Python for Data Science


Python is a general-purpose programming language that is rich in the library and moreover, it is open source. The popularity of Python is increasing day by day because of its community and library. Python can be used for various purposes like web development, machine learning, artificial intelligence along with data science. Students who have an interest in data analysis and data science can learn Python along with its popular libraries like Numpy, Pandas, and Matplotlib.

The course is designed in such a way that even a beginner can start learning Python for Data Science. However, one should first learn Python's fundamental programming concepts. At first, we teach fundamental programming concepts in Python for about a month and then we start Python for data science.

The objective of the training Python for Data Science

The objective of Python for Data Science Training is to equip learners with the skills and knowledge required to use Python for data science. The training covers a wide range of topics, including data visualization, data manipulation, machine learning, and statistical analysis. The training is designed to be hands-on, with learners working on real-world data science projects to apply their skills and knowledge. Some of the objectives of learning Python for Data Science are mentioned below:

  1. Data Manipulation: Using library like pandas you can clean, transform, and analyze data efficiently.
  2. Data Visualization: Libraries like matplotlib and seaborn can be used to create meaningful visualizations that help in understanding the data.
  3. Statistical Analysis: Applying statistical methods and hypothesis testing to derive insights from data.
  4. Machine Learning: Understanding and implementing machine learning algorithms using libraries like scikit-learn to build predictive models.
  5. Big Data Processing: Learning frameworks like PySpark for processing and analyzing large datasets.
  6. Data Wrangling: Gathering, cleaning, and transforming data from different sources into a usable format.
  7. Data Mining: Extracting patterns and knowledge from large datasets using techniques from statistics and machine learning.
  8. Predictive Modeling: Building models that can make predictions based on historical data.
  9. Deployment: Understanding how to deploy data science models into production environments.
  10. Collaboration: Working with others in a team setting, often using version control systems like Git.

Benefits of learning Python for Data Science at IT Training Nepal

  1. Versatile Skill Set: If you are planning to work in the area of Data Science then Python being a versatile programming language will be widely used in data science for its simplicity and readability, making it an essential skill for data professionals.
  2. Industry-Relevant Curriculum: The course at IT Training Nepal is designed to teach Python specifically for data science, focusing on the tools and techniques used in real-world scenarios.
  3. Hands-On Experience: Students get hands-on experience with Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn, which are commonly used in data science projects.
  4. Career Opportunities: Python is in high demand in the data science job market, and learning it can open up numerous career opportunities in various industries.
  5. Networking Opportunities: IT Training Nepal provides networking opportunities with industry professionals and fellow students, which can be valuable for future career prospects.
  6. Practical Projects: The course includes practical projects where students apply their Python skills to analyze data sets, derive insights, and build predictive models, providing valuable experience.
  7. Professional Guidance: Students receive guidance from experienced instructors who can help them navigate the complexities of Python and data science.
  8. Course Completion Certificate: Upon completion of the course, students receive a certification from IT Training Nepal, which can enhance their credibility in the job market.

Who can learn Python for Data Science?

Python for Data Science Training is suitable for anyone who wants to learn how to use Python for data science. This includes students, working professionals, and anyone who is interested in exploring the field of data science. No prior programming experience is required, as the training covers the Python programming Course in the beginning and gradually builds up to advanced topics in data science. However, if you want to have a good understanding of the course that its advised to have the following knowledge before starting the course.

  • Fundamental programming concepts
  • Basic understanding of mathematics
  • Familiarity with concepts like hypothesis testing, p-values, confidence intervals, and probability distributions in statistics
  • Basic understanding of data manipulation technique like sorting, filtering, grouping along with data format like CSV, Excel, database.

Duration: 2.5 Months

Syllabus Expand All
  • 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 


  • Integer
  • Float
  • Complex
  • String
  • Sequences
  • Mapping
  • Boolean
  • Set
  • Arithmetic Operators
  • Comparison Operators
  • Assignment Operators
  • Bitwise Operators
  • Logical Operators
  • Membership Operator (in, not in)
  • Identity Operators (is, is not)
  • Operators Precedence
  • if statements
  • ..else statements
  • Nested if statements
  • While loop
  • For loop
  • Nested loop
  • Loop Control Statements
  • Break Statement
  • Continue Statement
  • Pass Statement
  • 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
  • Definition and need of function
  • Function Call
  • Anonymous Function 
  • Arguments
  • Call Functions with different types of Arguments
  • Return Statement 
  • Class and objects
  • Private Identifier
  • Constructor
  • Inheritance
  • Polymorphism
  • Local Scope
  •  Non local Scope
  •  Global Scope
  • Saving memory with generators
  • Generator expressions
  • Generator functions
  • Generator classes
  • Stacking generators
  • Split
  • Working with special characters, date, emails
  • Quantifiers
  • Match and find all
  • Character sequence and substitute
  • Search method
  • Creating and configuration a github account
  • Initializing a Git Repo
  • Branching
  • Committing change
  • Adding a Remote
  • Pushing Changes
  • Cloning
  • Pickle
  • JSON
  • CSV
  • XML
  • Web data sources
  • Data via URL
  • RESTful data
  • Screen-scraping
  • The xlrd, xlwr, and xluti modules
  • Reading an existing spreadsheet
  • Creating a spreadsheet from scratch
  • Modifying an existing spreadsheet
  • Sorting data filtering values
  • Basic statistics
  • Leveraging NumPy
  • Using Pandas
  • NumPy Basic
  • Creating arrays
  • Indexing and slicing
  • A large number of sets
  • Transforming data
  • Advance tricks
  • Pandas overviews
  • Data frames
  • Reading data
  • Writing data
  • Data alignment 
  • Reshaping
  • Fancy indexing 
  • Slicing
  • Merging data sets 
  • Joining data sets
  • 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
  • PIL overview
  • Create image library
  • Image processing
  • Displaying images
  • Basic arithmetic
  • Simplification and expansion
  • Functions
  • Polynomials
  • Solving equations
  • Geometry

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