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About This Course
- Data Manipulation in Python: Master Python, NumPy & Pandas Course Overview
This Data Manipulation in Python: Master Python, NumPy & Pandas course provides practical knowledge of data handling, analysis, and processing using Python along with NumPy and Pandas. The course is designed to help learners efficiently organize, clean, transform, and analyze datasets for data science, business analytics, and machine learning applications.
Learners will explore Python fundamentals, arrays, data structures, indexing, filtering, aggregation, statistical analysis, and data visualization basics. The course also covers NumPy operations, Pandas DataFrames, data cleaning techniques, file handling, merging datasets, and exploratory data analysis methods. Students will understand how data professionals manipulate and process structured data to generate insights and support decision-making processes.
By completing this course, learners will gain practical programming and analytical skills for modern data-driven industries. It strengthens logical thinking, problem-solving, and computational analysis abilities. This training prepares individuals for careers in data analysis, data science, business intelligence, machine learning, and software development sectors.
- What You Will Learn Data Manipulation in Python: Master Python, NumPy & Pandas Course
- Understand Python data manipulation fundamentals clearly
- Work with NumPy arrays efficiently professionally
- Analyze datasets using Pandas DataFrames effectively
- Clean and transform structured data accurately
- Perform filtering and aggregation operations successfully
- Handle files and dataset imports confidently
- Apply exploratory data analysis techniques properly
- Improve problem-solving and analytical programming skills
- Why Should You Choose This Data Manipulation in Python: Master Python, NumPy & Pandas Course?
This course provides practical data manipulation training, helping learners process, analyze, and organize datasets using Python, NumPy, and Pandas for modern analytical and data-driven environments effectively and professionally.
It also strengthens programming and analytical thinking abilities. Learners gain hands-on experience with industry-standard data tools widely used in data science, machine learning, and business analytics sectors
- Key Topics Covered
- Introduction to Python programming
- NumPy arrays and numerical operations
- Pandas DataFrames and data structures
- Data cleaning and transformation techniques
- Filtering, sorting, and aggregation methods
- File handling and dataset importing
- Exploratory data analysis fundamentals
- Statistical operations and data insights
- Who Should Take This Course?
This course is ideal for beginners, aspiring data analysts, students, and programming enthusiasts who want to learn Python-based data manipulation techniques. It is suitable for learners interested in data science, analytics, and machine learning.
It is also beneficial for software developers, researchers, and business professionals who want to improve data processing and analytical capabilities. Anyone interested in working with structured datasets will benefit from this course.
- Entry Requirements
There are no strict academic requirements for this course. However, learners should have basic computer skills and an interest in programming or data analysis. Access to a computer with internet connection and Python installed is recommended for completing coding exercises and data projects successfully.
- Why Should You Learn with Imperial Academy
- Offers high-quality, industry-focused online courses
- Builds practical, job-ready skills across multiple sectors
- Provides flexible learning accessible anytime, anywhere
- Supports learners with structured and easy-to-follow content
- Enhances career opportunities and professional development
- Improves confidence through guided learning experience
- Career Prospects
- Data Analyst (£30,000 – £70,000)
- Junior Data Scientist (£35,000 – £80,000)
- Business Intelligence Analyst (£35,000 – £75,000)
- Python Developer (£35,000 – £75,000)
- Machine Learning Assistant (£38,000 – £85,000)
- Data Engineer (£40,000 – £90,000)
Course Curriculum
-
Welcome to the course!
00:01:00 -
Introduction to Python
00:01:00
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Course Materials
-
Setting up Python
00:02:00
-
What is Jupyter?
00:01:00
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Anaconda Installation: Windows, Mac & Ubuntu
00:04:00
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How to implement Python in Jupyter?
00:01:00
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Managing Directories in Jupyter Notebook
00:03:00
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Input/Output
00:02:00
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Working with different datatypes
00:01:00
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Variables
00:02:00
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Arithmetic Operators
00:02:00
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Comparison Operators
00:01:00
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Logical Operators
00:03:00
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Conditional statements
00:02:00
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Loops
00:04:00
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Sequences: Lists
00:03:00
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Sequences: Dictionaries
00:03:00
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Sequences: Tuples
00:01:00
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Functions: Built-in Functions
00:01:00
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Functions: User-defined Functions
00:04:00
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Installing Libraries
00:01:00
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Importing Libraries
00:02:00
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Pandas Library for Data Science
00:01:00
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NumPy Library for Data Science
00:01:00
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Pandas vs NumPy
00:01:00
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Matplotlib Library for Data Science
00:01:00
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Seaborn Library for Data Science
00:01:00
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Introduction to NumPy arrays
00:01:00
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Creating NumPy arrays
00:06:00
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Indexing NumPy arrays
00:06:00
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Array shape
00:01:00
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Iterating Over NumPy Arrays
00:05:00
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Basic NumPy arrays: zeros()
00:02:00
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Basic NumPy arrays: ones()
00:01:00
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Basic NumPy arrays: full()
00:01:00
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Adding a scalar
00:02:00
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Subtracting a scalar
00:01:00
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Multiplying by a scalar
00:01:00
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Dividing by a scalar
00:01:00
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Raise to a power
00:01:00
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Transpose
00:01:00
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Element wise addition
00:02:00
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Element wise subtraction
00:01:00
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Element wise multiplication
00:01:00
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Element wise division
00:01:00
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Matrix multiplication
00:02:00
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Statistics
00:03:00
-
What is a Python Pandas DataFrame?
00:01:00
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What is a Python Pandas Series?
00:01:00
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DataFrame vs Series
00:01:00
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Creating a DataFrame using lists
00:03:00
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Creating a DataFrame using a dictionary
00:01:00
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Loading CSV data into python
00:02:00
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Changing the Index Column
00:01:00
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Inplace
00:01:00
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Examining the DataFrame: Head & Tail
00:01:00
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Statistical summary of the DataFrame
00:01:00
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Slicing rows using bracket operators
00:01:00
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Indexing columns using bracket operators
00:01:00
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Boolean list
00:01:00
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Filtering Rows
00:01:00
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Filtering rows using & and | operators
00:02:00
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Filtering data using loc()
00:04:00
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Filtering data using iloc()
00:02:00
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Adding and deleting rows and columns
00:03:00
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Sorting Values
00:02:00
-
Exporting and saving pandas DataFrames
00:02:00
-
Concatenating DataFrames
00:01:00
-
groupby()
00:03:00
-
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
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Mean-based anomaly detection
00:03:00
-
Z-score-based Anomaly Detection
00:03:00
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Interquartile Range for Anomaly Detection
00:05:00
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Dealing with missing values
00:06:00
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Regular Expressions
00:07:00
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Feature Scaling
00:03:00
-
Introduction
00:01:00
-
Setting Up Matplotlib
00:01:00
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Plotting Line Plots using Matplotlib
00:02:00
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Plotting Line Plots using Matplotlib
00:02:00
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Plotting Histograms
00:01:00
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Plotting Bar Charts
00:02:00
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Plotting Pie Charts
00:03:00
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Plotting Scatter Plots
00:06:00
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Plotting Log Plots
00:01:00
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Plotting Polar Plots
00:02:00
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Handling Dates
00:01:00
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Creating multiple subplots in one figure
00:03:00
-
Introduction
00:01:00
-
What is Exploratory Data Analysis?
00:01:00
-
Univariate Analysis
00:02:00
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Univariate Analysis: Continuous Data
00:06:00
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Univariate Analysis: Categorical Data
00:02:00
-
Bivariate analysis: Continuous & Continuous
00:05:00
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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
-
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
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