Learning to Best Path of Big Data Analytics with Python Course in Singapore

Big data Analytics with python Course is an excellent choice for data analysis for several reasons. Easy to Learn and Use, Python is a simple and easy-to-learn language. Python provides several libraries that make it easy to read, manipulate, visualize and analyze large datasets. Similar library is Pandas which provides easy to use data structures for handling tabular datasets such as spreadsheets or SQL tables.

What is Big Data Analytics:

Big data simply refers to extremely large data sets. This size, combined with the complexity and developing nature of these data sets, has validate them to surpass the capabilities of traditional data management tools. This way, data warehouses and data lakes have emerged as the go-to solutions to handle big data, far off remarkable the power of conventional databases.

Types of Big Data Analytics:

Structured data:

Structured data following a specific structure can be called structured data. These structured data sets can be processed based on easily compared to other data types as users can exactly identify the structure of the data. A good example for structured data will be a distributed RDBMS which contains data in organized table structures.

Semi structured data:

This type of data does not follow any specific structure yet retains some type of observable structure such as a grouping or an organized hierarchy. Some examples of semi-structured data will be markup languages (XML), web pages, emails, etc

Unstructured data:

This type of data includes data that does not follow any schema or predetermined structure. It is the most common type of data when dealing with big data—things like text, pictures, video, and audio all come up under this type.

Why Big data Analytics with python Course:

Python is the best choice for data Analytics for some reasons, Easy to Learn and Use, Python is a simple and easy-to-learn language. Python provides several libraries that make it easy to read, manipulate, visualize and analyze large datasets. Similar library is Pandas which provides easy to use data structures for handling tabular datasets such as spreadsheets or SQL tables. This language fits well for data analysis professionals as it provides big support and offers an extensive range of libraries for some tasks.

Simple coding:

Python programming involves simple coding compared to other programming languages. We can execute the programs with few code lines, and the essential thing is we can associate and identify data types quickly with Python. This language can process and prolix tasks within a short time.

Open source and easy to learn:

learn big data analytics using Python Course is an open-source programming language developed with the community-based model. It’s free to use, it’s open source, supports multiple platforms and can be run on any environment (Linux, Windows, etc.) Python is easy to learn as well because of its simple syntax. This simple, readable syntax helps Big Data pros to focus on insights managing Big data, alternative wasting time in understanding the technical shade of the language. This one is one of the primary reasons to choose Python for Big Data.

Python has a high processing speed:

Python is the best language because of its fast moving data processing speed with big data analysis. Python programs are executed in a fraction of the time needed by other programming languages because of its simple syntax and manageable code. It supports various prototyping ideas, making it run code faster while maintaining best clarity between code and execution. This usually makes Python one of the most popular options for Big Data in the tech industry.

Python supports multiple libraries:

Python is a popular programming language because of its extensive support for libraries. These libraries are beneficial in saving time and make the language even more popular. Mostly Python libraries are useful for data analytics, visualization, numerical computing, and machine learning. Big Data requires a lot of scientific computing and data analysis, and the combination of Python with Big Data make them great companions.

Python has data processing support:

Python is a basic feature of supporting data processing for unconventional and unstructured data, and this is the most common requirement for Big Data to explore social media data. That is the reason why big data companies choose Python as an essential requirement in Big Data.

Data Analytics Course with Python:

Data Analytics Course with Python enables you to extract insights from data to help make decisions for the future. Python is the most preferred programming language for Data Science Professionals and this course covers various Libraries and Frameworks that Python has to offer. Along with the concepts you get to work on real-world projects.

Big Data Analytics With Python course outline

Lesson 1: The Python Data Science Stack

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

Lesson 2: Statistical Visualizations

Types of Graphs and When to Use Them
Components of a Graph
Which Tool Should Be Used?
Types of Graphs
Pandas DataFrames and Grouped Data
Changing Plot Design: Modifying Graph Components
Exporting Graphs

Lesson 3: Working With Big Data Frameworks

Hadoop
Spark
Writing Parquet Files
Handling Unstructured Data

Lesson 4: Diving Deeper With Spark

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

Lesson 5: Handling Missing Values And Correlation Analysis

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

Lesson 6: Exploratory Data Analysis

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