Data Science with Python Training in Nepal

Data Science with Python Training

Duration: 3 Months

Career: Data Science

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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 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

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

Basic syntax
- Environment setup
- The python programming language
- What is program?
- What is debugging?
Variables, expressions and statements
- 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
Functions
- 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
Conditionals and recursion
- 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
Fruitful functions
- Return values
- Incremental development
- Composition
- Boolean functions
- More recursion
- Leap of faith
- Checking types
- Debugging
Iteration
- Multiple assignments
- Updating variables
- The while statement
- Break
- Debugging
- For loop
Strings
- 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
Lists
- 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
Dictionaries
- Dictionary as a set of counters
- Looping and dictionaries
- Reverse lookup
- Dictionaries and lists
- Memos
- Global variables
- Long integers
- Debugging
Tuples
- 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
Set
- Usage
- Union, Difference
- Available methods
Exception Handling
- Introduction
- Exceptions versus Syntax Errors
- Raising an Exception
- The AssertionError Exception
- The try and except Block: Handling Exceptions
- The else Clause
Files
- Persistence
- Reading and writing
- Format operator
- Filenames and paths
- Catching exceptions
- Databases
- Writing modules
- Debugging
CSV
- Introduction, Application and Usage
- Reader and writer
- DictReader and DictWriter
- Simple CSV processing using functional functional programming
Pandas
- Introduction to Pandas
- DataFrame Data Structure
- DataFrame Indexing and Loading
- Querying a DataFrame
- Indexing Dataframes
- Manipulating DataFrame
Database with Python
- Installations
- Introduction, Application and Usage
- Basic Structured Query Language
- CRUID operations
Basic Data Visualization
- Principles of Information Visualization
- Visualizing Data Using Spreadsheets
- Matplotlib
- Plotly
- Scatterplots
- Line Plots
- Bar Charts
- Histograms
- Plotting with Pandas
Classes and objects
- User-defined types
- Attributes
- Real World Example
- Instances as return values
- Objects are mutable
- Copying
- Debugging
Classes and methods
- Object-oriented features
- The self
- Printing objects
- The init method
- The __str__ method
- Other special methods
- Operator overloading
- Type-based dispatch
- Polymorphism
- @staticmethod
- Debugging
Callable and Non Callable Object
- Introduction
- Checking callable or not
- Decorators
- Creating and using decorators
Inheritance
- Introduction
- Example
- Class attributes
- Private, Protected and Public
- Multiple Inheritance
- Class diagrams
- Debugging
- Data encapsulation
GIT
- 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
Tools
- Trello, Slack, Jira
Final Project
- Web Scraping project (includes handling web scraping tools, proper file handling and implementation of sql)
- GUI project (any desktop application e.g: calculator, 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