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Certifications
    Data Science Advance

    Python for Data Science

    The Python for Data Science course is designed to equip learners with the programming skills and analytical techniques needed to work effectively with data. Python has become the leading language for data science because of its simplicity, versatility, and powerful ecosystem of libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn.

    This course introduces learners to the fundamentals of Python programming before moving into data manipulation, visualization, and machine learning applications. Through hands-on exercises and real-world projects, participants gain practical experience in cleaning datasets, analyzing trends, building models, and communicating insights with clarity.

    4.0
    By Namifly
    Last updated: September 2025
    Python for Data Science

    $799

    One-time payment

    30-day money-back guarantee
    This course includes:
    • Boost Your Skills, Elevate Your Career!
    • Your Growth Starts Here!
    • Turn Knowledge into Action!
    • Learn Fast, Earn Faster!

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    Complete Assured Package

    $799.00

    Course Description

    The Python for Data Science Course is a comprehensive learning program designed to equip aspiring data professionals, analysts, and programmers with the skills needed to harness the power of Python for extracting meaningful insights from data. Python has become the de facto language of data science due to its simplicity, versatility, and an extensive ecosystem of libraries, making it the ideal starting point for anyone wishing to pursue a career in this domain.

    This course begins by establishing a solid foundation in Python programming, ensuring learners gain a strong grasp of core concepts such as data types, control structures, functions, file handling, and object-oriented programming. From there, participants are gradually introduced to essential tools and packages used in data analysis, including NumPy for numerical computing, Pandas for data manipulation, and Matplotlib/Seaborn for visualization. Learners will practice working with real-world datasets, mastering techniques to clean, transform, and prepare data for meaningful analysis.

    A major emphasis of the course lies in data exploration and visualization, where students learn to uncover trends, correlations, and outliers using intuitive plots and charts. As the program advances, participants dive into more advanced topics such as statistical analysis, hypothesis testing, and machine learning basics with scikit-learn. By working on predictive models, classification, regression, and clustering problems, students build the confidence to solve complex, data-driven challenges.

    The course is highly practical in nature, combining theoretical knowledge with hands-on exercises, coding assignments, and end-to-end projects that simulate real business and research scenarios. Whether analyzing sales data, building recommendation systems, or predicting customer churn, learners gain exposure to diverse applications of data science. Additionally, the course introduces learners to SQL integration, APIs, and cloud-based tools to ensure they are industry-ready.

    By the end of the program, participants will have acquired not only a strong command of Python but also the analytical thinking, problem-solving, and visualization skills essential to succeed in the field of data science. This course is suitable for beginners with no prior coding experience as well as professionals from non-technical backgrounds who wish to transition into data-focused roles.

    Key Highlights:

    Beginner-friendly Python programming foundation

    Extensive training in data manipulation, cleaning, and visualization

    Real-world datasets and case studies

    Hands-on projects with industry relevance

    Introduction to machine learning with Python

    Guidance on tools like Jupyter Notebook, Git, and cloud platforms

    Career-focused skills for Data Analyst, Data Scientist, and AI roles

     

    Course Curriculum

    Expand the sections below to see the detailed curriculum for this course.

      • Python Introduction
      • Python for Data Science
      • Installation and Setup
      • Hello World

      • Introduction to Code and Data
      • What is Code and Data?
      • Creating Data
      • Using Data with Code
      • Syntax and Naming Conventions

      • Introduction to Building Blocks
      • Data Types
      • Arithmetic Operators
      • Lists Basics
      • Important Functions and Features

      • Introduction to Data Structures
      • Data Structures
      • Lists
      • List Functions - Part 1
      • List Functions - Part 2
      • Tuples
      • Sets

      • Introduction to NumPy
      • What is NumPy?
      • Creating & using NumPy Arrays
      • NumPy Array Attributes
      • Array Indexing and Slicing
      • Array Operations - Part 1
      • Array Operations - Part 2
      • Different Ways of Creating Arrays
      • Random Number Generation

      • Visualization Overview
      • Introduction to visualization
      • Basic plots
      • Sub plots
      • Bar, Pie, Histogram and Scatter
      • Plots using Pandas
    Parves

    4.9 Instructor Rating

    Parves

    15+ Years

    13 Years
    Experience
    50000+
    Students
    25
    Courses
    4.9
    Rating

    Our expert instructor brings years of industry experience and a passion for teaching. With a proven track record of helping students achieve their career goals, they provide practical insights and real-world knowledge that you can apply immediately. Their expertise spans multiple domains and they stay current with the latest industry trends and technologies to ensure you receive the most relevant and up-to-date training.

    4.0

    Course Rating

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    Frequently Asked Questions

    Find answers to common questions about this course.

    Python is popular because it’s easy to learn, has a huge ecosystem of libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch, etc.), and strong community support. It balances readability with performance, making it ideal for both prototyping and production.

    No. Python is beginner-friendly, and many people start their programming journey with data science. However, understanding basic programming concepts (loops, functions, variables) helps speed things up.

    NumPy – numerical computing, arrays pandas – data manipulation and analysis Matplotlib & Seaborn – data visualization scikit-learn – machine learning Statsmodels – statistical analysis TensorFlow / PyTorch – deep learning

    Python: General-purpose, integrates easily into production, better for machine learning and deep learning. R: Strong in statistical modeling, built-in visualization, often used in academia and research. Many data scientists learn both, but Python dominates in industry

    Yes, but not at a PhD level. Core areas: Statistics – distributions, probability, hypothesis testing Linear Algebra – vectors, matrices Calculus – optimization, derivatives (mainly for ML) Logic & Problem-Solving – for writing efficient code

    NumPy array: Efficient container for numerical operations. pandas DataFrame: Built on NumPy, adds labeled rows and columns, ideal for tabular data.

    Learn Python basics (variables, loops, functions). Master NumPy and pandas. Learn visualization (Matplotlib, Seaborn). Practice with real datasets. Study statistics and machine learning with scikit-learn. Explore deep learning if needed. Build projects and share on GitHub/Kaggle.

    Data Analyst Data Scientist Machine Learning Engineer Business Intelligence Analyst Research Scientist AI Engineer

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