Analyzing data with Python is an essential skill for Data Scientists and Data Analysts. This course will take you from the basics of data analysis with Python to building and evaluating data models.
Offered By
IBM Skills NetworkAbout this Course
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Beginner Level
You should have a working knowledge of Python and Jupyter Notebooks.
Approx. 14 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Turkish, English, Spanish, Persian
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Try Coursera for BusinessWhat you will learn
Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data
Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy
Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines
Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making
Skills you will gain
- Predictive Modelling
- Python Programming
- Data Analysis
- Data Visualization (DataViz)
- Model Selection
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Beginner Level
You should have a working knowledge of Python and Jupyter Notebooks.
Approx. 14 hours to complete
English
Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, Turkish, English, Spanish, Persian
Could your company benefit from training employees on in-demand skills?
Try Coursera for Business
Offered by
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.
Syllabus - What you will learn from this course
2 hours to complete
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 hours to complete
6 videos (Total 20 min), 1 reading, 6 quizzes
2 hours to complete
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 hours to complete
6 videos (Total 19 min), 1 reading, 6 quizzes
2 hours to complete
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 hours to complete
6 videos (Total 20 min), 1 reading, 6 quizzes
2 hours to complete
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 hours to complete
6 videos (Total 27 min), 1 reading, 6 quizzes
Reviews
- 5 stars75.69%
- 4 stars18.78%
- 3 stars3.86%
- 2 stars0.91%
- 1 star0.73%
TOP REVIEWS FROM DATA ANALYSIS WITH PYTHON
by SCAug 11, 2020
Thank you very much to the instructors. I liked the course but it could have been better designed. More exercises ascending from easy to hard & real and teaching quiz questions would make it perfect.
by WZNov 7, 2020
A great introduction to fitting data in python.
The core principles and measures are well explained.
The only slight minus point: No information about the interpretation of fitting parameters.
by RJOct 16, 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.
by RPApr 19, 2019
perfect for beginner level. all the concepts with code and parameter wise have been explained excellently. overall best course in making anyone eager to learn from basics to handle advances with ease.
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