Course Data Science with Python | DATA SCIENCE duration Online
Data is the new gold! With the technological development, the computerization and the widespread diffusion of the Internet, large end up collecting large amounts of data, but small companies can easily accumulate large quantities as well.
Contents and modules
This data can provide a lot of valuable information: they can tell us, for example, which customers are interested in one product rather than another, what trends are emerging or becoming obsolete, which customers are about to switch to a competitor and why, or even what to expect with good confidence in the immediate future.
If the knowledge of Python alone, being the most in-demand programming language in the world, is enough to arouse interest in companies, what recruiters are really looking for is someone who is able to manage data and extract a concrete meaning; multinationals, consulting companies, web agencies, advertising agencies, educational institutions and government are just some of the realities that require these skills.
The course aims to train on the theoretical and practical level professional figures dedicated, or who want to integrate in other areas, to Python programming, a versatile and powerful language, which this year rises to the first place of programming languages; to manipulation, transformation, analysis, and presentation of data through Python libraries, such as Pandas or Matplotlib; to the use of ready-to-use Machine Learning and Artificial Intelligence tools for Python such as Scikit or Tensorflow.
What will I learn?
Module 1 | Python 4 Beginners
Learning unit to start programming with Python from the basics. Development environment setup, introduction to object-oriented programming paradigms, reserved words, syntax, instructions, lists, sets, dictionaries, comprehension, operators, functions, packages, classes, exceptions, lambda function, basic external modules, advanced data types, advanced modules, mathematics, scientific, input/output, write files, exceptions, errors, debugging.
Module 2 | Data Science with Python
Manage datasets with Pandas, Import and manipulate datasets, Basic statistics, Graph creation with Matplotlib, Exploratory analysis with Seaborn, Data preparation, Data cleaning, Normalization, Coding of categorical variables, Dummy variables, Date and time series management.
Request an individual interview to know the details of the individual teaching units.
At the end of the course you will know how to:
- program in Python language having a good overview of the language, being able to manage backend projects and having the right foundations to deepen different aspects on your own.
- manage data, know how to retrieve, filter, transform and present it, also in graphic forms, and will be able, for example, to create reports, inspect which customers are interested in which product, manage production or supply, etc..
Notebook: 64-bit dual core CPU | 4GB RAM | S.O. Windows, GNU/Linux or macOS
NB: Academic hours of 50'.
Read the opinions of former students!
The ideal course for those who are interested in quickly absorbing notions of data mining and data manipulation in Python and integrate advanced forecasting models to their skills; very well structured, I recommend it to everyone because it has a practical cut that allows you to use the skills learned from the beginning, both at work (for data analyst/data scientist) and on a personal level, providing the tools to continue independently at the end of the course.
While knowing that in just three weekends it's impossible to go through all the contents related to machine learning, the course has managed to draw a precise and complete drawing of the supervised/unsupervised learning techniques available, stimulating the curiosity of those like me who are interested in going deeper.
A praise to the organization and preparation of the speaker, who captured the attention of the participants showing availability and deep knowledge of the topics.