We are delighted to welcome you into the first course of the EdX / UC San Diego MicroMasters in Data Science: Python for Data Science. In this course, you will learn both the basics of conducting data science and how to perform data analysis in python.

Learners will learn how to set up Jupyter notebooks, create, run, and manage code cells, and integrate text and visualizations using Markdown. Additionally, the module will showcase real-life applications of Python in solving data-related problems.

Learn Python - the most popular programming language and for Data Science and Software Development. Apply Python programming logic Variables, Data Structures, Branching, Loops, Functions, Objects & Classes. Demonstrate proficiency in using Python libraries such as Pandas & Numpy, and developing code using Jupyter Notebooks.

Explore various steps of data science pipeline with role of Python. Design applications applying various operations for data cleansing and transformation. Use various data visualization tools for effective interpretations and insights of data. Perform data Wrangling with Scikit-learn applying exploratory data analysis.

It is based on the Data Science with Python Foundation course first developed by GoDataDriven. The course provides guidance on the principles and practice of loading, analysing, visualizing data with libraries such as pandas. It also teaches participants how predictive models work and how to use Scikit-learn can be used to train and fit models.

3 days ago · Python for data science: Python is a simple and easy-to-learn programming language. Due to its extensive library support (Panda, Numpy, etc.) and easier syntaxes, python is preferred for more Data science.

Gain new insights into your data. Learn to apply data science methods and techniques, and acquire analysis skills. The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language.

Python for data science, hands-on experience in data manipulation and visualization, and the ability to build and deploy machine learning models.

Building a solid foundation to explore the fields of web development, game development, data science & artificial intelligence. Become Industry 4.0 ready with this comprehensive Python course. 3. Functions. iii. Map. 4. Modules in Python. 5. List. 6. Tuples. 7. Set. g. Disjoint set Set methods. List performance analysis When to use a List. 8.

Differences between OLTP and OLAP. Normalization and De-Normalization. Difference between relational and dimensional modelling.