Data Manipulation: Python, Numpy and Pandas
Data Manipulation: Python, Numpy and Pandas Overview Ever feel overwhelmed by the sheer amount of data companies collect? What if …
Course Description
Data Manipulation: Python, Numpy and Pandas Overview
Ever feel overwhelmed by the sheer amount of data companies collect? What if you could transform this data into actionable insights that drive real results? This course empowers you to do just that!
We’ll guide you step-by-step through the essential tools and techniques of data science, all powered by the popular and beginner-friendly programming language, Python. Regardless of your current Python experience, this course bridges the gap. Whether you’re a complete novice or looking to refresh your skills, we’ll equip you to leverage the power of industry-standard libraries like NumPy and Pandas.
These libraries are your secret weapons for data manipulation. Imagine effortlessly organizing messy datasets, cleaning and filtering out inconsistencies, and preparing them for in-depth analysis. With newfound clarity, you’ll be able to create compelling charts and graphs, revealing hidden patterns and trends within the data.
Whether you’re a complete beginner or a seasoned professional looking to refine your data science skills, this course sets you on the path to success. It equips you with the foundational knowledge and practical skills to embark on a rewarding career in data science, a field with ever-growing demand and opportunities.
This course is your gateway to a world of possibilities. Enrol today and take the first step towards transforming data into a powerful tool for progress.
Learning Outcomes:
- Master the fundamentals of data manipulation using Python libraries.
- Gain expertise in wrangling and cleaning messy datasets.
- Craft compelling data visualizations to communicate insights effectively.
- Conduct exploratory data analysis to uncover hidden trends and patterns.
- Work confidently with time series data for insightful forecasting.
Why You Should Choose Data Manipulation: Python, Numpy and Pandas
- Lifetime access to the course
- No hidden fees or exam charges
- CPD Accredited certification on successful completion
- Full Tutor support on weekdays (Monday – Friday)
- Efficient exam system, assessment and instant results
- Download Printable PDF certificate immediately after completion
- Obtain the original print copy of your certificate, dispatch the next working day for as little as £9.
- Improve your chance of gaining professional skills and better earning potential.
Who is this Course for?
Data Manipulation: Python, Numpy and Pandas is CPD certified accredited. This makes it perfect for anyone trying to learn potential professional skills.
As there is no experience and qualification required for this course, it is available for all students from any academic backgrounds.
Requirements
Our Data Manipulation: Python, Numpy and Pandas is fully compatible with any kind of device. Whether you are using Windows computer, Mac, smartphones or tablets, you will get the same experience while learning. Besides that, you will be able to access the course with any kind of internet connection from anywhere at any time without any kind of limitation.
Career Path
You will be ready to enter the relevant job market after completing this course. You will be able to gain necessary knowledge and skills required to succeed in this sector. All our Diplomas’ are CPD accredited so you will be able to stand out in the crowd by adding our qualifications to your CV and Resume.
10 Sections 106 lectures 3 hours 55 mins in total
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Welcome to the course!
00:01:00 -
Introduction to Python
00:01:00 -
Course Materials
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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 -
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
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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
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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
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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
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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
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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
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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
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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
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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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