Advanced Techniques for Exploring Data Sets with Pandas
4.3( 3 REVIEWS ) Overview
Pandas is a popular Python library that is used for data analysis and data manipulation purposes. It makes working with data very easy and intuitive as it provides fast, flexible and expressive data frames for data analysis purposes with Python. Having a good grip on the advanced aspects of this library will prove worthwhile in your data science career, which is why we have developed this course to educate you on everything you need to know on Pandas to enhance your data analysis activities using it.
This course will start off by exploring how to read data from CSV based datasets, excel datasets and other kinds of datasets into pandas. You will then be guided on the techniques and methods that are used to select a subset of data in pandas, along with an insight into the various methods and techniques that can be used to sort and filter the imported data. You will also get to learn on how to use the “axis” parameter, multiple filter criteria and the string methods on pandas’ data.
This course will then enlighten you on how to manipulate, transform and reshape data that is read into pandas data frame as per your requirements using various methods and techniques. You will also be tutored on how to effectively visualize data by getting started with plots, control plot aesthetics, plot colour palettes and seaborn. By the end of this course, you will have an outstanding knowledge on the pandas library to excel in your data analysis and manipulation activities.
Why study at Global Edulink?
Global Edulink offers the most convenient path to gain recognised skills and training that will give you the opportunity to put into practice your knowledge and expertise in an IT or corporate environment. You can study at your own pace at Global Edulink and you will be provided with all the necessary material, tutorials, qualified course instructor, narrated e-learning modules and free resources which include Free CV writing pack, free career support and course demo to make your learning experience more enriching and rewarding.
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- Entry Requirements
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- Learners must be age 16 or over and should have basic understanding of the English Language, numeracy, literacy and ICT.
- A prior experience in Python programming will be advantageous
This certificate will enhance your qualifications for a number of jobs in data science. You can also use this certificate to prove your eligibility for the job promotion or salary increment put forth by your employer. Listed below are some of the jobs this certificate will benefit you in, along with the average UK salary per annum according to https://www.payscale.com/career-path-planner,
- Data analyst – £25,972 per annum
- Data scientist – £35,226 per annum
- Applications Architect – £59,869 per annum
- Statistician – £31,231 per annum
- Data Scientist – £35,226 per annum
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- Tutorials/materials from the industry leading experts.
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- The benefit of applying for TOTUM extra Discount Card.
- Recognised Accredited Qualification.
- Excellent customer service and administrative support
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Course Curriculum
| Getting Started | |||
| Online Training User Manual | 00:00:00 | ||
| E Certificate Download Guide | 00:00:00 | ||
| Section 1: Working With Different Kinds of Datasets | |||
| 1.1. The Course Overview | 00:00:00 | ||
| 1.2. Using Advanced Options While Reading Data from CSV Files | 00:00:00 | ||
| 1.3. Reading Data from Excel Files | 00:00:00 | ||
| 1.4. Reading Data from Some Other Popular Formats | 00:00:00 | ||
| Section 2: Data Selection | |||
| 2.1. Using Pandas Series Data Structure to Select a Subset of the Data | 00:00:00 | ||
| 2.2. Selecting Multiple Rows and Columns from a Pandas DataFrame | 00:00:00 | ||
| 2.3. Sorting a Pandas DataFrame or a Series | 00:00:00 | ||
| 2.4. Filtering Rows of a Pandas DataFrame by Column Value | 00:00:00 | ||
| 2.5. Applying Multiple Filter Criteria to a Pandas DataFrame | 00:00:00 | ||
| 2.6. Using the “axis” Parameter in Pandas | 00:00:00 | ||
| 2.7. Using String Methods in Pandas | 00:00:00 | ||
| 2.8. Changing the Data Type of a Pandas Series | 00:00:00 | ||
| Section 3: Manipulating, Transforming, and Reshaping Data | |||
| 3.1. Modifying a Pandas DataFrame “inplace” | 00:00:00 | ||
| 3.2. Using the “groupby” Method | 00:00:00 | ||
| 3.3. Handling Missing Values in Pandas | 00:00:00 | ||
| 3.4. Indexing in Pandas DataFrames | 00:00:00 | ||
| 3.5. Renaming Columns in a Pandas DataFrame | 00:00:00 | ||
| 3.6. Removing Columns from a Pandas DataFrame | 00:00:00 | ||
| 3.7. Working with Dates and Times Data | 00:00:00 | ||
| 3.8. Handling SettingWithCopyWarning | 00:00:00 | ||
| 3.9. Applying a Function to a Pandas Series or DataFrame | 00:00:00 | ||
| 3.10. Merging and Concatenating Multiple DataFrames into One | 00:00:00 | ||
| Section 4: Visualizing Data Like a Pro | |||
| 4.1. Controlling Plot Aesthetics | 00:00:00 | ||
| 4.2. Choosing the Colors for the Plots | 00:00:00 | ||
| 4.3. Plotting Categorical Data | 00:00:00 | ||
| 4.4. Plotting with Data Aware Grids | 00:00:00 | ||
| Mock Exam | |||
| Mock Exam : Advanced Techniques for Exploring Data Sets with Pandas | 00:40:00 | ||
| Exam | |||
| Exam : Advanced Techniques for Exploring Data Sets with Pandas | 00:40:00 | ||
Students feedback
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Kristoffer Polaha
You will learn how to use Pandas for data analysis and data manipulation. It is a very easy way to work with data and was useful to me in my job.
Sophia Mcdonald
Good course for data analysts. Highly recommended!
Jonathan Houghton
The course was on point on Pandas. I had previous working knowledge so I was quickly able to get up to speed with the course.