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Data Science with Python Training In Nepal Data Science with Python Training In Nepal
  • Schedule Black Duration 3 Months
  • Work outline black Career career

Introduction

Data science is the domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions. Data science uses complex machine learning algorithms to build predictive models.

The most appropriate term we currently have for the multidisciplinary collection of abilities that are becoming more and more crucial in numerous applications in both industry and academics is possibly "data science."

At its foundation, data science is about gathering, examining, and drawing conclusions from data. Python has a number of strong libraries and tools that make data collecting more effective. This article will walk you through the steps of using Python to collect data, going through important libraries and offering sample code for various situations.

Data Science with Python training in Nepal is designed to provide participants with a strong foundation in Python programming, covering essential concepts such as variables, data types, and control structures through hands-on exercises. The course delves into data manipulation and analysis using the Pandas library, guiding participants in cleaning, transforming, and analyzing datasets with a focus on real-world challenges. Additionally, participants will explore data visualization techniques using Matplotlib and Seaborn, enhancing their ability to communicate insights effectively through charts and graphs.

Data Science with Python training has plenty of advantages as it is used in so many industries of applications such as banking and financial sector, healthcare, social media, publishing and retail, publishing, etc. Due to machine learning, there are tools available to provide continuous quality improvement in complex and large process environments. It also helps in efficient utilization of resources and reducing time cycle.

Python is widely used in data science because it has many benefits that are just right for what data scientists need. It's easy to understand and read, making it a good choice for both beginners and experienced data scientists. Python has a bunch of helpful tools for working with data, like Pandas, NumPy, and scikit-learn. These tools are great for organizing data and doing analysis or machine learning. What's cool about Python is that it's not just a tool; it has a big group of people who use it and share ideas. This community helps each other solve problems and learn new things. Also, Python is free for everyone to use, which encourages people to come up with new ideas and share them with the whole data science community. 



    WHY PYTHON FOR DATA SCIENCE TRAINING IN NEPAL?

    • Python is widely used in the global and local data science industry, ensuring relevance and alignment with real-world applications.
    • Python's versatility allows data scientists in Nepal to tackle a broad spectrum of tasks, from data cleaning to advanced machine learning.  Python boasts powerful libraries like Pandas, NumPy, and scikit-learn, offering robust tools for efficient data manipulation, analysis, and machine learning.
    • Python boasts powerful libraries like Pandas, NumPy, and scikit-learn, offering robust tools for efficient data manipulation, analysis, and machine learning.
    • Python's simple and readable syntax makes it accessible for beginners, facilitating a smoother learning curve for aspiring data scientists in Nepal.
    • A vibrant Python community provides support, resources, and collaborative opportunities, enhancing the learning experience for individuals in Nepal.
    • Python being open source allows cost-effective training, promoting accessibility and innovation within the Nepalese data science community.
    • Python seamlessly integrates with big data technologies like Apache Spark, vital for handling large-scale datasets, a common feature in Nepal's evolving data landscape.
    • Training with Python emphasizes hands-on projects, enabling participants to apply theoretical knowledge to real-world scenarios, reinforcing practical skills.
    • Proficiency in Python for data science opens doors to diverse career opportunities in Nepal, aligning with the growing demand for skilled data professionals.
    • Python is globally recognized and used by major tech companies. Training in Python for data science in Nepal aligns individuals with international standards and practices, enhancing their global employability.


Course overview

Data science with Python can help you build your career in Nepal as a data scientist or analyst by giving you the skills you need to be successful in the IT industry. You'll learn how to create predictive models using Python, analyze data sets and perform statistical analysis on them, and even learn how to work with large amounts of data through machine learning techniques like neural networks.

Python is a popular programming language and it's also a great tool for data science. Python is becoming more and more popular in the field of data science because of its speed, versatility, and power. If you are looking to learn Python for data science and start your career in this field

"Data Science with Python" course is your one-stop resource for mastering Python's essential role in the data-driven domains.

Features:

Beginner-Friendly Python Tutorials: Whether you're a novice or an experienced programmer, we offer a wide range of tutorials that cater to all skill levels. These tutorials will help you build a strong foundation in Python programming. Hands-On Examples of Data Manipulation and Visualization: Data is at the heart of data science. We provide hands-on examples to teach you how to manipulate and visualize data effectively using Python. You'll learn how to transform raw data into actionable insights.

Hands-On Examples of Data Manipulation and Visualization: Data is at the heart of data science. We provide hands-on examples to teach you how to manipulate and visualize data effectively using Python. You'll learn how to transform raw data into actionable insights.

Machine Learning Projects: Dive into the fascinating world of machine learning with our projects. These cover regression, classification, and clustering, allowing you to understand and apply predictive modeling and pattern recognition techniques.

Exploratory Data Analysis Techniques for Insights: Data exploration is the key to discovering hidden patterns and trends. Our repository offers techniques for thorough exploratory data analysis, enabling you to extract valuable insights from your datasets. Notebooks Showcasing Popular Libraries: We've curated notebooks that showcase

Notebooks Showcasing Popular Libraries: We've curated notebooks that showcase popular Python libraries, including Pandas, Matplotlib, Seaborn, and Scikit-Learn. These libraries are the building blocks of data science, and you'll learn how to leverage them effectively in your projects.

Exploring Nepali Scenario Datasets: In addition to the above, we will dive into Nepali scenario datasets, allowing you to apply your data science skills to real-world data from the Nepali context. This offers a unique opportunity to work with data relevant to Nepal and gain insights that can make a difference in the local context.

Why Python for Data Science training is important in today’s context?

1. Versatility and Simplicity:

Python's versatility allows it to seamlessly adapt to a wide range of data science tasks, from data cleaning to complex machine learning algorithms. Its simple and readable syntax facilitates efficient coding, making it accessible for beginners and proficient for experienced practitioners.

2. Extensive Ecosystem of Libraries:

Python boasts a rich collection of libraries and frameworks specifically designed for data science, such as Pandas, NumPy, and scikit-learn. This vast ecosystem provides ready-to-use tools for tasks like data manipulation, statistical analysis, and machine learning, streamlining the data science workflow.

3. Community Support and Collaboration:

The Python data science community is vibrant and collaborative, fostering knowledge-sharing, problem-solving, and continuous learning. The collective expertise of the community ensures that data scientists have access to valuable resources, support, and innovative solutions.

4. Open Source and Accessibility:

Python is an open-source language, meaning that it is freely available for anyone to use, modify, and distribute. This fosters a culture of collaboration and innovation, making the latest advancements in data science accessible to a broad audience.

5. Ease of Integration:

Python seamlessly integrates with various data-related technologies, databases, and visualization tools. This ease of integration enables data scientists to create end-to-end solutions, ensuring a smooth workflow from data extraction to insights presentation.

6. Rapid Prototyping and Experimentation:

Python's dynamic typing and interpreted nature make it conducive to rapid prototyping and experimentation. Data scientists can quickly test hypotheses, build models, and iterate on their solutions, facilitating a dynamic and agile approach to problem-solving.

7. Compatibility with Big Data Technologies:

Python interfaces well with big data technologies such as Apache Spark and Hadoop. This compatibility is crucial in handling large-scale datasets and performing distributed computing, which are increasingly common in today's data-intensive environments.

8. Real-world Applications and Industry Adoption:

Python is widely used in various industries for practical applications, including finance, healthcare, marketing, and more. Its popularity in real-world scenarios reinforces its importance as a tool for solving complex problems and extracting meaningful insights.

9. Machine Learning and Deep Learning Capabilities:

Python is at the forefront of the machine learning and deep learning revolution. Libraries like scikit-learn, TensorFlow, and PyTorch provide powerful tools for building and deploying sophisticated models, aligning with the growing demand for advanced analytics and artificial intelligence.

Careers in Python for Data Science

According to a study done in recent years, Python was used for data analysis by 83% of data professionals. Data Scientists who are ideally the jack of all trades with the knowledge of Mathematics/Statistics, programming skills, and industry knowledge are the highest-paid professionals. The scope of data science in Nepal doesn’t only limit to these careers, among multiple career opportunities, few of them are

• Data Analyst

• Business Analyst

• Machine Learning Scientist

• Data Engineer

• Business Intelligence Analyst

• Statistician

• Data Mining Specialist

• Data Architect

• Data Scientist

These specialists are in high demand across practically all industries, including those in governmental institutions and big tech giants. Consequently, we may claim that Python is the best language for data science.                         

Teaching Methodology

Handful of assignments, tutorials and lab test of each chapter.

• Periodic feedback from Trainer and Trainee and do the required changes as per necessity.

• Each trainee need to develop demo application on their own, taking assistance form trainer when ever required

Prerequisites

• Good command in english language

• Good knowledge of Computer, Softwares

• Basic Knowledge programming language like C/C++ would be a plus

• Understanding of Softwares and Software installation


Course content

courses | 3 Months

Introduction to Data Science

  • What is data science and why is it so important?
  • Applications of data science
  • Various data science tools
  • Data Science project methodology
  • Tool of choice-Python: what & why?
  • Case study


Introduction to Python
  •  Variables  Datatypes
  •  Datatypes
  •  Basic Operations
  •  Control Flow
  •  Functions.
  •  Data Structures
  •  Error Handling
  •  Additional Python Concepts


Intermediate Python

  •  List comprehensions and Generators
  •  Object-Oriented Programming (OOP).
  •  Exception Handling.
  •  Decorators and Context Managers
  •  Regular Expressions..
  •  Advanced Data Structures
  •  Functional Programming


Advanced Python

  •  Advanced OOP Concepts
  •  Design
  •  Performance Optimization
  •  Advanced Modules and Libraries


Data Manipulation with Pandas

  •  Introduction to Pandas
  •  Data Loading and Cleaning.
  •  Data Exploration
  •  Indexing and Selection
  •  Data Transformation
  •  Data Aggregation and Grouping
  •  Time Series and Handling Dates..
  •  Categorical


Data Visualization with Matplotlib and Plotly

  •  Advanced Plot Customization
  •  3D and Geospatial Visualization  
  •  Interactive Visualization with Plotly
  •  Subplots and Multiple.
  •  Animation and Dynamic Visualization
  •  Advanced Plot Types


Numpy and Matplotlib

  •  Introduction to NumPy
  •  Array Creation and Manipulation
  •  Universal Functions
  •  Array Operations
  •  Indexing
  •  Advanced Array Operations
  •  Linear Algebra with NumPy
  •  Random Number Generation
  •  Data Visualization with Matplotlib


Linear Regression

  •  Simple Linear Regression
  •  Multiple Linear Regression
  •  Model Evaluation and Validation


Logistic Regression

  •  Introduction to Logistic Regression
  •  Logistic Regression Model.
  •  Model Evaluation for Classification


Decision Tree


  •  Introduction to Decision Trees
  •  Decision Tree Construction.
  •  Handling Categorical and Numerical Features.
  •  Decision Tree Visualization and Interpretability


Random Forest

  •  Introduction to Random Forest
  •  Random Forest Construction
  •  Feature Importance.
  •  Advantages and Limitations


Support Vector Machine(SVM)

  •  Introduction to Support Vector Machines.
  •  Linear SVM for Classification.
  •  Kernel Methods
  •  SVM for Regression
  •  Support Vector Machine Applications


Principal Component Analysis(PCA)


  •  Introduction to PCA
  •  PCA Algorithm
  •  Visualization and Interpretation
  •  Image Compression


Neural Networks

  •  Neural Network Basics
  •  Neural Network Architectures.
  •  Model Training and Optimization.
  •  Deep Learning Frameworks.
  •  Neural Network Applications


Recurrent Neural Networks

  •  Introduction to RNNs
  •  Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).
  •  Applications of RNNs
  •  Training and Optimization


Web Scraping

  •  Web Scraping Techniques
  •  Data Extraction from Websites.
  •  Data Processing and Storage


Project Presentation

  •  Project Showcasing
  •  Visualization and Explanation.
  •  Practical Examples.
  •  Q&A Session
  •  Feedback and Reflection
  •  Career and Future Steps.