Introduction to Data Science Course
What is a data science course ?
A data science course is a type of educational program that teaches the concepts and techniques used in the field of data science. It covers topics such as data collection, cleaning and pre-processing, statistical analysis, machine learning, data visualization, and communication of findings.
PurpleTutor offers an excellent introductory Data Science course, designed individually for differing student profiles such as middle school, senior school and junior college students. Our data science course is a Python for Data Science course, in which Python is the programming language used to perform the tasks involved in data science. Unlike most other data analytics courses, in our Python for Data Science course, the concepts are explained in a simple manner. Complex topics are introduced and explored, with practical and easily understandable examples. The goal of our data science course is to familiarize students well with data science concepts thus laying a base for them to pursue a university data science degree, in future, if they wish.
What will you learn in our data science course?
Our data science course introduces the student to the basics of data science and how they can be applied to real-world problems.
We offer the Python for data science course to students falling in these 3 age groups:
Age: 9-11 years, Young Learners (YL)
In this age-group, students will learn –
- Fundamentals of Python programming language, including data types, variables, loops, functions
- How to create and use Google Sheets for storing and summarizing data.
- Data handling and cleaning using libraries like Pandas.
- File handling: management of csv files in data science with Python
- Data visualization using Pandas.
- Handling and solving real-world data problems.
Age: 12-15 years, Early Achievers (EA) & Young Professionals (YP)
There are 2 courses covering the ages, one from 12 to 15 years and another for 15+ yrs.
In both the above courses students will –
- Review fundamentals of Python programming language, including data types, variables, loops, functions
- Explore and apply Python for Data Science libraries which are useful in data science such as the math, random, statistics libraries.
- Understand and apply the concepts of Object-Oriented Programming.
- Understand and apply descriptive and inferential statistics.
- Understand and use file handling: management of csv files in data science with Python.
- Understand the concept of big data.
- Understand Data handling and cleaning using libraries like Pandas and NumPy.
- Apply Data visualization using libraries like Matplotlib.
- Apply Data pre-processing techniques.
- Understand handling and solving of real-world data problems.
The goal of our data science with Python course is to equip students with the skills and knowledge required to perform end-to-end data analysis and modelling tasks.
What are the benefits of doing our Data Science course?
There are several benefits of doing our Data Science course for kids in the age groups of 9-14 yrs.
- Problem Solving Skills: Pursuing our Python for Data Science course teaches kids to think logically and systematically, helping them develop problem-solving skills.
- Coding Practice: The data science course we offer provides plenty of coding practice in Python for students as they complete the assigned tasks. This empowers them to perfect their Python coding skills.
- Early Exposure to Cutting-Edge Technology: Data science, and especially data science with Python is a rapidly evolving field and learning it at an early age can give students a competitive advantage in the future.
- Understanding of Real-World Applications: Python for data science has a wide range of applications, and children can learn about these applications and how they can be used to solve real-world problems.
- Improved Critical Thinking and Analytical Skills: Data science involves analyzing data and drawing conclusions, helping children improve their critical thinking and analytical skills.
In addition, for college students who are weighing career options, selecting our Python for data science course could be beneficial in the following ways –
- Career opportunities: Data science using Python is a rapidly growing field with high demand for skilled professionals. A course in data science using Python can open up a variety of career paths in industries such as finance, healthcare, technology, and more.
- Interdisciplinary Skills: Data science requires a combination of technical, mathematical, and business skills, making it a field that draws from multiple disciplines. Doing a Python for data science course will help college students to understand and excel in related subjects like math, science and statistics.
Overall, our data science course can provide a solid foundation for a rewarding and challenging career and the skills to work with data in a meaningful and impactful way.
Course Content
Our data science course has been created especially for students falling in these 3 age groups:
Age : 9-11 years
Name of the course – Introduction to Data Science –Young Learners (YL)
While pursuing the above data science course, students will explore and understand different types of data and their real-life applications. They will be introduced to the working of Google Sheets and will learn how to run basic math operations to analyse data and represent it using different types of charts and infographics. During the data analysis module, they will learn the Python Pandas library commands to read data from the CSV file and create dataframes to analyze data.
You can explore the content for the Data Science course(YL) here –
| INTRODUCTION TO DATA SCIENCE | |
| Session | Concept |
| 1 | Introduction to Data and Data Science |
| 2 | Introduction to Google sheets |
| 3 | |
| 4 | Using formulae in Google Sheets |
| 5 | |
| 6 | Formative Assessment |
| 7 | Event Planning |
| 8 | Data Visualization |
| 9 | |
| 10 | Data Representation |
| 11 | |
| 12 | Data Visualization techniques |
| 13 | Data cleanup |
| 14 | |
| 15 | Introduction to Infographics |
| 16 | Creating the Infographic |
| 17 | Formative Assessment |
| 18 | Introduction to Data Analysis & Python Basics |
| 19 | |
| 20 | |
| 21 | Introduction to Pandas Series |
| 22 | Introduction to pandas DataFrames |
| 23 | |
| 24 | |
| 25 | Introduction Pandas Statistical Functions |
| 26 | Working with Text Files and .csv Files in Python |
| 27 | |
| 28 | Pandas Plotting |
| 29 | |
| 30 | Formative Assessment |
To download the detailed Introduction to Data Science – (YL) course content, click here!
Age: 12-15 years
Name of the course – Data Science – Python for Early Achievers (EA)
While pursuing the above data science course, students will explore and understand different types of data and their real-life applications. They will be introduced to the working of Google Sheets and will learn how to use the Python Numpy module to analyze data.
Students will explore the Python Panda library commands to create dataframes. Using Pandas, students will learn how to read data from the CSV file and use dataframes to analyze data.
Students will learn how to visually represent the data using the methods of the Python Matplotlib library. The data is represented using different types of charts.
You can explore the content for the Data Science course(EA) here –
| DATA SCIENCE – PYTHON | |
| Session | Concept |
| 1 | Introduction to Python packages |
| 2 | Using Python Packages : Pandas |
| 3 | Using Python packages – Matplotlib |
| 4 | Using Python packages – NumPy |
| 5 | Introduction to modules -the statistics module |
| 6 | The math module |
| 7 | |
| 8 | The random module |
| 9 | |
| 10 | Errors and Error handling |
| 11 | Formative Assessment |
| 12 | Introduction to Files |
| 13 | Working with text files |
| 14 | Working with Binary files |
| 15 | Classes and Objects |
| 16 | |
| 17 | Principles of OOP |
| 18 | |
| 19 | Storing state of objects using the Pickle module |
| 20 | Formative Assessment |
| 21 | Understanding data |
| 22 | Big Data |
| 23 | Statistical analysis of data – Terms and Plotting |
| 24 | Statistical analysis of data – Statistical Measures |
| 25 | Formative Assessment |
| 26 | Exploring the numpy package |
| 27 | Operations on numpy arrays |
| 28 | |
| 29 | Working with file data in numpy |
| 30 | Statistical Methods in numpy |
| 31 | Exploring the Pandas package – Series |
| 32 | Operations on Pandas Dataframes |
| 33 | |
| 34 | Filtering Dataframes |
| 35 | Data Cleaning |
| 36 | Formative Assessment |
| 37 | Matplotlib – Line Plot |
| 38 | Matplotlib – Pie Plot |
| 39 | Matplotlib-Bar plot and Histogram |
| 40 | |
| 41 | Matplotlib-Scatter plot |
| 42 | |
| 43 | Data Science Project |
| 44 | |
| 45 | |
To download the detailed Data Science – Python(EA) course content, click here!
Age: 15+ years
Name of the course – Data Science – Python for Young Professionals(YP)
While pursuing the above data science course, students will explore and understand different types of data and their real-life applications. They will learn how to use the Python Numpy module to analyze data. Students will explore the Python Panda library commands to create dataframes. Using Pandas, students will learn how to read data from the CSV file and use dataframes to analyze data. Students will learn how to visually represent the data using the methods of the Python Matplotlib library. The data is represented using different types of charts.
You can explore the content for the Data Science – Python for Young Professionals(YP) course here –
| DATA SCIENCE – PYTHON | |
| Session | Concept |
| 1 | Introduction to Python packages |
| 2 | Using Python Packages : Pandas |
| 3 | Using Python packages – Matplotlib |
| 4 | Using Python packages – NumPy |
| 5 | Introduction to modules -the statistics module |
| 6 | The math module |
| 7 | |
| 8 | The random module |
| 9 | |
| 10 | Errors and Error handling |
| 11 | Formative Assessment |
| 12 | Introduction to Files |
| 13 | Working with text files |
| 14 | Working with Binary files |
| 15 | Classes and Objects |
| 16 | |
| 17 | Principles of OOP |
| 18 | |
| 19 | Storing state of objects using the Pickle module |
| 20 | Formative Assessment |
| 21 | Understanding data |
| 22 | Big Data |
| 23 | Statistical analysis of data – Terms and Plotting |
| 24 | Statistical analysis of data – Statistical Measures |
| 25 | Formative Assessment |
| 26 | Exploring the numpy package |
| 27 | Operations on numpy arrays |
| 28 | |
| 29 | Working with file data in numpy |
| 30 | Statistical Methods in numpy |
| 31 | Exploring the Pandas package – Series |
| 32 | Operations on Pandas Dataframes |
| 33 | |
| 34 | Filtering Dataframes |
| 35 | Data Cleaning |
| 36 | Formative Assessment |
| 37 | Matplotlib – Line Plot |
| 38 | Matplotlib – Pie Plot |
| 39 | Matplotlib-Bar plot and Histogram |
| 40 | |
| 41 | Matplotlib-Scatter plot |
| 42 | |
| 43 | Data Science Project |
| 44 | |
| 45 | |
To download the detailed Introduction to Data Science – Python for Young Professionals (YP)
course content, click here!
Course Duration and Certificate
The Introduction to Data Science for Young Learners (9-11 years) course consists of 30 sessions of one hour each, therefore the total duration of this course is: 30 hours.
The Data Science with Python for Early Achievers (12-15 years) course consists of 45 sessions of one hour each, therefore the total duration of this course is: 45 hours.
The Data Science with Python for Young Professionals (15+ years) course consists of 45 sessions of one hour each, therefore the total duration of this course is: 45 hours.
On completion of the course, a certificate is given to the student. The certificate recognises the skills the student learnt, and the level of mastery achieved.
Requirements for the course
- Students need to have the knowledge of core Python programming concepts such as data types, variables, loops, conditionals and functions. Using these concepts, they should be able to write Python code to perform small tasks.
- It is necessary to have a laptop or computer with a webcam and a stable internet connection to take our Data Science course.
Frequently Asked Questions (FAQs)
1. Do you offer a demo class?
Yes. The first demo class is free of charge. You can book the free class from the booking link.
2. Can I choose my own days and timings for the classes?
A: Yes. The days and timings of the classes are flexible. You can select any time and any day that suits your timetable.
3. How do I know if learning Data Science using Python is easy?
A: The teachers assess the level of the student in the demo class and then will give the suggestion of whether to go ahead with the data science course online.
4. Is there any data science certification done on completion of the Python for data science course online?
A: The student will get a certificate after completion of the course. The certificate recognises the skills the student learnt, and the level of mastery achieved.
5. What do you require for learning data science using Python from PurpleTutor?
A: It is necessary to have a laptop or computer with a webcam and a stable internet connection to take our data science course online
6. Do you have assessments during the course?
A. Yes, we assess the student periodically during the progress of the classes and give feedback on the student’s performance.
7. Will PurpleTutor’s data science course help me later if I wish to pursue a university data science degree?
A. Definitely. PurpleTutor’s data science course enables the student to understand and gain expertise in the fundamental concepts of data science.
8. How is your course different from other data analytics courses?
A. Unlike most other data analytics courses, in our Python for Data Science course, the concepts are explained in a simple manner. You could say another name for our course is ‘Data Science made easy’.