Big Data Analysis with Python

Acquire extensive practical expertise in big data analysis using Python through our comprehensive course and hands-on lab. Our lab is purpose-built to offer immersive learning experiences in Python-driven data analysis, beginning with foundational concepts and advancing to proficiency in diverse data types. This program encompasses a wide spectrum of topics, including the Python data science stack, statistical visualizations, navigating big data frameworks, managing missing data, and conducting correlation analyses. Additionally, it delves into exploratory data analysis and emphasizes reproducibility in large-scale data analysis. Through active participation in this initiative, you will develop the crucial skills and understanding necessary to adeptly handle and manipulate vast datasets using Python. Upon completion, you'll be equipped with the confidence and expertise to engage in effective big data analysis, making you an invaluable asset in the realm of data-driven decision-making.

Certification Objectives:

Lessons

Lesson 1: Preface

• About

Lesson 2: The Python Data Science Stack

• Introduction
• Python Libraries and Packages
• Using Pandas
• Data Type Conversion
• Aggregation and Grouping
• Exporting Data from Pandas
• Visualization with Pandas
• Summary

Lesson 3: Statistical Visualizations

• 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

Lesson 4: Working with Big Data Frameworks

• Introduction
• Hadoop
• Spark
• Writing Parquet Files
• Handling Unstructured Data
• Summary

Lesson 5: Diving Deeper with Spark

• Introduction
• Getting Started with Spark DataFrames
• Writing Output from Spark DataFrames
• Exploring Spark DataFrames
• Data Manipulation with Spark DataFrames
• Graphs in Spark
• Summary

Lesson 6: Handling Missing Values and Correlation Analysis

• Introduction
• Setting up the Jupyter Notebook
• Missing Values
• Handling Missing Values in Spark DataFrames
• Correlation
• Summary

Lesson 7: Exploratory Data Analysis

• 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

Lesson 8: Reproducibility in Big Data Analysis

• Introduction
• Reproducibility with Jupyter Notebooks
• Gathering Data in a Reproducible Way
• Code Practices and Standards
• Avoiding Repetition
• Summary

Lesson 9: Creating a Full Analysis Report

• Introduction
• Reading Data in Spark from Different Data Sources
• SQL Operations on a Spark DataFrame
• Generating Statistical Measurements
• Summary

Hands-on LAB Activities

The Python Data Science Stack

• 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

Statistical Visualizations

• 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

Working with Big Data Frameworks

• Performing DataFrame Operations in Spark
• Accessing Data with Spark
• Parsing Text in Spark

Diving Deeper with 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

Handling Missing Values and Correlation Analysis

• Filtering Data
• Counting Missing Values
• Handling NaN Values
• Using the Backward and Forward Filling Methods
• Calculating Correlation Coefficient

Exploratory Data Analysis

• Generating the Feature Importance of the Target Variable
• Identifying the Target Variable
• Plotting a Heatmap
• Generating a Normal Distribution Plot

Reproducibility in Big Data Analysis

• Performing Data Reproducibility
• Preprocessing Missing Values with High Reproducibility
• Normalizating the Data

Target Audience:

Big Data Analysis with Python training includes professionals and individuals seeking to gain proficiency in utilizing Python for analyzing large datasets. This could encompass data scientists, analysts, engineers, programmers, researchers, and anyone involved in handling and interpreting substantial volumes of data. Additionally, it may be beneficial for students or individuals looking to enter the field of data analysis, as well as those seeking to enhance their skill set in Python-based data science and analytics. This training is designed to cater to a diverse range of backgrounds and experience levels, from beginners to intermediate learners, aiming to equip them with the tools and knowledge needed to effectively navigate and extract insights from big data using Python.

Benefits and Beyond:

Big Data Analysis with Python training provides a strong foundation in Python, essential for data analysis careers. It enables insightful decision-making from large datasets, while also imparting advanced skills in handling complex data structures and frameworks. This training deepens understanding of statistical visualization, correlation analysis, and exploratory data techniques. It serves as a gateway to specialization in areas like machine learning and AI, and instills a mindset of reproducibility and robustness in data processes, ensuring high-quality insights. Overall, it's a transformative experience, fostering technical expertise and a data-driven mindset for success in today's data-centric world.