communicate it and lastly visualize it through our experts’ guidance and mentoring.

But why learn Python alongside? Python will enable developers to roll out programs and create a prototype which will make the development process faster. You can also switch to more difficult languages like Java and C if you want. To join the career-oriented Data Science with Python training in Nepal Broadway will help and guide you how to practice and gather expertise in data science.

Benefits of Data Science with Python Training in Nepal

This course is useful for those IT professionals who are interested in pursuing a career in data analytics. Data Science with Python course will help you build a strong understanding of Data Science and also enhance your analytics technique using python. After the completion of this course you’ll learn the main concepts of python programming and will also have the better understanding in data analytics machine learning, data visualization, web scraping, and natural language processing machine learning, data visualization, web scraping, and natural language processing. This course will further provide you with below benefits:

  • Gain Expertise in Machine Learning.
  • In-depth understanding of data science processes.
  • Important concepts of Python Programming.
  • Perform Data analysis.
  • Extract data from different websites, and many more.

Broadway has become the students’ choice because of its finest teaching methods and experts who are focused on giving the students what they came for. We make sure every trainee gets knowledge based on the latest updates which will help them easily recruited in the job market. We encourage interested students to contact us at the earliest and enroll for our upcoming training sessions on data science with python course.

Benefits of Data Science with Python Training in Broadway Infosys Nepal

We are driven towards building skilled individuals in Data Science and further add credentials to the students’ portfolio. After the concentrated training from a team of highly dedicated and committed experts, the students will have the chance to utilize their attained information professionally. Learning data science with python at Broadway gives you the following benefits.

  • Experienced Mentors and Trainers
  • Internship and job placement opportunities for capable candidates
  • Exposure to real world scenario
  • Opportunity to expand network with likeminded professionals
  • Affordable training costs and quality education
  • Facilitation for globally recognized international level certifications
  • Wider access to training equipment and materials
  • Scholarship offers for deserving and needy students

Pre-requisites for Data Science with Python

This course is designed for those students who want to work with python and looking to explore the field of data analytics and for those IT professionals who are interested in pursuing a career in analytics and data science. All the students must have a genuine interest and learning attitude to join this course.

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
  • Available 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 Databases
  • Introduction to Big Data
  • Working with Databases
  • Accessing Databases using Python
  • Course Assignment
  • Introduction to Numpy
  • Random Data Generation
  • Numpy Array, Indexing & Operations
  • Course Assignment
  • Importing Datasets
  • Data Wrangling
  • Exploratory Data Analysis
  • Model Development
  • Introduction to Scipy
  • Numerical Computaions
  • Exploratory Data Analysis
  • Model Generation
  • Principles of Information Visualization
  • Basic Charting
  • Charting Fundamentals
  • Applied Visualizations
  • Realtime Data Visualization
  • Basic Natural Language Processing
  • Working with NLTK
  • Classification of Text
  • Sentiment Analysis
  • Corpora Building
  • Topic Modeling
  • Introduction to Machine Learning using Scikit-learn
  • 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
22nd Dec, 2019 Sunday
12:00PM - 1:30PM

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    • Data Science with Python

    First of all, I would like to thank Broadway Infosys Team and my teachers. As we all(data science group) are from the Non-IT or non-CS background, we learned very new terms related to CS and IT and ... Read More