Python for Data Science - NumPy, Pandas & Scikit-Learn
What you'll learn
- solve over 330 exercises in NumPy, Pandas and Scikit-Learn
- deal with real programming problems in data science
- work with documentation and Stack Overflow
- guaranteed instructor support
- basic knowledge of Python
- basic knowledge of NumPy, Pandas and Scikit-Learn
The "Python for Data Science - NumPy, Pandas & Scikit-Learn" course is a comprehensive guide to Python's most powerful data science libraries, designed to provide you with the skills necessary to tackle complex data analysis projects.
This course is tailored for beginners who want to delve into the world of data science, as well as experienced programmers who wish to diversify their skill set. You will learn to manipulate, analyze, and visualize data using Python, a leading programming language for data science.
The course begins with an exploration of NumPy, the fundamental package for numerical computing in Python. You'll gain a strong understanding of arrays and array-oriented computing which is crucial for performance-intensive data analysis.
The focus then shifts to Pandas, a library designed for data manipulation and analysis. You'll learn to work with Series and DataFrames, handle missing data, and perform operations like merge, concatenate, and group by.
The final section of the course is dedicated to Scikit-Learn, a library providing efficient tools for machine learning and statistical modeling. Here you'll delve into data preprocessing, model selection, and evaluation, as well as a broad range of algorithms for classification, regression, clustering, and dimensionality reduction.
By the end of the "Python for Data Science - NumPy, Pandas & Scikit-Learn" course, you will have a firm grasp of how to use Python's primary data science libraries to conduct sophisticated data analysis, equipping you with the knowledge to undertake your own data-driven projects.
Data Scientist - Unveiling Insights from Data Universe!
A data scientist is a skilled professional who leverages their expertise in mathematics, statistics, programming, and domain knowledge to extract meaningful insights and valuable knowledge from complex datasets. They utilize various analytical techniques, statistical models, and machine learning algorithms to discover patterns, trends, and correlations within the data.
The role of a data scientist involves tasks such as data collection, data cleaning, exploratory data analysis, feature engineering, and building predictive or prescriptive models. They work closely with stakeholders to understand business needs, formulate data-driven strategies, and communicate findings effectively to support decision-making processes.
Data scientists possess strong analytical and problem-solving skills, as well as a deep understanding of statistical concepts and programming languages such as Python or R. They are proficient in data manipulation, data visualization, and machine learning techniques.
In addition to technical skills, data scientists possess strong communication and storytelling abilities. They can translate complex data findings into actionable insights and effectively communicate them to both technical and non-technical audiences.
Data scientists play a crucial role in various industries, including finance, healthcare, marketing, technology, and more. They help organizations make informed decisions, optimize processes, identify new opportunities, and solve complex problems by harnessing the power of data.
Some topics you will find in the NumPy exercises:
working with numpy arrays
generating numpy arrays
generating numpy arrays with random values
iterating through arrays
dealing with missing values
working with matrices
computing basic array statistics
image as an array
determinant of the matrix
eigenvalues and eignevectors
working with polynomials
working with dates
working with strings in array
solving systems of equations
Some topics you will find in the Pandas exercises:
working with Series
working with DatetimeIndex
working with DataFrames
working with different data types in DataFrames
working with indexes
working with missing values
calculating cumulative statistics
working with duplicate values
preparing data to machine learning models
working with csv and json filles
Topics you will find in the Scikit-Learn exercises:
preparing data to machine learning models
working with missing values, SimpleImputer class
classification, regression, clustering
splitting data into train and test set
MAE - Mean Absolute Error
MSE - Mean Squared Error
dimensionality reduction, PCA analysis
Who this course is for:
- data scientists or analysts who want to learn and leverage Python libraries such as NumPy, Pandas, and Scikit-Learn for data manipulation, analysis, and machine learning tasks
- students or individuals pursuing a career in data science or data analysis who need a strong foundation in using Python for data processing and analysis
- programmers or developers who are new to data science and want to learn how to use Python libraries like NumPy, Pandas, and Scikit-Learn for data manipulation and machine learning tasks
- professionals working with large datasets or involved in data analysis projects who want to enhance their skills in utilizing Python libraries for efficient data processing, exploration, and modeling
- Python developers interested in expanding their knowledge of data science and machine learning techniques and want to learn how to use relevant Python libraries for these tasks
- self-learners or enthusiasts interested in data science and want to develop their Python skills specifically for data manipulation, analysis, and machine learning tasks
Python Developer/AI Enthusiast/Data Scientist/Stockbroker
Enthusiast of new technologies, particularly in the areas of artificial intelligence, the Python language, big data and cloud solutions. Graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science and Big Data specialization. Master's degree graduate in Financial and Actuarial Mathematics at the Faculty of Mathematics and Computer Science at the University of Lodz. Former PhD student at the faculty of mathematics. Since 2015, a licensed Securities Broker with the right to provide investment advisory services (license number 3073). Lecturer at the GPW Foundation, conducting training for investors in the field of technical analysis, behavioral finance, and principles of managing a portfolio of financial instruments.
Founder at e-smartdata
Data Scientist, Securities Broker
Jestem miłośnikiem nowych technologii, szczególnie w obszarze sztucznej inteligencji, języka Python big data oraz rozwiązań chmurowych. Posiadam stopień absolwenta podyplomowych studiów na kierunku Informatyka, specjalizacja Big Data w Polsko-Japońskiej Akademii Technik Komputerowych oraz magistra z Matematyki Finansowej i Aktuarialnej na wydziale Matematyki i Informatyki Uniwersytetu Łódzkiego. Od 2015 roku posiadam licencję Maklera Papierów Wartościowych z uprawnieniami do czynności doradztwa inwestycyjnego (nr 3073). Jestem również wykładowcą w Fundacji GPW prowadzącym szkolenia dla inwestorów z zakresu analizy technicznej, finansów behawioralnych i zasad zarządzania portfelem instrumentów finansowych. Mam doświadczenie w prowadzeniu zajęć dydaktycznych na wyższej uczelni z przedmiotów związanych z rachunkiem prawdopodobieństwa i statystyką. Moje główne obszary zainteresowań to język Python, sztuczna inteligencja, web development oraz rynki finansowe.
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