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; …

Data Manipulation in Python Master Python, Numpy & Pandas

Data Manipulation in Python: Master Python, Numpy & Pandas

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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

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