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Data Manipulation in Python: Master Python, Numpy & Pandas
522 Students (35 Reviews)
Register on the Data Manipulation in Python: Master Python, Numpy & Pandas today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job.
GET THIS COURSE AND 3000+ OTHERS FOR ONLY £49 PER YEAR. FIND OUT MORE
Overview
The Data Manipulation in Python: Master Python, NumPy & Pandas course introduces learners to the essential skills needed to work with, organise, and analyse data using Python. It focuses on widely used tools and techniques that help make large or complex datasets easier to understand and manage.
Learners will explore Python fundamentals alongside NumPy and Pandas, including data types, arrays, DataFrames, data selection, filtering, sorting, cleaning, and transformation. The course also introduces techniques for handling missing information, combining datasets, and preparing data for further analysis.
Delivered through flexible online learning, the course allows learners to study at their own pace using an internet-enabled computer, tablet, or smartphone. It is suitable for beginners, aspiring data analysts, Python learners, students, and professionals who want to develop their data manipulation skills and build a foundation for further work in data analysis.
Learning Outcomes
- Understand Python fundamentals for working with data.
- Use NumPy arrays for efficient data operations.
- Create and manage Pandas DataFrames effectively.
- Filter, sort, and select relevant dataset information.
- Clean datasets and handle missing values.
- Combine and transform data from different sources.
- Prepare datasets for further analysis and visualisation.
Why choose this course
- Affordable, Accessible, informative and well-structured study materials
- Earn a globally recognised accredited qualification
- Study in your own time, at your own pace, through any device
- Benefit from feedback through mock exams and multiple-choice assessments
- Excellent career advancement/career switch opportunities
- Get 24/7 help or advice from our email and live chat teams
Certification
Certificate of Achievement
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.
Who Is This Course For
This course is suitable for beginners, students, Python learners, aspiring data analysts, business professionals, researchers, and anyone interested in working with data. It can also benefit professionals who want to improve their understanding of Python-based data manipulation and prepare for further data-related learning.
Requirements
There are no formal academic entry requirements for this course. Learners should have basic computer skills and a reasonable understanding of English. Some familiarity with Python can be helpful, but beginners can also use the course to build their programming and data skills.
Career path
- Data Analyst
- Business Intelligence Analyst
- Data Engineer
- Research Analyst
- Reporting Analyst
Course Curriculum
| Python Quick Refresher (Optional) | |||
| Welcome to the course! | 00:01:00 | ||
| Introduction to Python | 00:01:00 | ||
| Course Materials | 00:00:00 | ||
| Setting up Python | 00:02:00 | ||
| What is Jupyter? | 00:01:00 | ||
| Anaconda Installation: Windows, Mac & Ubuntu | 00:04:00 | ||
| How to implement Python in Jupyter? | 00:01:00 | ||
| Managing Directories in Jupyter Notebook | 00:03:00 | ||
| Input/Output | 00:02:00 | ||
| Working with different datatypes | 00:01:00 | ||
| Variables | 00:02:00 | ||
| Arithmetic Operators | 00:02:00 | ||
| Comparison Operators | 00:01:00 | ||
| Logical Operators | 00:03:00 | ||
| Conditional statements | 00:02:00 | ||
| Loops | 00:04:00 | ||
| Sequences: Lists | 00:03:00 | ||
| Sequences: Dictionaries | 00:03:00 | ||
| Sequences: Tuples | 00:01:00 | ||
| Functions: Built-in Functions | 00:01:00 | ||
| Functions: User-defined Functions | 00:03:00 | ||
| Essential Python Libraries for Data Science | |||
| Installing Libraries | 00:01:00 | ||
| Importing Libraries | 00:02:00 | ||
| Pandas Library for Data Science | 00:01:00 | ||
| NumPy Library for Data Science | 00:01:00 | ||
| Pandas vs NumPy | 00:01:00 | ||
| Matplotlib Library for Data Science | 00:01:00 | ||
| Seaborn Library for Data Science | 00:01:00 | ||
| Fundamental NumPy Properties | |||
| Introduction to NumPy arrays | 00:01:00 | ||
| Creating NumPy arrays | 00:06:00 | ||
| Indexing NumPy arrays | 00:06:00 | ||
| Array shape | 00:01:00 | ||
| Iterating Over NumPy Arrays | 00:05:00 | ||
| Mathematics for Data Science | |||
| Basic NumPy arrays: zeros() | 00:02:00 | ||
| Basic NumPy arrays: ones() | 00:01:00 | ||
| Basic NumPy arrays: full() | 00:01:00 | ||
| Adding a scalar | 00:02:00 | ||
| Subtracting a scalar | 00:01:00 | ||
| Multiplying by a scalar | 00:01:00 | ||
| Dividing by a scalar | 00:01:00 | ||
| Raise to a power | 00:01:00 | ||
| Transpose | 00:01:00 | ||
| Element wise addition | 00:02:00 | ||
| Element wise subtraction | 00:01:00 | ||
| Element wise multiplication | 00:01:00 | ||
| Element wise division | 00:01:00 | ||
| Matrix multiplication | 00:02:00 | ||
| Statistics | 00:03:00 | ||
| Python Pandas DataFrames & Series | |||
| What is a Python Pandas DataFrame? | 00:01:00 | ||
| What is a Python Pandas Series? | 00:01:00 | ||
| DataFrame vs Series | 00:01:00 | ||
| Creating a DataFrame using lists | 00:03:00 | ||
| Creating a DataFrame using a dictionary | 00:01:00 | ||
| Loading CSV data into python | 00:02:00 | ||
| Changing the Index Column | 00:01:00 | ||
| Inplace | 00:01:00 | ||
| Examining the DataFrame: Head & Tail | 00:01:00 | ||
| Statistical summary of the DataFrame | 00:01:00 | ||
| Slicing rows using bracket operators | 00:01:00 | ||
| Indexing columns using bracket operators | 00:01:00 | ||
| Boolean list | 00:01:00 | ||
| Filtering Rows | 00:01:00 | ||
| Filtering rows using & and | operators | 00:02:00 | ||
| Filtering data using loc() | 00:04:00 | ||
| Filtering data using iloc() | 00:02:00 | ||
| Adding and deleting rows and columns | 00:03:00 | ||
| Sorting Values | 00:02:00 | ||
| Exporting and saving pandas DataFrames | 00:02:00 | ||
| Concatenating DataFrames | 00:01:00 | ||
| groupby() | 00:03:00 | ||
| Data Cleaning | |||
| Introduction to Data Cleaning | 00:01:00 | ||
| Quality of Data | 00:01:00 | ||
| Examples of Anomalies | 00:01:00 | ||
| Median-based Anomaly Detection | 00:03:00 | ||
| Mean-based anomaly detection | 00:03:00 | ||
| Z-score-based Anomaly Detection | 00:03:00 | ||
| Interquartile Range for Anomaly Detection | 00:05:00 | ||
| Dealing with missing values | 00:06:00 | ||
| Regular Expressions | 00:07:00 | ||
| Feature Scaling | 00:03:00 | ||
| Data Visualization using Python | |||
| Introduction | 00:01:00 | ||
| Setting Up Matplotlib | 00:01:00 | ||
| Plotting Line Plots using Matplotlib | 00:02:00 | ||
| Title, Labels & Legend | 00:07:00 | ||
| Plotting Histograms | 00:01:00 | ||
| Plotting Bar Charts | 00:02:00 | ||
| Plotting Pie Charts | 00:03:00 | ||
| Plotting Scatter Plots | 00:06:00 | ||
| Plotting Log Plots | 00:01:00 | ||
| Plotting Polar Plots | 00:02:00 | ||
| Handling Dates | 00:01:00 | ||
| Creating multiple subplots in one figure | 00:04:00 | ||
| Exploratory Data Analysis | |||
| Introduction | 00:01:00 | ||
| What is Exploratory Data Analysis? | 00:01:00 | ||
| Univariate Analysis | 00:02:00 | ||
| Univariate Analysis: Continuous Data | 00:06:00 | ||
| Univariate Analysis: Categorical Data | 00:02:00 | ||
| Bivariate analysis: Continuous & Categorical | 00:02:00 | ||
| Bivariate analysis: Categorical & Categorical | 00:03:00 | ||
| Bivariate analysis: Continuous & Categorical | 00:02:00 | ||
| Detecting Outliers | 00:06:00 | ||
| Categorical Variable Transformation | 00:04:00 | ||
| Time Series in Python | |||
| Introduction to Time Series | 00:02:00 | ||
| Getting Stock Data using Yfinance | 00:03:00 | ||
| Converting a Dataset into Time Series | 00:04:00 | ||
| Working with Time Series | 00:04:00 | ||
| Time Series Data Visualization with Python | 00:03:00 | ||
| Order your Certificates & Transcripts | |||
| Order your Certificates & Transcripts | 00:00:00 | ||
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Frequently asked questions
Can’t find the anwser you’re looking for ? Reach out to customer support team.
Data manipulation in Python involves organising, cleaning, transforming, and preparing data for analysis. Python provides several tools that make it easier to work with datasets and perform common data-related tasks efficiently.
NumPy is a Python library commonly used for numerical operations and array-based data. Pandas is designed for working with structured data and provides useful features such as DataFrames, filtering, sorting, cleaning, and data transformation.
Yes. The course is suitable for beginners who want to develop their Python and data manipulation knowledge. Basic computer skills are recommended, while previous programming experience can be helpful but is not essential.
You will learn how to work with Pandas DataFrames, select and filter information, sort data, handle missing values, combine datasets, and transform data into a more useful format for analysis.
NumPy provides efficient tools for numerical calculations and array-based operations. It is widely used within the Python data ecosystem and can support tasks involving numerical datasets and mathematical operations.
Previous Python knowledge can be useful, but it is not essential for getting started. The course introduces relevant Python concepts while focusing on how Python, NumPy, and Pandas can be used for data manipulation.
Yes. The course is delivered online, allowing you to study at a convenient time and location. You can access the learning materials using an internet-enabled computer, tablet, or smartphone.
Python, NumPy, and Pandas can be used with many types of structured and numerical data. Depending on the dataset, you can organise, filter, clean, transform, and prepare information for further analysis.
Yes. Data cleaning is an important part of the course. You will explore techniques for handling missing information, organising datasets, correcting data issues, and preparing information for further analysis.
The course can help you develop foundational knowledge of Python-based data manipulation. You can build on these skills through further study, data projects, analytical tools, and additional qualifications.