Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!
von
IBM Skills NetworkÃœber diesen Kurs
690.266 kürzliche Aufrufe
Flexible Fristen
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Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100Â % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Anfänger“
Ca. 14 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Italienisch, Vietnamesisch, Deutsch, Russisch, Türkisch, Englisch, Spanisch, Persisch
Was Sie lernen werden
Describe Python data acquisition and analysis techniques.
Analyze Python data using a dataset.
Identify three Python libraries and describe their uses.
Read data using Python's Pandas package.
Kompetenzen, die Sie erwerben
- Predictive Modelling
- Python Programming
- Data Analysis
- Data Visualization (DataViz)
- Model Selection
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100Â % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Anfänger“
Ca. 14 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Italienisch, Vietnamesisch, Deutsch, Russisch, Türkisch, Englisch, Spanisch, Persisch
von
IBM Skills Network
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Lehrplan - Was Sie in diesem Kurs lernen werden
2 Stunden zum Abschließen
Importing Datasets
In this module, you will learn how to understand data and learn about how to use the libraries in Python to help you import data from multiple sources. You will then learn how to perform some basic tasks to start exploring and analyzing the imported data set.
2 Stunden zum Abschließen
6 Videos (Gesamt 20 min), 1 Lektüre, 6 Quiz
2 Stunden zum Abschließen
Data Wrangling
In this module, you will learn how to perform some fundamental data wrangling tasks that, together, form the pre-processing phase of data analysis. These tasks include handling missing values in data, formatting data to standardize it and make it consistent, normalizing data, grouping data values into bins, and converting categorical variables into numerical quantitative variables.
2 Stunden zum Abschließen
6 Videos (Gesamt 19 min), 1 Lektüre, 6 Quiz
2 Stunden zum Abschließen
Exploratory Data Analysis
In this module, you will learn what is meant by exploratory data analysis, and you will learn how to perform computations on the data to calculate basic descriptive statistical information, such as mean, median, mode, and quartile values, and use that information to better understand the distribution of the data. You will learn about putting your data into groups to help you visualize the data better, you will learn how to use the Pearson correlation method to compare two continuous numerical variables, and you will learn how to use the Chi-square test to find the association between two categorical variables and how to interpret them.
2 Stunden zum Abschließen
6 Videos (Gesamt 20 min), 1 Lektüre, 6 Quiz
2 Stunden zum Abschließen
Model Development
In this module, you will learn how to define the explanatory variable and the response variable and understand the differences between the simple linear regression and multiple linear regression models. You will learn how to evaluate a model using visualization and learn about polynomial regression and pipelines. You will also learn how to interpret and use the R-squared and the mean square error measures to perform in-sample evaluations to numerically evaluate our model. And lastly, you will learn about prediction and decision making when determining if our model is correct.
2 Stunden zum Abschließen
6 Videos (Gesamt 27 min), 1 Lektüre, 6 Quiz
Bewertungen
- 5 stars75,69Â %
- 4 stars18,78Â %
- 3 stars3,86Â %
- 2 stars0,91Â %
- 1 star0,74Â %
Top-Bewertungen von DATENANALYSE MIT PYTHON
von BD22. Nov. 2019
Really interesting course, if one wants learn programming language. Well designed and structured. Only suggestion is, if the small videos contains example that be really great to understand it well
von CT13. Mai 2021
Good course, sometimes moves a bit fast in the final modules and the labs are quite tough but great course and would recommend to broaden your knowledge of coding, data analysis and visualisation
von AB12. Feb. 2020
Great introduction to data manipulation and analysis for common problems that arise in data science. Also allows you to gain a further understanding of Python syntax, specifically the pandas library.
von RJ16. Okt. 2018
It will be helpful if a video is added on:
1) how to store multiple results from different models in single dataframe
2) how to automate the process. More example needed on Grid and Pipeline.
Häufig gestellte Fragen
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