Overview
This comprehensive course on Data Manipulation in Python is designed to equip learners with the skills needed to handle, analyze, and transform data using Python and its powerful libraries, Numpy and Pandas. It focuses on building a strong foundation in Python programming while introducing essential data manipulation techniques required in data science. The course covers a wide range of topics, from data cleaning and preparation to performing complex transformations and aggregations. With practical, hands-on exercises, learners gain experience working with real datasets, preparing them to tackle data-driven challenges across various domains. This course is suitable for beginners and those with some Python experience who wish to deepen their understanding of data manipulation, providing skills that can be directly applied in fields like finance, healthcare, e-commerce, and beyond.
Learning Outcomes
By the end of this course, you will be able to:
- Master Python fundamentals for data manipulation.
- Learn essential Numpy functions for numerical operations.
- Perform data cleaning and preparation with Pandas.
- Conduct data aggregation and analysis efficiently.
- Handle and transform large datasets effectively.
- Gain practical experience with real-world datasets.
- Develop problem-solving skills for data-driven roles.
How will I get my certificate?
You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.
Who is This course for?
This course is ideal for beginners in data science, Python enthusiasts, or professionals in fields like finance, marketing, and business analytics. It is also suited for anyone looking to enhance their data manipulation skills for career growth.
Requirements
Basic knowledge of Python programming is recommended but not mandatory. Learners should have access to a computer with an internet connection, and familiarity with basic math concepts would be beneficial. No prior experience with data manipulation or data science tools is required.
Career Path
- Data Analyst
- Data Scientist
- Business Intelligence Analyst
- Python Developer
- Financial Analyst
Course Curriculum
- 9 sections
- 105 lectures
- 3 hours, 52 minutes total length
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Welcome to the course! 00:01:00 -
Introduction to Python 00:01:00
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Setting up Python 00:02:00
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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
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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
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Exporting and saving pandas DataFrames 00:02:00
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Concatenating DataFrames 00:01:00
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groupby() 00:03:00
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Introduction to Data Cleaning 00:01:00
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Quality of Data 00:01:00
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Examples of Anomalies 00:01:00
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Median-based Anomaly Detection 00:03:00
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Mean-based anomaly detection 00:03:00
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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
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Introduction 00:01:00
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Setting Up Matplotlib 00:01:00
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Plotting Line Plots using Matplotlib 00:02:00
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Title, Labels & Legend 00:07:00
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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
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Introduction 00:01:00
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What is Exploratory Data Analysis? 00:01:00
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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
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Bivariate analysis: Categorical & Categorical 00:03:00
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Bivariate analysis: Continuous & Categorical 00:02:00
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Detecting Outliers 00:06:00
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Categorical Variable Transformation 00:04:00
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Introduction to Time Series 00:02:00
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Getting Stock Data using Yfinance 00:03:00
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Converting a Dataset into Time Series 00:04:00
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Working with Time Series 00:04:00
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Time Series Data Visualization with Python 00:03:00
Frequently Bought Together
4.7 course rating - 18 reviews
£29
£425
This course includes:
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Duration:3 hours, 52 minutes
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Access:Lifetime access
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Units:105
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Assignments:No
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Quizzes:No
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Certificate:Yes
