BIG-DATA-PYTHON.AJ1
Big Data Analysis with Python
Practice and refine your big data analytical skills with Python to distill complicated data into digestible and meaningful insights.
- Practice in 48 Hands-On Labs — nothing to install
- 9 Interactive Lessons and 54 topics mapped to the official exam objectives
- 100 Practice Test Questions
Intermediate Self-paced · 1 year access 4.4/5 (237 Reviews)
48 Hands-On LiveLabs
Practice real IT tasks in guided environments.
- Real environments
- Auto-graded
- No installation
9Interactive Lessons
54Topics
48LiveLab
100Practice Test Questions
65Flashcards
65Glossary of terms
01 / Skills you'll get
What you will be able to do
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No credit card required
This big data analysis with Python course online is your go-to training guide for mastering the art of handling and analyzing massive piles of data. You’ll experiment with Python libraries like Pandas, Seaborn, and Spark. Also, our course modules will help you visualize data, manage missing values, and perform in-depth statistical analysis, giving you hands-on experience. By the end, you’ll have the technical skills to tackle real-world challenges and make data-driven decisions.
- Use Pandas and Spark for effective data handling
- Create insightful statistical visualizations using Seaborn and Matplotlib to communicate findings clearly
- Work with frameworks like Hadoop and Spark to manage large datasets
- Handle missing values and prepare data for analysis and accuracy
- Translate business problems into a measurable metric and actionable insight
- Maintain data analysis reproducibility with best practices using Jupyter Notebooks
- Dive deep into Spark DataFrames for advanced data manipulation and analysis
- Compile full analysis reports to present data findings professionally
- Execute SQL operations on Spark DataFrames for efficient data querying
Target Career Roles
- Data scientist and systems architect
- Expected Salary $122
- 000
02 / Lessons & labs
See exactly what you will learn and practice
Lessons
9 Interactive Lessons · 54 topics01 Preface 1 topics +
- About
02 The Python Data Science Stack 8 topics · 10 LiveLab +
- Introduction
- Python Libraries and Packages
- Using Pandas
- Data Type Conversion
- Aggregation and Grouping
- Exporting Data from Pandas
- Visualization with Pandas
- Summary
10 LiveLab in this lesson — see the labs panel →
03 Statistical Visualizations 10 topics · 14 LiveLab +
- Introduction
- Types of Graphs and When to Use Them
- Components of a Graph
- Seaborn
- Which Tool Should Be Used?
- Types of Graphs
- Pandas DataFrames and Grouped Data
- Changing Plot Design: Modifying Graph Components
- Exporting Graphs
- Summary
14 LiveLab in this lesson — see the labs panel →
04 Working with Big Data Frameworks 6 topics · 3 LiveLab +
- Introduction
- Hadoop
- Spark
- Writing Parquet Files
- Handling Unstructured Data
- Summary
3 LiveLab in this lesson — see the labs panel →
05 Diving Deeper with Spark 7 topics · 9 LiveLab +
- Introduction
- Getting Started with Spark DataFrames
- Writing Output from Spark DataFrames
- Exploring Spark DataFrames
- Data Manipulation with Spark DataFrames
- Graphs in Spark
- Summary
9 LiveLab in this lesson — see the labs panel →
06 Handling Missing Values and Correlation Analysis 6 topics · 5 LiveLab +
- Introduction
- Setting up the Jupyter Notebook
- Missing Values
- Handling Missing Values in Spark DataFrames
- Correlation
- Summary
5 LiveLab in this lesson — see the labs panel →
07 Exploratory Data Analysis 5 topics · 4 LiveLab +
- Introduction
- Defining a Business Problem
- Translating a Business Problem into Measurable Metrics and Exploratory Data Analysis (EDA)
- Structured Approach to the Data Science Project Life Cycle
- Summary
4 LiveLab in this lesson — see the labs panel →
08 Reproducibility in Big Data Analysis 6 topics · 3 LiveLab +
- Introduction
- Reproducibility with Jupyter Notebooks
- Gathering Data in a Reproducible Way
- Code Practices and Standards
- Avoiding Repetition
- Summary
3 LiveLab in this lesson — see the labs panel →
09 Creating a Full Analysis Report 5 topics +
- Introduction
- Reading Data in Spark from Different Data Sources
- SQL Operations on a Spark DataFrame
- Generating Statistical Measurements
- Summary
Hands-On Labs Our edge
48 LiveLabs- Interacting with the Python Shell
- Calculating the Square
- Grouping a DataFrame
- Applying a Function to a Column
- Subsetting a DataFrame
- Slicing and Subsetting
- Reading Data from a CSV File
- Viewing the Standard Deviation
- Calculating the Median Value
- Calculating the Mean Value
- Plotting an Analytical Graph
- Creating a Graph
- Creating a Graph for a Mathematical Function
- Creating a Line Graph Using Seaborn
- Creating a Line Graph Using pandas
- Creating a Line Graph Using matplotlib
- Detecting Outliers
- Displaying Histograms
- Using a Box Plot
- Constructing a Scatterplot
- Plotting a Line Graph with Styles and Color
- Configuring a Title and Labels for Axis Objects
- Designing a Complete Plot
- Exporting a Graph to a File on a Disk
- Performing DataFrame Operations in Spark
- Accessing Data with Spark
- Parsing Text in Spark
- Creating a DataFrame Using a CSV File
- Creating a DataFrame from an Existing RDD
- Specifying the Schema of a DataFrame
- Removing a Column from a DataFrame
- Renaming a Column in a DataFrame
- Adding a Column to a DataFrame
- Creating a KDE Plot
- Creating a Linear Model Plot
- Creating a Bar Chart
- Filtering Data
- Counting Missing Values
- Handling NaN Values
- Using the Backward and Forward Filling Methods
- Calculating Correlation Coefficient
- Generating the Feature Importance of the Target Variable
- Identifying the Target Variable
- Plotting a Heatmap
- Generating a Normal Distribution Plot
- Performing Data Reproducibility
- Preprocessing Missing Values with High Reproducibility
- Normalizating the Data
Labs run in your browser — nothing to install.
03 / Exam details
Big Data Analysis with Python Details
Get hands-on experience of big data analysis with Python with the comprehensive course and lab. The lab provides hands-on learning in analyzing data with the use of python, beginning up with the basics to mastering different types of data. The course and lab deal with python data science stack, statistical visualizations, working with big data frameworks, handling missing values and correlation analysis, exploratory data analysis, reproducibility in big data analysis, and many more.
Practice Tests
100 Practice Questions
(mapped to official exam objectives)
Certification
BIG-DATA-PYTHON.AJ1
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04 / FAQs
Questions before you start
What is big data, and why is it important? +
Big data consists of massive amounts of datasets that are analyzed to identify and reveal patterns, trends, and relationships. Big data analysis helps organizations to make decisions, improve their operations, and discover new opportunities to penetrate the market.
Why is Python popular for big data analysis? +
Python programming language is famous in the data science field due to its simplicity, improved readability, user-friendly libraries, and strong developer community support.
What is the significance of data visualization in today’s world? +
Polish your data visualization skills to present raw data in graphical formats and identify patterns and trends to make data-driven decisions. Eventually, it provides you with a competitive advantage in the market.
Do I need prior Python programming experience to take this course? +
Yes, having basic knowledge of Python programming is beneficial to take this data analysis with Python course.
Who is this course suitable for? +
This course is ideal for data scientists, analysts, and anyone interested in improving their analytical skills using Python.
Do I need to have prior knowledge of Python to take this course? +
Yes, a basic understanding of Python programming languages is recommended to get the most out of this course.
What tools and libraries are covered in this course? +
The course covers tools and libraries like Pandas, Seaborn, Matplotlib, and Spark for data manipulation and visualization.
Do I need to know machine learning for this course? +
No, prior knowledge of machine learning is not required to enroll in this course.
What is the average salary for a big data analyst with Python skills? +
The average salary of a big data analyst varies, but typically ranges from $80,000 to $120,000 per year, depending on experience, location, and industry.
How can this course benefit my career?+
By learning Python big data analysis and visualization, you’ll be able to handle large datasets, perform advanced analysis, and make smart, profitable decisions. In addition, you’ll be in a position to pursue high-paying jobs, promotions, and other career opportunities.
What are the prerequisites for this exam?+
Installed Python and Jupyter Notebook
Where can I find more information about this exam?+
Command Big Data Analysis Using Python
Gain the skills to transform vast amounts of raw data into clear visuals for improved decision-making in your career.
- 1 year of full access
- 48 LiveLab included
- Certificate of completion
No credit card required