Data Manipulation in Python: Master Python, Numpy & Pandas

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Data Manipulation in Python: Master Python, Numpy & Pandas

Overview Welcome to “Data Manipulation in Python: Master Python, NumPy & Pandas.” This course is designed to equip you with …

Data Manipulation in Python: Master Python, Numpy & Pandas

Original price was: $417.25.Current price is: $35.30.

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5 hours, 18 minutes

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

Welcome to “Data Manipulation in Python: Master Python, NumPy & Pandas.” This course is designed to equip you with essential skills for data manipulation and analysis using Python. You’ll start with a quick refresher on Python, then delve into the crucial libraries for data science, such as NumPy and Pandas. You’ll explore fundamental properties of NumPy, essential mathematics for data science, and how to work with Pandas DataFrames and Series. Additionally, the course covers data cleaning, data visualisation using Python, exploratory data analysis, and time series analysis. By the end of this course, you’ll be proficient in manipulating data and performing complex data analyses with Python.

Learning Outcomes

Refresh your Python programming skills.
Utilise essential Python libraries for data science.
Understand and apply fundamental NumPy properties.
Implement mathematical concepts essential for data science.
Work efficiently with Pandas DataFrames and Series.
Perform data cleaning to prepare datasets for analysis.
Create data visualisations using Python libraries.
Conduct exploratory data analysis (EDA) to uncover insights.
Analyse and interpret time series data.
Develop robust data manipulation workflows in Python.

Eligibility Requirements

To Enrol In “Data Manipulation In Python: Master Python, NumPy & Pandas,” You Should Have Basic Computer Skills And A Keen Interest In Data Science. Prior Experience With Python Is Helpful But Not Required, As An Optional Python Refresher Is Provided. Ensure You Have A Computer With Python Installed To Follow Along With The Exercises.

Entry Requirements

Age Requirement: Applicants must be aged 16 or above, allowing both young learners and adults to engage in this educational pursuit.
Academic Background: There are no specific educational prerequisites, opening the door to individuals from diverse academic histories.
Language Proficiency: A good command of the English language is essential for comprehension and engagement with the course materials.
Numeracy Skills: Basic numeracy skills are required for understanding nutritional data and dietary planning.

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

Completing this course opens up numerous career opportunities in data science, analytics, and research. You will gain the skills to work as a data analyst, data scientist, or research analyst. Additionally, the expertise acquired in this course proves valuable for roles in business intelligence, machine learning, and financial analysis. By mastering data manipulation in Python, you will be prepared to tackle complex data-driven projects and advance your career rapidly.

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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Data Manipulation in Python: Master Python, Numpy & Pandas

Data Manipulation in Python: Master Python, Numpy & Pandas

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

Learning Outcomes

Eligibility Requirements

Entry Requirements

Why Choose Us