What is a Python for Data Science tutorial?
Python is a popular programming language that is widely used in data science. It offers a variety of tools and libraries that make it easier to perform data analysis, visualization, and modeling. A Python for Data Science tutorial covers the basics of how to use Python for data science, such as :
- Installing and setting up Python for data science – Before you start a Python for data science tutorial, you need to install and set up the necessary tools. The most common way to do this is by downloading and installing the Anaconda distribution. Once you’ve installed Anaconda, you can open up Jupyter Notebook, which is an interactive coding environment that allows you to write, run, and save Python code. In a Python for Data Science tutorial taken online, alternatively, you can use Google Colab Notebooks which is completely cloud-based and includes Python, as well as a variety of Python data science libraries.
- Working with data using Python’s data structures and libraries – Python provides a collection of data structures that make it easy to work with data, including lists, dictionaries, and arrays. A good Python for data science tutorial will teach the student how to work with these data structures. In addition, the Python for data science tutorial should also explore in depth the Python libraries that are specifically designed for data science tasks.
Some of the most commonly used libraries include:
NumPy: A library for working with arrays of data.
Pandas: A library for working with data in tables (similar to Excel spreadsheets).
Matplotlib: A library for creating visualizations of data.
Scikit-learn: A library for performing machine learning tasks.
- Data cleaning and preprocessing – Before you can perform any meaningful analysis on your data, you need to clean and preprocess it. A Python for data science tutorial will involve tasks like removing missing values, dealing with outliers, and transforming the data into a format that is suitable for analysis. Pandas provides a variety of functions for doing these tasks, including functions for filling in missing data, removing duplicates, and transforming data.
- Data visualization with Python – Visualizing data is an important part of data science, as it allows you to gain insights into your data that might not be apparent from just looking at the raw numbers. Python for data science tutorial sessions will teach students the intricacies of Matplotlib, which is a powerful library for creating visualizations in Python.
- Statistical analysis and modeling with Python – Once you’ve cleaned and preprocessed your data and created some visualizations in your Python for data science tutorial classes, you can start performing statistical analysis and building models.
These are the essential concepts that an introductory Python for data science tutorial should include. In our repertoire of popular courses, PurpleTutor offers the perfect Python for data science tutorial course, which covers all these fundamentals in a straightforward and practical way.
What will you learn in our Python for data science tutorial?
Our Python for data science tutorial explains the fundamentals of data science and demonstrates the application of data science to real-world problems.
We have age appropriate courses across levels. PurpleTutor offers the Python for data science tutorial courses to students falling in these 3 age groups:
Age group 9-11 years: Young Learners (YL)
In this age-group, students will learn:
- Core concepts of Python
- Understanding how to create and use Google Sheets for storing and summarizing data.
- Using the Python Pandas library for cleaning,presenting, and managing data, and for data visualization.
- Management of csv files
- Understanding and solving real-world data problems.
The second and third age groups for which we offer the Data Science using Python tutorial courses are as follows:
Age group: 12-15 years Early Achievers (EA) & 15+ years-Young Professionals (YP)
In both the above courses students will –
- Review core concepts of Python, including data types, variables, loops, functions
- Explore the Python libraries which are useful in data analytics such as the math, random, statistics libraries.
- Understand and apply the concepts of Object Oriented Programming.
- Analyze data using descriptive and inferential statistics.
- Manage files
- Understand the concept of big data
- Learn how to create and use Google Colab notebooks.
- Perform data handling and cleaning using Pandas and NumPy.
- Perform data visualization using Matplotlib.
- Apply Data pre-processing techniques
- Solve real-world data problems.
The aim of our Python for data science tutorial courses is to equip students with the skills and knowledge required to perform end-to-end data analysis and modeling tasks.
What are the benefits of opting for our Python for data science tutorial courses?
There are several benefits of opting for our Python for data science tutorial courses including:
- Structured learning: We provide a structured learning experience that covers all the key concepts and tools in Python for data science. This can help you learn more efficiently and effectively than if you were trying to learn on your own.
- Expert guidance: When you take our Python for data science tutorial sessions, you’ll have access to expert instructors who can provide guidance and support as you learn. This can be especially helpful if you’re new to data science or programming in general.
- Hands-on experience: Our Python for data science tutorial classes will provide opportunities for you to apply what you’re learning in practical, hands-on exercises and projects. This can help you develop your skills and build a portfolio of work that can demonstrate your abilities to your peers.
- Credentials: Finally, taking a Python for data science tutorial course from us can provide you with a credential that demonstrates your knowledge and skills in Python for data science. This can be valuable for pursuing further education in the field.
Overall, taking a Python for data science tutorial course from us can provide you with a structured, expert-guided learning experience that can help you develop your skills and build a strong foundation in data science.
Course Content
Our Python for data science tutorial courses have been created specially for students falling in these 3 age groups:
1. Age group: 9-11 years
Name of the course : Introduction to Data Science – Young Learners (YL)
While pursuing the above Python for data science tutorial 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 analyze 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 the content for the Data Science – Python 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 – Python(YL) course content, click here!
Age group: 12-15 years
Name of the course –Data Science – Python for Early Achievers(EA)
While pursuing the above Python for data science tutorial 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 Google Colab Notebooks 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 the content for the Data Science – Python course(EA) for ages 12-15 years, 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 for ages 12-15 years, click here!
Age group – 15+ years
Name of the course – Data Science – Python for Young Professionals(YP)
Students will explore and understand different types of data and their real life applications, while pursuing the above Python for data science tutorial course. Students will be introduced to the environment of Google Colab Notebooks. They will learn how to create notebooks, write and execute code in the notebooks, and how to interpret the output. They will learn how to use the Python Numpy module to analyze data. Students will explore the Python Panda library commands to create and manage data using dataframes. Students will learn how to read data from the CSV file. Students will visually present the data using the Python Matplotlib library, in the form of plots and charts.
You can explore the content for the Data Science – Python for Young Professionals(YP)
course for ages 15+ years 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 for Young Professionals(YP) course content for ages 15+ years, click here!
Course Duration and Certificate
The Data Science with Python for Young Learners(YL-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(EA-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(YP-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 Python for data science tutorial course
- Students should know the core Python Programming concepts such as variables, data-types, loops, conditionals, functions. They should be able to write Python code for performing small tasks.
- It is necessary to have a laptop or computer with a webcam and a stable internet connection to take our Data Science using Python course.
Frequently Asked Questions (FAQs)
1. For a student who is interested in enrolling, do you offer a sample of your classes first?
A: Yes. We give one free demo class, which can be booked from the booking link. We encourage you to take the class and assess the experience.
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 time-table.
3. How do I know if learning Python for data science 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 course online.
4. Is there any certificate given on completion of the Python for data science tutorial 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 taking the Python for data science tutorial from PurpleTutor?
A: Knowledge of basic Python concepts is required. It is necessary to have a laptop or computer with a webcam and a stable internet connection to take our Python for 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. What are the courses that PurpleTutor offers?
A: PurpleTutor provides Cutting edge courses to make the student’s future ready. We have courses like – Python, Web Development, Machine Learning and Artificial Intelligence Courses, Cyber Security, Roblox Games & many more on offer. Please visit our courses section for more information or talk to a counsellor. We encourage you to book a complimentary class with us, enjoy & assess the in-class experience. One can also discuss courses with our teachers in-person too during the class too.