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Python for Data Science – NumPy, Pandas & Scikit-Learn

Improve your data science skills and solve over 330 exercises in Python, NumPy, Pandas and Scikit-Learn!
(10 reviews)
5,023 students
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Platform: Udemy
Video: 1h 28m
Language: English
Next start: On Demand

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Detailed Analysis

CourseMarks Score®

9.3 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

10.0 / 10
This course was last updated on 6/2022.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

8.2 / 10
We analyzed factors such as the rating (4.6/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.0 / 10
Video Score: 7.8 / 10
The course includes 1h 28m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
Detail Score: 9.4 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.9 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

338 articles.
0 resource.
331 exercises.
0 test.

Table of contents


Welcome to the Python for Data Science – NumPy, Pandas & Scikit-Learn course, where you can test your Python programming skills in data science, specifically in NumPy, Pandas and Scikit-Learn.

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
•reading/writing files
•joining arrays
•reshaping arrays
•computing basic array statistics
•sorting arrays
•filtering arrays
•image as an array
•linear algebra
•matrix multiplication
•determinant of the matrix
•eigenvalues and eignevectors
•inverse matrix
•shuffling arrays
•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
•reading/writing files
•working with different data types in DataFrames
•working with indexes
•working with missing values
•filtering data
•sorting data
•grouping data
•mapping columns
•computing correlation
•concatenating DataFrames
•calculating cumulative statistics
•working with duplicate values
•preparing data to machine learning models
•dummy encoding
•working with csv and json filles
•merging DataFrames
•pivot tables

Topics you will find in the Scikit-Learn exercises:
•preparing data to machine learning models
•working with missing values, SimpleImputer class
•classification, regression, clustering
•feature extraction
•PolynomialFeatures class
•LabelEncoder class
•OneHotEncoder class
•StandardScaler class
•dummy encoding
•splitting data into train and test set
•LogisticRegression class
•confusion matrix
•classification report
•LinearRegression class
•MAE – Mean Absolute Error
•MSE – Mean Squared Error
•sigmoid() function
•accuracy score
•DecisionTreeClassifier class
•GridSearchCV class
•RandomForestClassifier class
•CountVectorizer class
•TfidfVectorizer class
•KMeans class
•AgglomerativeClustering class
•HierarchicalClustering class
•DBSCAN class
•dimensionality reduction, PCA analysis
•Association Rules
•LocalOutlierFactor class
•IsolationForest class
•KNeighborsClassifier class
•MultinomialNB class
•GradientBoostingRegressor class

This course is designed for people who have basic knowledge in Python, NumPy, Pandas and Scikit-Learn packages. It consists of 330 exercises with solutions. This is a great test for people who are learning the Python language and data science and are looking for new challenges. Exercises are also a good test before the interview. Many popular topics were covered in this course.

If you’re wondering if it’s worth taking a step towards Python, don’t hesitate any longer and take the challenge today.

You will 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

This course is for

• everyone who wants to learn by doing
• everyone who wants to improve Python programming skills
• everyone who wants to improve data science skills
• everyone who wants to prepare for an interview

How much does the Python for Data Science - NumPy, Pandas & Scikit-Learn course cost? Is it worth it?

The course costs $14.99. And currently there is a 25% discount on the original price of the course, which was $19.99. So you save $5 if you enroll the course now.

Does the Python for Data Science - NumPy, Pandas & Scikit-Learn course have a money back guarantee or refund policy?

YES, Python for Data Science – NumPy, Pandas & Scikit-Learn has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Python for Data Science - NumPy, Pandas & Scikit-Learn course, but there is a $5 discount from the original price ($19.99). So the current price is just $14.99.

Who is the instructor? Is Paweł Krakowiak a SCAM or a TRUSTED instructor?

Paweł Krakowiak has created 67 courses that got 3,964 reviews which are generally positive. Paweł Krakowiak has taught 167,420 students and received a 4.6 average review out of 3,964 reviews. Depending on the information available, Paweł Krakowiak is a TRUSTED instructor.
Python Developer/Data Scientist/Stockbroker
Python Developer/Data Scientist/Stockbroker
Founder at e-smartdata[.]org. Big fan of new technologies!
Graduate of postgraduate studies at the Polish-Japanese Academy of Information Technology in the field of Computer Science and Big Data specialization.
Graduate of MA studies 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.
Stockbroker license holder (no 3073).
Lecturer at the GPW Foundation (technical analysis, behavioral finance and portfolio management).
Data Scientist, Securities Broker
Założyciel platformy e-smartdata[.]orgMiłośnik nowych technologii, szczególnie w obszarze sztucznej inteligencji, języka Python oraz rozwiązań chmurowych.
Absolwent podyplomowych studiów na Polsko-Japońskiej Akademii Technik Komputerowych na kierunku Informatyka, spec. Big Data.
Absolwent studiów magisterskich z matematyki finansowej i aktuarialnej na wydziale Matematyki i Informatyki Uniwersytetu Łódzkiego.
Od 2015 roku posiadacz licencji Maklera Papierów Wartościowych z uprawnieniami do czynności doradztwa inwestycyjnego (nr 3073).
Wykładowca w Fundacji GPW prowadzący szkolenia dla inwestorów z zakresu analizy technicznej, finansów behawioralnych i zasad zarządzania portfelem instrumentów finansowych.
Z doświadczeniem w prowadzeniu zajęć dydaktycznych na wyższej uczelni z przedmiotów związanych z rachunkiem prawdopodobieństwa i statystyką.
Główne obszary zainteresowań to język Python, sztuczna inteligencja, web development oraz rynki finansowe.


CourseMarks Score®







Platform: Udemy
Video: 1h 28m
Language: English
Next start: On Demand

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