Python with Data Science Course

overview

A Python with Data Science course in Nepal could be one of the most rewarding investments that a professional working with data can make in today’s market. The skills learned in this course can be used in a variety of scientific and managerial scenarios. Whether you’re working with market information or data collected from the stars in the night sky, Python with Data Science can help you clean up the collected data and translate it into meaningful information. Hence, it is an established fact that Python is the most popular programming language among data scientists. Let us explore why.

In Data Science, there are four stages of problem-solving, and they are:

• Data collection & cleaning
• Data exploration
• Data modeling
• Data visualization

According to Flatiron School, the tools accessible through Python can assist any data scientist in all 4 of these stages.

While these concepts can sound complicated to many people, a Python with Data Science course in Nepal would get you started from the absolute basics. Someone with little or no experience in programming could also sign up for these courses. The first month of training would be exclusively dedicated to getting one started on the basics of the programming language and the remaining two-thirds of the course will be dedicated to teaching you to work with data in Python.

Some academic programs in Nepal, like IT degrees, engineering degrees, and master’s level business degrees also include aspects of the Python with Data Science course in their curriculum.
 

Program Structure

As previously stated, a Python with Data Science course in Nepal usually takes 3 months. However, the duration is an average estimation and some institutions can have a course that is longer or shorter than 3 months, depending on the depth of knowledge and the model of delivery that the course was designed for.

During this time, you should at least be covering the following aspects of Python with Data Science:

• Data Manipulation: You should be instructed on how you can prepare the datasets available to you and transform them if required to later be analyzed.
• Statistical analysis: You should be taught about how you can employ different statistical operations on the datasets to test hypotheses and deduct meaningful inferences to be later used in decision-making.
• Data visualization: You should be instructed on how to make visual representations of your data and statistical findings such that the information can be assessed in an easier way.
• Machine Learning: You should be able to understand and implement machine learning models to build predictive models that can make projections on the given dataset based on historical data.
• Big Data Processing: You should also be introduced to tools like PySpark that would help you process and analyze otherwise large sets of data.
• Data Mining: You should be adept at using machine learning models to extract patterns and usable information to be used for later analysis.
• Model Deployment: You should be mentored in deploying the data analysis model you’ve developed into a production environment in a professional capacity.

These seven skills largely cover the scope of what you’ll be studying as a student of Python with Data Science Course in Nepal. As for what exact modules you will be working with, please check the aggregated syllabus below:

Introduction

• Python installation
• Interpreters and Compilers
• Latest Version and package manager
• Working with Python Shell
• Integrated Development Environments ( Pycharm, Jupyter, Notebook)
• Object Oriented programming
• Naming Convention

Basic Concepts of Python

• Data types
• expand_less
• Integer
• Float
• Complex
• String
• Sequences
• Mapping
• Boolean
• Set

Operator

• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Bitwise Operators
• Logical Operators
• Membership Operator (in, not in)
• Identity Operators (is, is not)
• Operators Precedence

Loops and Decision Making

• if statements
• ..else statements
• Nested if statements
• While loop
• For loop
• Nested loop
• Loop Control Statements
• Break Statement
• Continue Statement
• Pass Statement

Data Structures

• List
• Accessing Value in Lists
• Updating Lists Element
• Deleting Lists Elements
• Indexing
• Slicing
• Matrixes
• Built in Functions and methods
• Tuples
• Accessing Value in Tuples
• Updating Tuples
• Delete Tuple Elements
• Basic Tuples Operations
• No enclosing Delimiters
• Built-in Tuple Functions
• Dictionaries
• Properties of Dictionary keys
• Accessing Value in Dictionary
• Updating Tuples
• Nested Dictionaries
• Built in Dictionary Function and methods
• Set
• Create a set
• Add Set items
• Update Set items
• Set Operation: Union, intersection, Difference
• Set method: isdisjoint(), issuperset(), symmetric_difference()
• Built-in Functions with Set

Function

• Definition and need of function
• Function Call
• Anonymous Function
• Arguments
• Call Functions with different types of Arguments
• Return Statement

OOPs Concept

• Class and objects
• Private Identifier
• Constructor
• Inheritance
• Polymorphism

Variable Scope

• Local Scope
• Non local Scope
• Global Scope

Generators

• Saving memory with generators
• Generator expressions
• Generator functions
• Generator classes
• Stacking generators

Regular Expression

• Split
• Working with special characters, date, emails
• Quantifiers
• Match and find all
• Character sequence and substitute
• Search method

Git and Github

• Creating and configuration a github account
• Initializing a Git Repo
• Branching
• Committing change
• Adding a Remote
• Pushing Changes
• Cloning

Serializing Data

• Pickle
• JSON
• CSV
• XML

Consuming Data from the Web

• Web data sources
• Data via URL
• RESTful data
• Screen-scraping

Excel Spreadsheet

• The xlrd, xlwr, and xluti modules
• Reading an existing spreadsheet
• Creating a spreadsheet from scratch
• Modifying an existing spreadsheet

Analyzing Datasets

• Sorting data filtering values
• Basic statistics
• Leveraging NumPy
• Using Pandas

Numpy

• NumPy Basic
• Creating arrays
• Indexing and slicing
• A large number of sets
• Transforming data
• Advance tricks

PANDAS

• Pandas overviews
• Data frames
• Reading data
• Writing data
• Data alignment
• Reshaping
• Fancy indexing
• Slicing
• Merging data sets
• Joining data sets

matplotlib

• Creating a basic plot
• Commonly use plots
• Scatter plotting
• Heat maps
• Bubble Charts
• Bar Charts
• Pie Charts
• Box and Whisker plots
• Time series plot
• Line Graph
• Geographical data
• Advance uses
• Exporting images

THE PYTHON IMAGE LIBRARY (PIL)

• PIL overview
• Create image library
• Image processing
• Displaying images

SYMPY

• Basic arithmetic
• Simplification and expansion
• Functions
• Polynomials
• Solving equations
• Geometry

Objectives

• To provide a foundational understanding of Python programming tailored to data science applications.
• To teach students how to clean and manipulate datasets, preparing them for in-depth analysis.
• To equip learners with the ability to perform statistical analyses on datasets to derive meaningful insights and make informed decisions.
• To train students in data visualization techniques, enabling them to present data in a clear and accessible manner.
• To introduce machine learning concepts and their implementation in Python, allowing students to create predictive models.
• To familiarize students with big data processing tools like PySpark, preparing them to handle and analyze large-scale datasets.
• To guide students through the deployment of data models in professional environments, ensuring they can apply their skills in real-world scenarios.
• To develop a comprehensive skill set in data mining and pattern recognition, essential for extracting valuable information from complex datasets.
   

Carrer Prospects

At any given point in time, there are hundreds of job postings on LinkedIn for people with Python with Data Science backgrounds in Nepal. There are thousands of remote jobs posted from companies across the globe for the same, where even if you’re based in Nepal, you can work and earn at international rates. You can also be employed full-time with a company or work on a project-to-project basis.

As a Python with Data Science professional, you will have endless opportunities both here in Nepal, and abroad.

It is also important to note that this course is more of a gateway. The responsibility of putting your learnings into practice, keeping yourself updated with the technology, and expanding your horizons with specialties is on you: the student. However, should you choose to do that, these might be some attractive positions that you could apply for:

• Junior Data Analyst
• Junior Data Engineer
• Business Data Analyst
• Risk Management Analyst
• Market Research Analyst

Prerequisite

Being beginner-friendly is a core feature of what makes Python so popular. Although approachable for beginners, there are a few things one needs to build upon before starting a Python with Data Science Course in Nepal. The idea is, that you’ll have an easier time understanding what you’ll be taught in your classes if you have a good understanding of the basics.

Let’s start with the foundations of the course: Python.

For one to learn Python, there isn’t much you need to know. You just have to be adept at basic computer operations, like understanding your operating systems, installing/uninstalling programs, troubleshooting, working with browsers, etc. If you want to learn app development and web development, you can build a foundation in scripts like HTML, CSS, and JavaScript. However, for the purposes of this course, you don’t have to have that background.

What you need to focus on more is the mathematical aspect of things, more specifically statistics. Because you’re going to be working with large sets of data, manipulating it, running analysis in it, and so on, you need to have a solid grasp on the kinds of details available, the kinds of hypotheses one can hold, and the operations one can do to run the analysis. A foundational understanding of concepts like hypothesis testing, p-values, confidence intervals, and probability distributions in statistics is a must when learning Python with Data Science Course in Nepal.

Again, it is possible to learn these statistical concepts as you do the course. However, if both Python and statistics are new to you, you will have much more trouble mastering the Python with Data Science course.
 

Self Learning Tips

As previously stated, Python is a popular programming language, and not just among data scientists. Hence, there is a huge community of Python developers working in all sorts of spaces, from data sciences to web development. Python Software Foundation, the creators of this open-source programming language, have many tutorials and community posts that a beginner can utilize to their heart’s content. Apart from that, there are communities on Discord, Stack Overflow, Reddit, GitHub, etc. where one can learn from the wisdom of the programmers who came before them. Of course, you can also make use of the resources on YouTube.

If you find yourself asking, “Is Python with Data Science Course in Nepal free?” Most of the resources mentioned above can be used without a cost.

Now, you will have to pull out your wallets if you want a more hands-on mentorship. You can find a bunch of courses on the internet—some from reputed universities, others from experienced professionals looking to further monetize their skills, & others still from online learning platforms.
 

Conclusion

If you are someone who needs to actively interact with your tutor and learn Python with Data Science in the physical presence of a teacher, there are any number of institutions that offer this course in different parts of Nepal.

A Python with Data Science Course in Nepal would cost you anywhere from NPR Twenty thousand to Thirty thousand for a 2.5 to 3-month period. However, it is recommended that even learning with in-person tutors, you make use of the online community and resources made available to you by the programmers before you.

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