Data Manipulation in Python: Master Python, Numpy & Pandas

Data Manipulation in Python: Master Python, Numpy & Pandas Overview

Are you looking to gain a new in-demand skill from the comfort of your home? Well, look no further; you’ve come to the right place!

Our easy-to-follow Data Manipulation in Python: Master Python, Numpy & Pandas will provide you with all the deep knowledge and insight you need to know about this topic. This comprehensive course has been broken down into several manageable modules, which we believe will assist you to easily grasp each concept – from the fundamental to the more advanced aspects of the course.

Learn the most in-demand skills from the safety & comfort of your home. Enjoy the freedom to learn at your own comfortable pace and prepare yourself for the market of the future.

Learning Outcomes

Whether you are an aspiring professional or a complete beginner, this course will improve your expertise and boost your CV with key skills and an accredited certification attesting to your knowledge.

Entry Requirements

This course is available to all learners of all academic backgrounds.
Learners should be aged 16 or over to undertake the qualification.
Some basic understanding of the English language and numeracy.

Why Choose Us

Affordable, engaging & high-quality e-learning study materials;
Tutorial videos/materials from the industry-leading experts;
Study in a user-friendly, advanced online learning platform;
Efficient exam systems for the assessment and instant result;
The UK & internationally recognised accredited
Access to course content on mobile, tablet or desktop from anywhere, anytime;
The benefit of career advancement opportunities;
24/7 student support via email.

Career Path

Develop Your Career Plan is a useful qualification to possess and would be beneficial for any related profession or industry.

Course Curriculum

Python Quick Refresher (Optional)
	Welcome to the course!	00:01:00
	Introduction to Python	00:02:00
	Course Materials	00:00:00
	Setting up Python	00:02:00
	What is Jupyter?	00:01:00
	Anaconda Installation: Windows, Mac & Ubuntu	00:05:00
	How to implement Python in Jupyter?	00:04:00
	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	00:03:00
	Loops	00:04:00
	Sequences: Lists	00:03:00
	Sequences: Dictionaries	00:05:00
	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	00:03:00
	NumPy Library for Data Science	00:00:00
	Pandas vs NumPy	00:02:00
	Matplotlib Library for Data Science	00:01:00
	Seaborn Library for Data Science	00:03:00
Fundamental NumPy Properties
	Introduction to NumPy arrays	00:01:00
	Creating NumPy arrays	00:06:00
	Indexing NumPy arrays	00:07:00
	Array shape	00:02:00
	Iterating Over NumPy Arrays	00:06:00
Mathematics for Data Science
	Basic NumPy arrays: zeros()	00:04:00
	Basic NumPy arrays: ones()	00:04:00
	Basic NumPy arrays: full()	00:01:00
	Adding a scalar	00:03:00
	Subtracting a scalar	00:01:00
	Multiplying by a scalar	00:01:00
	Dividing by a scalar	00:01:00
	Raise to a power	00:03:00
	Transpose	00:03:00
	Element wise addition	00:02:00
	Element wise subtraction	00:02:00
	Element wise multiplication	00:03:00
	Element wise division	00:01:00
	Matrix multiplication	00:04:00
	Statistics	00:04:00
Python Pandas DataFrames & Series
	What is a Python Pandas DataFrame?	00:02:00
	What is a Python Pandas Series?	00:04:00
	DataFrame vs Series	00:01:00
	Creating a DataFrame using lists	00:04:00
	Creating a DataFrame using a dictionary	00:01:00
	Loading CSV data into python	00:02:00
	Changing the Index Column	00:02:00
	Inplace	00:03:00
	Examining the DataFrame: Head & Tail	00:01:00
	Statistical summary of the DataFrame	00:02:00
	Slicing rows using bracket operators	00:01:00
	Indexing columns using bracket operators	00:01:00
	Boolean list	00:01:00
	Filtering Rows	00:02:00
	Filtering rows using & and \| operators	00:04:00
	Filtering data using loc()	00:06:00
	Filtering data using iloc()	00:04:00
	Adding and deleting rows and columns	00:05:00
	Sorting Values	00:03:00
	Exporting and saving pandas DataFrames	00:02:00
	Concatenating DataFrames	00:02:00
	groupby()	00:05:00
Data Cleaning
	Introduction to Data Cleaning	00:03:00
	Quality of Data	00:03:00
	Examples of Anomalies	00:02:00
	Median-based Anomaly Detection	00:04:00
	Mean-based anomaly detection	00:04:00
	Z-score-based Anomaly Detection	00:05:00
	Interquartile Range for Anomaly Detection	00:05:00
	Dealing with missing values	00:06:00
	Regular Expressions	00:08:00
	Feature Scaling	00:03:00
Data Visualization using Python
	Introduction	00:01:00
	Setting Up Matplotlib	00:00:00
	Plotting Line Plots using Matplotlib	00:04:00
	Title, Labels & Legend	00:08:00
	Plotting Histograms	00:01:00
	Plotting Bar Charts	00:02:00
	Plotting Pie Charts	00:03:00
	Plotting Scatter Plots	00:07:00
	Plotting Log Plots	00:01:00
	Plotting Polar Plots	00:02:00
	Handling Dates	00:02:00
	Creating multiple subplots in one figure	00:03:00
Exploratory Data Analysis
	Introduction	00:02:00
	What is Exploratory Data Analysis?	00:03:00
	Univariate Analysis	00:03:00
	Univariate Analysis: Continuous Data	00:06:00
	Univariate Analysis: Categorical Data	00:04:00
	Bivariate analysis: Continuous & Continuous	00:05:00
	Bivariate analysis: Categorical & Categorical	00:05:00
	Bivariate analysis: Continuous & Categorical	00:02:00
	Detecting Outliers	00:08:00
	Categorical Variable Transformation	00:04:00
Time Series in Python
	Introduction to Time Series	00:02:00
	Getting Stock Data using Yfinance	00:04:00
	Converting a Dataset into Time Series	00:06:00
	Working with Time Series	00:06:00
	Time Series Data Visualization with Python	00:03:00