About this course
This is the IBM Data Science with Python Basics course which provides a beginner-friendly introduction to Python for Data Science, where you can:
- Practice through lab exercises, and create your first Python scripts on your own.
- Start learning Python for data science, as well as programming in general with an introduction to Python.
- Go from zero to hero in Python programming in just a matter of hours. This will give you a taste of how to start working with data in Python.
About Data Science
Data science provides meaningful information based on large amounts of complex data or big data. Data science combines different fields of work in statistics and computation to interpret data for decision-making purposes.
A data scientist collects, analyzes, and interprets large volumes of data, in many cases, to improve a company’s operations. Data science professionals develop statistical models that analyze data and detect patterns, trends, and relationships in data sets.
Why Python for Data Science?
Python is one of the most popular languages in Data Science, which can be used to perform data analysis, data manipulation, and data visualization. Python offers access to a wide variety of data science libraries and it is the ideal language for implementing algorithms and the rapid development of applications.
The programming requirements of data science demand a very versatile yet flexible language that is simple to write code but can handle highly complex mathematical processing. Python is most suited for such requirements as it has already established itself both as a language for general computing as well as scientific computing. Moreover, it is being continuously upgraded in the form of a new addition to its plethora of libraries aimed at different programming requirements. Below we will discuss such features of python which makes it the preferred language for data science.
- It is simple and easy to learn, we can achieve results in fewer lines of code.
- Its simplicity also makes it robust to handle complex scenarios with minimal code and much less confusion on the general flow of the program.
- It supports cross-platform, thus the same code works with multiple environments.
- It executes faster than other similar languages used for data analysis like R and MATLAB.
- Its excellent memory management capability, especially garbage collection makes it versatile in gracefully managing a very large volume of data transformation.
- Python has got a very large collection of libraries and packages, which serve as special-purpose analysis tools where we can directly use code from other languages (Java or C).
What you’ll learn from the IBM Data Science with Python course?
The objectives of this course is to get you started with Python as the programming language and give you a taste of how to start working with data in Python.
In this course on IBM data science with python basics, you will learn about:
- What Python is and why it is useful
- The application of Python to Data Science
- How to define variables in Python
- Sets and conditional statements in Python
- The purpose of having functions in Python
- How to operate on files to read and write data in Python
- How to use pandas, a must-have package for anyone attempting data analysis in Python.
What after completing this course?
After completion of this course, you will be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this course is for you.
Also, you can start creating your own data science projects and collaborating with other data scientists using IBM Watson Studio. When you sign up, you will receive free access to Watson Studio. Start now and take advantage of this platform and learn the basics of programming, machine learning, and data visualization with this introductory course.
Syllabus IBM Data Science with Python course
The course on IBM data science with python basics consists of 5 weeks module as:
- Your first program
- Expressions and Variables
- String Operations
Python Data Structures
- Lists and Tuples
Python Programming Fundamentals
- Conditions and Branching
- Objects and Classes
Working with Data in Python
- Reading files with open
- Writing files with open
- Loading data with Pandas
- Working with and Saving data with Pandas
Working with Numpy Arrays
- Numpy 1d Arrays
- Numpy 2d Arrays
Prerequisites for IBM Data Science with Python course
You should be familiar with basic Mathematics algorithms.
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- 1-3 Months
- Online Course
- Free Course (Affordable Certificate)
- Data Science Data Science with 'Python'