Best Data Scientist Courses & Certifications
What Is Data Science?
Data Science is a rapidly growing field that combines statistical analysis, machine learning, and computer science to extract insights from complex data sets. As the amount of available data continues to grow, so does the demand for data scientists. For young professionals looking to enter this field, there are a variety of educational options available, from full-time university programs to part-time online courses. Many universities now offer degree programs in data science that provide young professionals with comprehensive training in the field. Data scientist courses for students lay an excellent foundation who aspire to become data scientists. In this article, we will explore PurpleTutor’s best Data Scientist courses for the young generation.
A fundamental aspect of data science is the ability to analyze and interpret data accurately. Data science courses involve teaching statistical and computational techniques to analyze and make sense of data, which is an essential skill in fields such as business, healthcare, finance, and many more. There is a huge gap in the demand for data scientists and their supply. As per a recent survey, 92% of hiring managers feel that there is a shortage of talent when it comes to data scientist careers. The goal of our Data Scientist courses is to familiarize students well with data science concepts in Python.
In conclusion, Data Scientist courses offer numerous opportunities for young professionals to build successful careers. With our Data Scientist courses, young professionals can prepare themselves for a rewarding career by learning Python Programming language for Data Science. With dedication, hard work, and a passion for data science, young professionals can build a bright future in this field.
What will you learn in the course?
Our Data Scientist courses introduce the student to the basics of data science and how they can be applied to real-world problems.
We offer Data Scientist courses to students falling in these 3 age groups:
Age group: 9-11 years (Young Learners)
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.
The second and third age groups for which we offer the Data Science using Python course are as follows:
Age group: 12-14 years (Early Achievers) & 15+ years (Young Professionals)
The following will be covered in both of the above courses:
- Review fundamentals of Python programming language, including data types, variables, loops, and functions
- Explore and apply Python for Data Science libraries that are useful in data science such as the math, random, and statistics libraries.
- Understand and apply the concepts of Object Oriented Programming.
- Understand and apply descriptive and inferential statistics.
- Understand and use file handling, and 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 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 modeling tasks,
Benefits of Data Scientist courses
There are several benefits of doing our Data Scientist course for students aged 9 years and above.
- Problem-Solving Skills: Pursuing our Data Scientist courses teach students to think logically and systematically, helping them develop problem-solving skills.
- Coding Practice: The Data Scientist courses we offer provide 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 Data Scientist courses 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 Scientist course will help college students to understand and excel in related subjects like math, science, and statistics.
Overall, our Data Scientist courses can provide a solid foundation for a rewarding and challenging career and the skills to work with data in a meaningful and impactful way.
Our Data Scientist course has been created especially for students falling in these 3 age groups:
Age group: 9-11 years
Name of the course – Introduction to Data Science – Young Learners (YL)
While pursuing the above 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 data frames 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 for ages 9-11 years, click here!
Age group: 12-15 years
Name of the course – Data Science – Python for Early Achievers (EA)
As students pursue the Data Scientist course, they will explore and understand different types of data and their real-life applications. Data analysis will be taught using the Python Numpy module and Google Sheets. Students will learn how to create data frames using Python Panda’s commands. With Pandas, students will read and analyze CSV files using data frames. Matplotlib is a Python library that helps students visualize data.
You can explore the content for the Data Scientist course of EA level 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)
In the Data Science course, students will learn about different types of data and their real-life applications. Analyzing data will be done using Numpy, a Python module. Dataframes will be created using Python Panda library commands. By using Pandas, students will learn how to read data from CSV files and analyze data with dataframes. Data will be visually represented using the Python Matplotlib library.
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 Data Science – Python for Young Professionals(YP) course content for ages 15+ years, click here!
Course Duration and Certificate
The Introduction to Data Science for Young Learners(YL-9 to 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 to 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 learned and the level of mastery achieved.
Requirements for the course
- Students do not need to have any prior coding knowledge when they first come to us for learning Data Science
- There are no coding prerequisites since our Data Scientist course will start with the very basics before moving on to the core concepts.
- It is necessary to have a laptop or computer with a webcam and a stable internet connection to take our Data Scientist course.
Frequently Asked Questions (FAQs)
1. Can I try a free class for coding?
A: 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 our Data Scientist courses online.
4. Is there any data science certification done upon completion of the Data Scientist courses online?
A: The student will get a certificate after completion of the course. The certificate recognises the skills the student learned 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 Scientist courses 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 Scientist courses help me later if I wish to pursue a university data science degree?
A. Definitely. PurpleTutor’s Data Scientist courses enable 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 Data Scientist course, the concepts are explained in a simple manner. You could say another name for our course is ‘Data Science made easy’.