BIG DATA ANALYTICS: (6 Months)

Description

A Business Intelligence (BI) course covers a wide range of topics related to data analysis, reporting, and decision-making in a business context. These topics are designed to provide individuals with the skills and knowledge necessary to work with data effectively.

What will you learn

• Understanding Big Data Concepts: Demonstrate an understanding of the key characteristics of big data, including volume, velocity, variety, and veracity.
• Data Collection and Integration: Collect and integrate data from diverse sources, both structured and unstructured.
• Data Storage and Management: Implement storage solutions for big data, including data warehousing, NoSQL databases, and distributed file systems.
• Data Processing and Transformation: Clean, preprocess, and transform raw data into a usable format for analysis.
• Big Data Technologies: Proficiency in using big data tools and frameworks like Hadoop, Spark, and Kafka.
• Machine Learning and Predictive Analytics: Apply machine learning algorithms to build predictive models and extract insights from large datasets.
• Data Visualization: Create effective data visualizations and dashboards to communicate findings and patterns.
• Distributed Computing: Understand the principles of distributed computing and parallel processing for efficient big data analysis.
• Data Security and Privacy: Ensure data security and privacy compliance when handling large volumes of sensitive data.
• Scalability and Performance Optimization: Optimize the performance and scalability of big data applications and platforms.
• Real-Time Analytics: Analyze and process data in real-time for immediate insights and decision-making.
• Data Ethics and Bias: Recognize and address ethical considerations and potential biases in big data analysis.
• Use Cases and Applications: Apply big data analytics techniques to real-world use cases and business scenarios across various industries.
• Big Data Tools and Ecosystem: Proficiency in using tools and components within the big data ecosystem, such as Hadoop, Spark, and related technologies.
• Problem-Solving Skills: identify and frame business problems that can benefit from big data analytics and propose effective solutions.
• Project Management: Manage big data analytics projects, including project planning, execution, and monitoring.
• Communication Skills: Effectively communicate findings, insights, and recommendations to both technical and non-technical stakeholders.
• Emerging Trends in Big Data: Stay informed about the latest developments and emerging trends in the field of big data analytics.

Requirements

Graduation (relevant) / Master’s degree (non-relevant)

Lessons

21 Lessons
00:00:00 Hours

Introduction to Business Intelligence:

Overview of BI concepts, tools, and their importance in modern business.

Predictive Analytics:

Using data to build predictive models and make forecasts for future trends.

Machine Learning in BI:

Applications of machine learning algorithms for advanced analytics.

Big Data and NoSQL Databases:

Understanding big data concepts and NoSQL databases for handling large and unstructured datasets.

Business Intelligence Strategy:

Developing strategies for using BI to support organizational goals and decision-making.

Data Ethics:

Ethical considerations in data collection, analysis, and reporting.

Data Storytelling:

Communicating data insights effectively to non-technical stakeholders.

Real-World Case Studies:

: Analyzing real business scenarios and datasets to apply BI techniques.

BI Project Management:

Managing BI projects, including planning, execution, and monitoring.

Data Security and Privacy:

: Ensuring data security and addressing privacy concerns when handling sensitive information.

Data Governance and Compliance

: Implementing data governance policies to ensure data accuracy and compliance with regulations.

Data Quality and Cleansing:

identifying and resolving data quality issues, such as missing values and duplicates.

Data Warehousing:

Understanding data warehousing concepts and architectures for storing and managing large datasets.

Data Modeling:

Designing data models to represent structured data efficiently.

Data Integration and ETL (Extract, Transform, Load):

Techniques for extracting data from various sources, transforming it, and loading it into a data warehouse.

Data Visualization:

Creating meaningful charts, graphs, and dashboards to communicate insights effectively.

Data Analysis Techniques:

: Statistical analysis, data exploration, and hypothesis testing for deriving insights from data.
: Familiarity with different BI platforms, their features, and how to use them effectively.