Data Manipulation in Python: Master Python, Numpy & Pandas

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Data Manipulation in Python: Master Python, Numpy & Pandas

Data Manipulation in Python: Master Python, Numpy & Pandas

Level: Intermediate
Modules: 107 Modules
Duration: 4 hours, 47 minutes
Students: 1

Unlock the transformative power of data with “Data Manipulation in Python: Master Python, Numpy & Pandas.” Dive deep into the heart of data science, where you’ll unravel the complexities of data manipulation and emerge as a master of Python, Numpy, and Pandas. Our course is meticulously crafted to provide a thorough understanding of Python’s essential libraries for data science, guiding you from fundamental properties of Numpy to the intricacies of data cleaning and visualization. Discover the elegance of Pandas DataFrames and Series, explore the mathematical foundations crucial for data science, and become proficient in handling time series data. This course is your gateway to mastering the tools and techniques that drive data analysis, enabling you to transform raw data into meaningful insights. Immerse yourself in a rich learning experience that promises to elevate your data manipulation skills to new heights, all within a structured and comprehensive curriculum designed for both beginners and those looking to deepen their expertise. Whether you’re aiming to refine your data science acumen or aspiring to start a new journey in the data domain, “Data Manipulation in Python: Master Python, Numpy & Pandas” is the quintessential course to propel you towards your goals.

Learning Outcomes

Understand and utilize essential Python libraries for data science.
Master the fundamental properties and functionalities of Numpy.
Perform effective data cleaning and preparation.
Create insightful data visualizations using Python tools.
Conduct comprehensive exploratory data analysis.
Analyze and manipulate time series data efficiently.

Why buy this Data Manipulation in Python: Master Python, Numpy & Pandas course?

Unlimited access to the course for forever
Digital Certificate, Transcript, student ID all included in the price
Absolutely no hidden fees
Directly receive CPD accredited qualifications after course completion
Receive one to one assistance on every weekday from professionals
Immediately receive the PDF certificate after passing
Receive the original copies of your certificate and transcript on the next working day
Easily learn the skills and knowledge from the comfort of your home

Certification

After studying the course materials of the Data Manipulation in Python: Master Python, Numpy & Pandas there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99. Original Hard Copy certificates need to be ordered at an additional cost of £8.

Who is this Data Manipulation in Python: Master Python, Numpy & Pandas course for?

Aspiring data scientists seeking a strong foundation in data manipulation.
Python enthusiasts aiming to enhance their data analysis capabilities.
Students in mathematics or statistics looking to apply their knowledge to data science.
Professionals transitioning into data-driven roles.
Anyone interested in learning how to manage and analyse data using Python.
Researchers and analysts wanting to improve their data processing skills.

Prerequisites

This Data Manipulation in Python: Master Python, Numpy & Pandas does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Data Manipulation in Python: Master Python, Numpy & Pandas was made by professionals and it is compatible with all PC’s, Mac’s, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.

Career path

Data Analyst: £25,000 to £45,000 per year
Data Scientist: £45,000 to £70,000 per year
Business Intelligence Analyst: £30,000 to £55,000 per year
Machine Learning Engineer: £50,000 to £80,000 per year
Quantitative Analyst: £60,000 to £90,000 per year
Data Engineer: £40,000 to £65,000 per year

Course Curriculum

Python Quick Refresher (Optional)
	Welcome to the course!	00:01:00
	Introduction to Python	00:01:00
	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	00:04:00
	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	00:04:00
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
Course Materials
	Course Materials	00:50:00