Master the key Python skills for data analysis, visualization, statistical analysis, and machine learning. Build a solid foundation for a successful start to your journey in data science.
Python for Data Science
6h 21m 57s
English
Paid
Python for Data Science is a 21-lesson 6 hours 21 minutes self-paced course by LunarTech. Master the key Python skills for data analysis, visualization, statistical analysis, and machine learning.
Course facts
- Lessons
- 21
- Duration
- 6 hours 21 minutes
- Level
- All levels
- Language
- English
- Updated
- Instructor
- LunarTech
- Price
- Premium
Who teaches Python for Data Science? LunarTech
LunarTech is an online tech academy focused on data science, machine learning, and quantitative analysis — covering both the theoretical foundations (linear algebra, calculus, statistics) and the practical Python / SQL toolchain that working data scientists use. The school operates globally with cohort-based and self-paced tracks.
The CourseFlix listing carries twelve LunarTech courses spanning machine-learning theory, deep learning, applied data-science workflows, and the math fundamentals underlying the field. Material is paid and aimed at engineers and analysts transitioning into formal data-science roles or upskilling within them.
What lessons are included in Python for Data Science?
0:00
/ #1: Introduction
All Course Lessons (21)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 1 | Introduction Demo | 15:09 | |
| 2 | Best Coding Practices | 05:32 | |
| 3 | Python & PyCharm (IDE) Configuration | 16:09 | |
| 4 | Demo 1: Data Types & Creating Variables | 18:39 | |
| 5 | Demo 2: Lists/Matrices/Dictionaries | 17:30 | |
| 6 | Demo 3: For-Loops in Python | 16:54 | |
| 7 | Demo 4: If-Else Statements | 20:00 | |
| 8 | Demo 5: Python Libraries for Data Science | 18:04 | |
| 9 | Demo 6: Loading Data in Python | 16:06 | |
| 10 | Demo 7: Data Exploration & Preprocessing | 14:33 | |
| 11 | Demo 8: Random Data Generation (Data Simulation) | 19:53 | |
| 12 | Demo 9: Filtering, Sorting, Grouping | 21:08 | |
| 13 | Demo 10: Descriptive Statistics | 15:50 | |
| 14 | Demo 11: Merging & Joining Datasets | 21:27 | |
| 15 | Demo 12: User Defined Functions (UDF) | 13:50 | |
| 16 | Demo 13: Text Cleaning and Preparation in NLP | 21:11 | |
| 17 | Demo 14: Data Visualization in Python | 20:57 | |
| 18 | Demo 15: Data Sampling (Part 1) - Theory | 28:16 | |
| 19 | Demo 16: Data Sampling (Part 2) - Coding | 20:34 | |
| 20 | Demo 17: A/B Test Results Analysis (Part 1) - Theory | 22:49 | |
| 21 | Demo 18 - A/B Test Results Analysis (Part 2) - Coding | 17:26 |
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Frequently asked questions
What prerequisites are needed for this course?
The course is designed for beginners in data science. A basic understanding of programming concepts is helpful but not necessary, as the course starts with foundational Python programming, including data types, variables, and control structures like for-loops and if-else statements.
What types of projects will I build during the course?
You will engage in practical exercises such as loading and exploring data, filtering and sorting datasets, and performing descriptive statistics. You'll also work on projects involving data visualization and A/B test results analysis, both in theory and coding, which will help solidify your data science skills.
Who is the target audience for this course?
This course is aimed at individuals who are new to data science and want to learn Python to perform data analysis, visualization, and statistical analysis. It's suitable for those interested in pursuing a career in data science or enhancing their data-handling skills.
How does the course compare in depth to other similar courses?
This course provides a solid introduction to Python for data science, focusing on practical skills like data preprocessing, merging datasets, and text cleaning for NLP. It offers a foundational understanding rather than an in-depth exploration of advanced topics, making it ideal for beginners.
What specific tools or platforms will I learn to use?
You will learn to configure and use the PyCharm Integrated Development Environment (IDE) for Python programming. The course also covers key Python libraries for data science, which are essential for performing data analysis and visualization tasks effectively.
What is not covered in this course that might be important for a data science career?
The course does not cover advanced machine learning algorithms or deep learning techniques. While it introduces statistical analysis and A/B testing, more complex statistical modeling and data engineering concepts are not addressed, which might be important for advanced data science roles.
How much time should I expect to commit to this course?
The course consists of 21 lessons. While the exact runtime is not specified, you should allocate time for each lesson to understand the concepts and complete practical exercises. Depending on your pace and familiarity with the material, dedicating several hours per week is advisable.