Data Science with Python
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Course Overview
Want to enter the world of Data Science and master Python programming? This Data Science with Python short course helps you learn applied data science through a practical, blended program. Build skills in data preparation, model evaluation and visualisation while mastering core Python tools (NumPy, pandas) and statistical techniques (hypothesis testing, probability, ANOVA). You’ll create insights with Matplotlib, Seaborn, Plotly and Bokeh, and complete hands-on projects to apply what you learn—delivered online with live expert sessions and self-paced content.
What you will learn
By the end of this short course, you will accomplish the following:
- Learn core data-science concepts and practical applications.
- Prepare data, build/evaluate simple models, and interpret results.
- Use Python fundamentals (strings, lists, Lambda functions).
- Work confidently with NumPy (arrays, indexing, slicing, stats).
- Apply linear algebra and essential calculus ideas in analysis.
- Understand statistics: central tendency, dispersion, skewness, covariance, correlation.
- Perform hypothesis testing (Z-test, T-test, ANOVA).
- Use pandas (Series/DataFrame) for loading, merging, binning, normalising and standardising data.
- Create visualisations with Matplotlib, Seaborn, Plotly, Bokeh.
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Tools Covered:
- Python
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Plotly
- Bokeh
- Jupyter Notebook
Course Modules
Get oriented to the program structure, learning outcomes and how the lessons, labs and resources fit together so you’re set up for success.
Discover what data science is, where it’s used, and the end-to-end process—from framing a business problem to delivering insights and impact.
Set up Jupyter and practise Python fundamentals: functions, types, strings, files/CSV, dates and objects, plus lambdas and list comprehensions. Survey key data-science packages you’ll use throughout the course.
Master NumPy arrays and their attributes. Perform arithmetic and conditional operations, apply common maths/statistics functions, and work confidently with indexing, slicing and basic file handling.
Build the maths foundation for data work: vectors (dot product, norms, independence) and matrices (operations, transpose, rank, determinants, inverses), with an intro to eigenvalues/eigenvectors and where calculus fits.
Learn core statistical concepts and terminology, types of statistics and data, levels of measurement, central tendency and dispersion, shape (skewness/kurtosis), covariance and correlation, and random variables/sets.
Explore discrete and continuous distributions (binomial, Poisson, normal, uniform, Bernoulli). Understand PDFs/PMFs, CDFs, the Central Limit Theorem and estimation theory to support sound inference.
Apply hypothesis testing end-to-end: null/alternative hypotheses, Type I/II errors, confidence intervals/levels and margins of error. Run t-tests, z-tests, chi-square and ANOVA, and interpret F-distributions—reinforced with Python demos.
Work fluently with Series and DataFrames. Use common and statistical functions, handle dates/timedeltas, read/write data, manage categorical and text data, iterate and sort, and create quick plots with pandas.
Understand data types and sources, choose appropriate tools, and perform collection and wrangling. Import/export in Python, use regular expressions for text processing, and access databases for analysis.
Write idiomatic pandas, load/index/reindex efficiently, merge datasets, optimise memory, clean and impute missing values, bin and standardise data, and describe datasets to prepare them for modelling.
Apply visualisation principles and practice with Matplotlib, Seaborn, Plotly and Bokeh. Produce a range of charts (including 3D) and learn when to use each to communicate insights clearly.
Solve a complete statistics problem in Python—from problem statement and approach to basic and advanced solutions (including SciPy)—then recap key takeaways and best practices.
- Lesson 01: Course Introduction
- Lesson 02: Introduction to Statistics
- Lesson 03: Understanding the Data
- Lesson 04: Descriptive Statistics
- Lesson 05: Data Visualisation
- Lesson 06: Probability
- Lesson 07: Probability Distributions
- Lesson 08: Sampling and Sampling Techniques
- Lesson 09: Inferential Statistics
- Lesson 10: Application of Inferential Statistics
- Lesson 11: Relation between Variables
- Lesson 12: Application of Statistics in Business
- Lesson 13: Assisted Practice
Payment Options
Course projects
To further bolster your Python programming in data science skills, you will get the chance to work on 5 practical course projects that mimic real-world scenarios. This gives you the opportunity to apply your data science knowledge and problem-solve in an industry-related project. You will work on projects with leading industries, and these are highlighted in detail below.
Sales Analysis for Business Growth
Marketing Campaign Analysis
Real Estate Data Visualisation
Housing Price Analysis
Customer Behaviour Analysis
Show off your achievements
Earn your Data Science with Python Short Course Certification
Digital certificates are the new way for Upskilled and Simplilearn graduates to offer proof of their hard earned knowledge or skill set.
You will receive individual certificates after each short course. Additionally, upon completion of the entire course, you will earn a certificate demonstrating your competence and expertise as a data science professional specialising in python.
Differentiate Yourself
Set yourself apart from the competition with the Data Science with Python Short Course. This is your ticket to get your foot through the door and proof that you have applied Python knowledge in data science and skills to real-world projects and labs making you job ready.
Share your achievement
You worked for it, you earned it! Share your achievement loud and proud! Talk about your Data Science with Python short course certification on LinkedIn, Twitter, Facebook. Add it to your CV to stand out and showcase to your employers.
FAQs
An Online Bootcamp is an intensive and accelerated learning program made up of a collection of self-paced eLearning components and live online classes that students are required to attend.
The Online Bootcamp program curriculum contain a combination of specifically chosen courses and career-critical skills that are aligned to a job role.
A Short Course on the other hand are shorter courses that is designed to target developing a specific skillset or topic. They generally are much quicker to complete than Bootcamp Programs.
Attaining a certificate in Python programming can be challenging and appear difficult if you don't have the proper preparation in it. By preparing yourself with the necessary concepts, principles and concepts Python computing and apply your skills in hands-on projects, you can set yourself up to be an expert in Python programming.
Students come from a diverse range of backgrounds, from seasoned professionals to non-data or non-technical background, this data science with python course is open to everyone of any background. Students do not necessarily have a related programming background to qualify for this online python online course.
Examples of students who study the Data Science with Python short course have transitioned into data science from the following backgrounds:
- Software Engineering
- Finance
- User Experience
- Marketing & Sales
- Psychologist
- HR
- Design
- A new comer with no relevant or technical background
On average, professionals in data science with Python background earn an annual salary of $140,000 AUD
The job outlook for certified Python programmers is growing in demand at a consistent rate and will continue to rise in the next coming years. Because Python programming is considered a farily easy and scalable coding language, many large corporations and other growing organisations use Python as their primary coding language. Overall, the demand for professionals with a certificate in Python programming has greatly increase and a role in Python is considered highly valuable to a company.
Data science is a domain in the technology space that involves dealing with large volumes of data using modern technologies and tools. Data scientists are logic specialists and masters in information data, using tools to discover unique patterns in data, identify any hidden trends and derive valuable information to aid in better decision-making.
As organisations collect large amounts of data, they implement various data science concepts and algorithms to build predictive data models. If you're interested in the world of big data, the Data Scientist with Python short course can help you learn and apply all of these concepts from scratch.
Data scientists are high in demand and are ranked among the top fields in Linkedin's Job Reports for the last three years and running. According to the latest information, data scientist roles are growing annually at 37%.
As such, data science bootcamps are increasingly valued due to their emphasis on a hands-on focused and immersive approach. These days many organisations value skills that are job ready and demonstrable, and bootcamps are an appraised path to achieve this practical experience.
Yes, upon completing the course program and projects, you will gain a Data Scientist with Python certification as a demonstration of your applied practical skills and knowledge in Python, which you can showcase in your CV.
To enrol in this Data Science with Python short course, you'll need to first submit an enquiry form via our website. You'll need to provide the following details:
- Your Name
- Best Phone Number
- Email Address
Once you've submitted your online form, one of our education consultants will be in touch within 48 hours.
During the consultation, you'll be able to ask questions regarding payment options, learning content and what career outcomes you can pursue if you complete your studies.
To become a data science expert, all you require is prior experience in mathematics or statistics and knowledge of programming languages like Python, Java, C++, etc. Upskilled and Simplilearn helps you gain an in-depth understanding of the application of Python in the world of data science, its concepts, tools and methodologies.
Designed with a unique interactive environment, the Data Science with Python short course will help you gain fluency in Python so you can advance in your career in data science. Upon completion you will earn an industry-recognized certificate that will strongly demonstrate your new skills and on-the-job expertise as a master in Python programming in data science.