Select Page
Python Programming

Introduction to Data Science Course

Learn Data Science Basics from the Experts with our Online Course

No credit card required. No obligation to purchase

Introduction to Data Science Online Course 

As the world becomes more data-driven, data science has become an essential skill for students to learn. The Introduction to Data Science Course is designed for school-level students who want to gain a solid understanding of the basics of data science.

Our course covers key concepts such as data analysis, data visualization, and machine learning, and our experienced tutors provide personalized instruction to help students understand these concepts in a way that is accessible and engaging. We use real-world examples and projects to make the course relevant to students’ lives, and we provide plenty of opportunities for hands-on practice so that students can build their skills and confidence.

Whether your students are interested in pursuing a career in data science or simply want to gain a better understanding of this important field, our Introduction to Data Science Course is an excellent choice. With our comprehensive curriculum and experienced tutors, students can develop the skills they need to excel in data science and beyond.

Introduction to Data Science – Course Description

The course is designed to build upon students’ existing knowledge of Python programming and provide an introduction to the core concepts and techniques used in data science. This online course will begin by checking the student’s understanding of Python on topics like operators, functions, and libraries, and will then move on to cover a range of data science topics.

The course will start with an overview of data and its types, followed by an introduction to data science and its applications. Students will explore the ethical and practical challenges associated with data science, including issues of privacy, bias, and ethics. They will also learn about the role of statistics and probability in data science, including important formulas for understanding data distributions and probabilities.

The course will then move on to exploring different types of data, including relational and non-relational data, and will provide an introduction to data exploration using Pandas, Matplotlib, and NumPy. Students will also learn about Jupyter Lab Notebooks and how to write and execute code using this powerful tool.

Students will have an opportunity to apply their learning by picking a dataset and understanding all the techniques covered in the course with coding in Google Colab. They will also learn about the lifecycle of data science, from defining the problem to deploying a solution.

Finally, the course will introduce students to machine learning and how it can be used to build predictive models using data science techniques. Students will learn how to build a training model for machine learning using real-world datasets.

By the end of this course, students will have a solid understanding of Python programming and the fundamentals of data science. They will have hands-on experience with a range of data science tools and techniques and will be able to apply these skills to real-world data science problems. They will also be able to understand and implement machine learning algorithms using Python.

Introduction to Data Science Online Course

GRADES – 6 to 12

Understanding of Python Language
  • Checking the understanding of student about python on topics like operators, functions and libraries
  • Introduction to Data Science
  • Data and its types, defination of Data Science and Applications
  • Data Science Ethics and Challenges
  • Covering Statistics and Probability
  • Role of Statistics and Probability in Data Science and Important Formulae
  • Exploring and Finding Data
  • Relational Data and its exploring, Non Relational Data and its exploring
  • Python Libraries for Data Science
  • Learning about Pandas, Matplotlib, NumPy
  • Introduction to platform for Data Science Project
  • Getting Introduced to Jupyter Lab Notebooks and how to write and execute code
  • Data Cleaning, Exploring and Visualization Techniques
  • Picking a dataset to understand all the techniques with coding in google colab
  • Lifecycle of Data Science Process
  • Knitting whatever learnt together in lifecycle that explains Data Science Process
  • Understanding of Python language
  • Building a training model for Machine Learning using Data Science
  • $529

    Why Should You Learn Data Science?

    In today’s world, data is everywhere. Every time you use a mobile phone, browse the internet, or buy something online, you generate data. Companies use this data to understand your behavior, preferences, and needs. Governments use data to make policies and decisions. Scientists use data to make discoveries and develop new technologies. Data is changing the way we live, work, and interact with the world around us.

    This is where data science comes in. Data science is the field of study that deals with extracting insights and knowledge from data. It is a combination of statistics, mathematics, programming, and domain expertise. Data scientists use tools and techniques to explore, clean, visualize, and analyze data. They then use this information to make predictions, identify patterns, and solve real-world problems.

    Learning data science can be beneficial for many reasons. First, it is a highly sought-after skill in today’s job market. Many companies are looking for people who can help them make sense of their data. Learning data science can help you stand out from other candidates and increase your chances of getting hired.

    Second, data science is a field that is constantly evolving. There are always new tools, techniques, and technologies being developed that make data science more efficient and effective. By learning data science, you can stay up-to-date with the latest trends and developments in the field.

    Third, data science can help you make better decisions. By analyzing data, you can gain insights and knowledge that can help you make informed decisions. For example, you can use data to predict future trends, identify potential risks, or improve business processes.

    Finally, data science can be a fun and rewarding field to work in. It allows you to work with data from a variety of sources and domains, and to use your creativity and problem-solving skills to solve real-world problems.

    Benefits of Enrolling in a Data Science Course with eTutorWorld

    • Experienced and Qualified Tutors: eTutorWorld has a team of experienced and qualified tutors who are experts in the field of data science. They have a deep understanding of the subject and are committed to helping students learn and succeed.
    • Personalized Learning: The data science course with eTutorWorld is tailored to meet the specific needs and learning styles of each student. Tutors work with students one-on-one to identify areas of strength and weakness and to create a personalized learning plan that helps them achieve their goals.
    • Flexible Schedule: eTutorWorld offers flexible scheduling options that allow students to learn at their own pace and on their own schedule. This means that students can fit their data science studies around their other commitments and responsibilities.
    • Interactive Learning: eTutorWorld uses interactive tools and resources to engage students in the learning process. This includes online simulations, videos, and interactive exercises that help students understand key concepts and apply them to real-world problems.
    • Affordable Pricing: eTutorWorld offers competitive pricing for their data science course, making it accessible to a wide range of students. This means that students can receive high-quality data science education without breaking the bank.
    • Comprehensive Curriculum: The data science course with eTutorWorld covers a wide range of topics, including data types and applications, statistics and probability, data exploration and visualization, and machine learning. This comprehensive curriculum ensures that students have a deep understanding of the field and are well-prepared for future studies or careers in data science.

    Frequently Asked Questions

    1. What is Data Science?

    Data Science is an interdisciplinary field that involves using scientific methods, processes, algorithms, and systems to extract insights and knowledge from data. It combines various fields such as statistics, mathematics, computer science, and domain-specific expertise to analyze and interpret complex data.

    2. What are the career opportunities in Data Science?

    Data Science has become one of the most in-demand and lucrative careers of the 21st century. The field offers many career opportunities such as data scientist, data analyst, business intelligence analyst, data engineer, machine learning engineer, and many more.

    3. What do I learn during this course?

    During this course, you will learn about the different aspects of Data Science such as data types and applications, statistics and probability, data exploration and visualization, and machine learning. You will also learn about popular tools and libraries used in Data Science such as Pandas, Matplotlib, and NumPy. Additionally, you will learn how to work with Jupyter notebooks and Google Colab to write and execute code, and how to build a training model for machine learning using Data Science.

    4. How much does this course cost?

    The cost of this course may vary depending on the course provider. However, at eTutorWorld, the cost of the Data Science course is competitive and affordable.

    5. What are the prerequisites and requirements to learn this course?

    To learn this course, it is recommended to have a basic understanding of Python programming language, as the course will cover some Python libraries for Data Science. Additionally, a basic understanding of statistics and probability will be helpful in understanding the concepts covered in the course.

    6. What is Data Science all about?

    Data Science is all about using scientific methods, processes, algorithms, and systems to extract insights and knowledge from data. It involves collecting, analyzing, and interpreting large amounts of data to identify patterns and trends, and to make informed decisions. Data Science has applications in various fields such as healthcare, finance, retail, and marketing, among others.