Python for Data Science: Analyzing Data Using Python 2024 E-Learning
Python for data science: analysing data with Python
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✔️ 1 year 24/7 access to rich interactive videos, exercises and progress reports.
✔️ Includes tests per chapter to instantly test your knowledge and improve your skills.
Why choose this course?
Data science is one of the most in-demand skills of the 21st century. Python, one of the most popular programming languages, is essential for analysing, processing and visualising large data sets. This course offers:
What you will learn:
- Python programming skills for data analysis: Learn the basics and advanced techniques to efficiently analyse and manipulate data.
- Managing data frames and tables: Use powerful Python libraries such as Pandas and NumPy.
- Data visualisation: Create compelling data visualisations with Matplotlib and Seaborn to clearly convey insights.
- Machine Learning and AI applications: Discover how to use Python in combination with libraries such as Scikit-learn to perform predictive analytics.
- Practical case studies: Work on real-life projects to put your skills into practice and deepen your understanding.
With this course, you will be ready to take your data analysis skills to the next level and play an important role in data-driven decision-making.
Who should participate?
This course is perfect for:
- Data scientists and analysts who want to improve their skills in Python.
- IT professionals who want to learn how to analyse and visualise data.
- Students and academics interested in data science and Python programming.
- Decision-makers and managers who want to implement data-driven strategies within their organisations.
- Anyone with an interest in data science and a desire to learn how to use Python for data analysis.
Course content
Python for Data Science: Introduction to NumPy for Multi-dimentional Data
Course: 1 Hour
- Course Overview
- Introduction to NumPy and the NumPy Ecosystem
- Array Creation - Part 1
- Array Creation - Part 2
- Printing Arrays
- Basic Array Operations
- Universal Functions
- Indexing and Slicing
- Iterating Over Arrays
- Reshaping Arrays
- Exercise: Python NumPy Array Operations
Python for Data Science: Advanced Operations with NumPy Arrays
Course: 1 Hour, 8 Minutes
- Course Overview
- Splitting NumPy Arrays
- Images as Arrays
- Image Manipulation Using NumPy
- Views and NumPy Arrays
- Deep Copies of Arrays
- Introduction to Index Masks
- Applying Index Masks
- Indexing with Boolean Masks
- Structured Arrays
- Understanding Array Broadcasting
- Applying Broadcasting Rules on Array Operations
- Exercise: NumPy Multi-dimensional Array Operations
Python for Data Science: Introduction to Pandas
Course: 1 Hour, 6 Minutes
- Course Overview
- Features of Pandas and the Pandas Ecosystem
- Introduction to Pandas
- Work with Pandas
- Introduction to DataFrames
- Work with DataFrames
- Load Data into a DataFrame
- Add and Delete DataFrame Contents
- Select Parts of a DataFrame
- Access Pandas DataFrames
- Introduction to Multi-Indexing in a Dataframe
- Reshape DataFrames
- Reshape Dataframes Using Stack and Melt Operations
- Exercise: Pandas for Basic Tabular Data Manipulation
Python for Data Science: Manipulating and Analyzing Data in Pandas DataFrames
Course: 45 Minutes
- Course Overview
- Iterating Over the Contents of a DataFrame
- Exporting a DataFrame
- Sorting
- Handling Missing Data
- Grouping with a Multi-Index
- Merging DataFrames
- Applying Join Operations on DataFrames
- Pandas and Relational Databases
- Exercise: Pandas for Advanced Data Manipulation
Python for Data Science: Basic Data Visualization Using Seaborn
Course: 1 Hour, 7 Minutes
- Course Overview
- Introduction to Seaborn
- Install Seaborn
- Simple Univariate Distributions
- Configure Univariate Distribution Plots
- Simple Bivariate Distributions
- Explore Different Types of Bivariate Distributions
- Analyze Multiple Variable Pairs
- Regression Plots
- Themes and Styles in Seaborn
- Exercise: Basic Data Visualization Using Seaborn
Python for Data Science: Advanced Data Visualization Using Seaborn
Course: 1 Hour, 4 Minutes
- Course Overview
- Searching for Patterns in a Dataset
- Configuring Plot Aesthetics
- Normal Distribution and Outliers
- Distributions Within Categories - Part 1
- Distributions Within Categories - Part 2
- Analyzing Categories with Facet Grids - Part 1
- Analyzing Categories with Facet Grids - Part 2
- Introducing Color Palettes5
- Using Color Palettes
- Exercise: Advanced Data Visualization Using Seaborn
Analyzing Data Using Python
Analyzing Data Using Python: Data Analytics Using Pandas
Course: 1 Hour, 15 Minutes
- Course Overview
- Creating a pandas Series
- Performing Basic Series Operations
- Performing Casting Operations Using Series
- Performing Logical Operations Using Series
- Creating and Editing pandas DataFrames
- Performing Data Lookup Using DataFrames
- Constructing DataFrames from Dictionaries and Tuples
- Course Summary
Analyzing Data Using Python: Importing, Exporting, & Analyzing Data With Pandas
Course: 1 Hour, 23 Minutes
- Course Overview
- Importing and Exporting CSV Files
- Importing and Exporting Data in HTML and JSON Files
- Reading and Writing Excel Files Using DataFrames
- Performing Basic DataFrame Operations
- Computing Unique Values and Renaming Columns
- Sorting Data Based on Specific Columns
- Computing Basic Statistics
- Computing Univariate and Bivariate Statistics
- Course Summary
Analyzing Data Using Python: Analyzing Data Using Python: Filtering Data in Pandas
Course: 1 Hour, 26 Minutes
- Course Overview
- Performing Data Lookup Operations
- Leveraging the loc and iloc Functions
- Using the loc, iloc, at, and iat Functions to Filter Data
- Filtering Data Using Wildcards and Boolean Predicates
- Using the Query Function to Filter Data
- Manipulating Data Using Datetime Values
- Selecting and Dropping Columns
- Applying Advanced Techniques to Select and Drop Columns
- Course Summary
Analyzing Data Using Python: Cleaning & Analyzing Data in Pandas
Course: 1 Hour, 53 Minutes
- Course Overview
- Identifying Duplicates in Data
- Categorizing and Binning Data
- Computing Aggregations on Data
- Grouping and Aggregating Data1
- Viewing Data Using Pivot Tables
- Summarizing Data Using Pivot Tables
- Combining Data in DataFrames
- Implementing Inner Joins
- Implementing Left and Right Joins
- Performing Joins Using DataFrame Indexes
- Analyzing Time Series Data
- Course Summary
Get started with Python for Data Science!
✔️ Learn at your own pace with extensive interactive videos, practical assignments and challenging tests.
✔️ Build practical skills that will help you get started immediately in the world of data analysis.
✔️ Get certified and add value to your CV and career.
Order your course now and start your journey to data analytics expertise today!
