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Data Manipulation: Python, Numpy and Pandas Overview

Data Manipulation: Python, Numpy and Pandas Course is yet another “Teacher’s Choice” course from Teachers Training for a complete understanding of the fundamental topics. You are also entitled to exclusive tutor support and a professional CPD-accredited certificate in addition to the special discounted price for a limited time. Just like all our courses, this Data Manipulation: Python, Numpy and Pandas Course and its curriculum have also been designed by expert teachers so that teachers of tomorrow can learn from the best and equip themselves with all the necessary skills.

Consisting of several modules, the course teaches you everything you need to succeed in this profession.

The course can be studied part-time. You can become accredited within 05 Hours studying at your own pace. Your qualification will be recognised and can be checked for validity on our dedicated website.

Why Choose Teachers Training

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Entry Requirements

No formal entry requirements. You need to have:


CPD Certification from The Teachers Training

Successfully completing the MCQ exam of this course qualifies you for a CPD-accredited certificate from The Teachers Training. You will be eligible for both PDF copy and hard copy of the certificate to showcase your achievement however you wish.

  • You can get your digital certificate (PDF) for £4.99 only
  • Hard copy certificates are also available, and you can get one for only £10.99
  • You can get both PDF and Hard copy certificates for just £12.99!

The certificate will add significant weight to your CV and will give you a competitive advantage when applying for jobs.

Course Curriculum

Data Manipulation Python Numpy and Pandas
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 00:05:00
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 00:03:00
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 & Continuous 00:05:00
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


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