Data Science with Python Training in Nepal

Data Science with Python Training

Duration: 3 Months
Career: Data Science
Master Your Skills
Become a Professional
Build a Career!
Description

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

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 

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

Career Option

As the demand of data scientists is increasing rapidly across the globe this training course is beneficial for the aspiring data analysts and data scientists to acquire important Python skills facilitating data analysis. Broadway encourages the interested students to fill up the inquiry forms to start the enrollment process for upcoming data science training. Also, we are more than happy to hear from you over telephone or our Facebook page. Should you be more comfortable visiting our office location please feel to call us and set up an inquiry appointment at the earliest.

Data Science with Python Training - Outlines
    Course Outline: Python Programming
  • Installation

  • Python version and pip package manager

  • Introduction to Google Colab, Jupyter Notebook / IDE

    • Introduction to markdown
  • Python Program and statements

  • Python Arithmetic Operators

    • Using Python as calculator
  • How to define a variable name and Variable Naming convention in Python

  • Operator, Operands, and Operator Precedence

  • Changing and updating variable values in Python

  • Assigning multiple values to multiple variables

  • Data types in Python

  • Number data type: int, float, complex

    • Number data type
    • Taking input from the users
    • Type casting and type checking
    • Type validation
    • Number type with conditionals
  • Conditions and Recursion

    • Modulus operator
    • Boolean expressions
    • Logical operators
    • Conditional execution
    • Chained conditionals
    • Nested conditionals
    • Recursion
    • Stack diagrams for recursive functions
    • Infinite recursion
  • Iteration

    • Multiple assignments
    • Updating variables
    • The while statement
    • Break
    • Debugging
    • For loop
  • Python string

    • Introduction
    • Single line vs multiline string
    • Indexing
    • slicing
    • len()
    • Loop: for loop using range()
    • Loop with conditionals
    • continue vs break
    • characters vs substrings
    • Immutable data type
    • String methods: .replace(),.lower(), .upper(),.title() .lstrip(), .rstrip(), .strip(), .split(), .join(), .isdigit(), isupper(), islower(), .format()
  • Python Built-in data types

          List

    • Introduction to list
    • Indexing/Negative indexing
    • Slicing
    • looping & conditionals, len()
      • Different types of for loop ,while loop, for loop vs while loop
    • list of list and nested loop
    • Membership operators: in , not in
    • Mutable vs Immutable data type with exmaple
    • List methods: .insert(), .append(), .remove(), .pop(), .sort(), .extend(), .remove(), .sort()
    • List Comprehension
    • with if and else

    Tuple

    • Introduction to tuple
    • Indexing , slicing, looping
    • list vs tuple
    • Typecasting list -> tuple and tuple ->list
    • tuple unpacking

    Set

    • Introduction to set
    • .remove() , .add(), .discard() in sets
    • Type conversion
    • Set operation in Python : union, intersection, difference
    • Frozenset vs set

    Dictionary

    • Introduction
    • disctionary methods: .get(), .update(), .keys(), .values(), .pop()
    • Loop and dictionary comprehension
    • Nested Dictionary
  • None type

    • Identity Operators
  • Python Functions

    • Introduction and syntax: Why function is necessary
    • Function definition and function call
    • arguments/parameters in function
    • return statement in function
    • returning multiple value from function
    • Handling multiple return values
    • Default argument vs non default argument and why it is necessary
    • global and local variables
    • *args vs **kwargs
    • Introduction to Recursion and Recursion tree
    • pass keyword
  • OOP in python

    • Class and Objects
    • Class attribute and Object attribute
    • Initilizing object attribute
    • __init__()
    • Self keyword and its importance
    • Inheritance and its types
    • Single Inheritance
    • super() method
    • Mulitple Inheritance
    • Multi-level Inheritance
    • Abstraction and Access specifiers
    • Polymorphism
    • + and len()
    • Operator overloading using Dunders/magic method (user defined class)
    • function overriding
    • Encapsulation
  • Introduction to Exceptions

    • Understanding exceptions in Python
    • Types of exceptions and their meaning
    • Importance of exception handling
    • Handling Exceptions
      • Using try-except blocks to handle exceptions
      • Catching specific exceptions
      • Handling multiple exceptions
      • Using the finally clauses
    • Raising Exceptions
  • File Handling

    • open()
    • modes:
      • read : ‘r’
        • read(), readline(), readlines()
      • write: ‘w’
      • append: ‘a’
      • create: ‘x’
      • Comparison of append and write modes
    • File handling on CSV files.
    • DictReader and DictWriter
    • File handling with exception handling
  • Others

    • Lambda function/Annynomous Function:
      • map() , filter()
    • os library
    • random library
    • math library
  • Introduction to SQL in python

    • Creating database
    • Defining table structure with SQL statements and Specifying column names, data types, and constraints
    • Inserting new records into tables using SQL INSERT statements
    • Retrieving data from tables using SQL SELECT statements
    • Modifying existing data in tables using SQL UPDATE statements
    • Removing tables data  using SQL DELETE statements
    • Filtering data using the WHERE clause in SQL SELECT statements
  • Introduction to git and Github

    • Installing Git Bash
      • Overview of Git Bash
      • Installation
    • Creating a GitHub Account
      • Sign up for a GitHub account
      • Set up profile
    • Creating an Empty Repository
      • Create a new empty repository on GitHub
    • Initializing a Git Repository Locally
      • Initialize a Git repository on your local machine using Git Bash
    • Tracking Files
      • Add files to the staging area
      • Commit changes to the repository
    • Configuration of Global User Information
      • Configure your global user.name and user.email for Git
    • Branching (main)
      • Understand the concept of branches
      • Work with the default main branch
    • Adding a Remote
      • Connect your local repository to the remote repository on GitHub
      • Configure the remote repository URL
    • Pushing Changes
      • Push your local changes to the remote repository on GitHub
    • Cloning
      • Clone a repository from GitHub to your local machine using Git Bash
    • Creating a New Branch
      • Create a new branch for making changes
      • Switch between branches
    • Pushing Changes to a Branch
      • Push your changes to the remote repository on a specific branch
  • Pandas

    • Introduction to Pandas
    • DataFrame Data Structure
    • Reading and writing csv files using DataFrame
    • Manipulating DataFrame
  • Basic Data Visualization

    • Introduction to Matplotlib and Seaborn and plotly
    • Basic plotting using any of these library
  • Project Work (one of the following ):

    • Web Scraping project + Databse
      OR
    • Any desktop application: eg. Data Entry application
  • Data Science Course
  • Introduction

    • Prelude
    • The problem landscape
    • Defining Data Science
    • Demystifying Data Science, Decision Science, AI, ML and DL        
    • Overview of Data Scientist's Toolbox
  • Data Science Tool Box

    • 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
       
  • Probability and Statistics

    • Statistics (90% Theoretical Concept + 10% Practical)
    • Introduction
    • Data Description
    • Population and Sample
    • Variables and Variable Measurements Scales
    • Data Distribution
    • Central measure of Tendency (mean, median, mode)
    • Measure of dispersion (Variance, standard deviation)
    • Gaussian Normal Distribution
    • P values
    • Type 1 and Type 2 error
    • 1-tailed and 2-tailed Test
    • Statistical Test (z-test,t-test, chi-square test)
    • Pearson Correlation Coefficient
    • Spearman’s rank correlation
    • Addition Rule and Multiplication rule
    • Permutation and Combination
    • Function of random variables
    • Log-Normal Distribution
    • Bernoulli Distribution
    • Binomial Distribution
    • Pareto Distribution
    • Poisson distribution
  • Numpy

    • Introduction to Numpy
    • Random Data Generation
    • Numpy Array, Indexing & Operations
    • Array Data Structures in Numpy
    • Array operations and methods
    • Course Assignment

     

  • Pandas

    • Importing Datasets
    • Data Wrangling
    • Exploratory Data Analysis and Model Development

     

     

  • Scipy and Seaborn

    • Introduction to Scipy
    • Numerical Computaions
    • Exploratory Data Analysis
    • Model Generation
  • Plotting, Charting & Data Visualization

    • Principles of Information Visualization
    • Basic Charting
    • Charting Fundamentals
    • Applied Visualizations
  • Tableau Basics

    • Introduction to Tableau
    • Download and Install Tableau Public
    • Load Data from Excel
    • Creating Charts and Graphs
    • Basic Visual Analysis
  • Exploratory Data Analysis (EDA) and Hypothesis Testing

    • Overview of the Machine Learning methodology
    • Exploratory Data Analysis (EDA)
    • Introduction to Feature Engineering
    • Statistical Inference, Probability Distributions
    • Hypothesis Testing
  • Text Mining In Python

    • Basic Natural Language Processing
    • Working with NLTK
    • Text Preprocessing
    • Text Cleaning and regular expression
    • Regex Introduction
    • Regex codes
    • Text extraction with Python Regex
    • Stop Word Removal
    • Stemming
    • Lemmatization
    • POS Tagging

     

  • MACHINE LEARNING INTRODUCTION

    Machine Learning Introduction

    • ML core concepts
    • Unsupervised and Supervised Learning
    • Clustering, Classification, and Regression
    • Supervised Vs Unsupervised

     

  • Supervised Learning

    • Introduction to Linear Regression
    • Regression and Best Fit Line
    • Modeling and Evaluation in Python
    • Introduction to Logistic Regression
    • Classification & Sigmoid Curve Modeling and Evaluation
    • Introduction to SVM
    • Modeling and Evaluation of SVM in Python
  • Unsupervised Machine Learning

    • Understanding Clustering (Unsupervised)
    • K Means Algorithm
    • K Means theory
    • Modeling in Python
  • ML Web App development Streamlit

    • Introduction to Flask
    • URL and App routing
    • Streamlit application – ML Model Deployment
  • Projects

    • Exploratory Data Analysis (EDA) and Hypothesis Testing
    • Regression : Predict Employee Salary using regression
    • Text classification
    • Topic Modeling or Customer Segmentation
Upcoming Class Schedule
14 Apr 2024 03:00 PM - 04:30 PM
14 Apr 2024 06:00 PM - 07:30 PM
21 Apr 2024 10:30 AM - 12:00 PM
21 Apr 2024 12:00 PM - 01:30 PM
28 Apr 2024 08:00 AM - 09:30 AM
28 Apr 2024 11:00 AM - 12:30 PM
28 Apr 2024 03:00 PM - 04:30 PM
28 Apr 2024 06:00 PM - 07:30 PM

Quick Inquiry