Data Analytics and Big Data
Accredited by Averest
350 Learners
INTERMEDIATE
The combination of Data Analytics and Big Data has transformed the way organizations make decisions, improve processes, and gain insights. This 5-day Data Analytics and Big Data training program introduces participants to fundamental concepts in data analysis, the tools and techniques used to process large datasets, and how Big Data technologies are leveraged to drive data-driven decision-making. By the end of the course, participants will have the skills to work with both structured and unstructured data, and will gain hands-on experience with Big Data platforms and advanced analytics techniques.
Accreditation With .
Data Analytics and Big Data Overview
This comprehensive 5-day training program equips participants with the essential skills to harness the power of Big Data and data analytics for driving informed decision-making. You'll delve into fundamental concepts, explore industry-leading tools, and gain hands-on experience with real-world datasets.
What You Will Learn ?
- Understand real-world applications of data analytics in various industries, such as finance, healthcare, and marketing.
- Gain hands-on experience with Big Data platforms, data processing frameworks, and visualization tools.
- Learn to apply data analytics methods to large datasets and extract actionable insights.
- Explore the architecture and tools in Big Data environments, such as Hadoop, Spark, and NoSQL databases.
- Understand the key concepts and techniques of data analytics and how they apply to Big Data.
Course Key Features
- Comprehensive Coverage
- Hands-on Learning
- Expert Instructors
- Industry-Relevant Content
- Flexible Learning Options
Training Options
In Class
- 5-days in-class training
- Pre-course consultation
- Highly experienced instructor(s)
- Post-course follow-up
- All related Averest's quality control tools & required stationary
- 5 or 4 stars training venue
- Pay later by invoice -OR- at the time of checkout by credit card
- Continuous learner assistance and support
Online
- 5-days instructor-led training
- Pre-course consultation
- Highly experienced instructor(s)
- Post-course follow-up
- Pay later by invoice -OR- at the time of checkout by credit card
- Continuous learner assistance and support
Corporate Training
- A highly customized Corporate Training service designed exclusively for corporate employees and teams. Our training programs are meticulously planned and executed to fill knowledge and experience gaps, helping organizations achieve their business goals. With a comprehensive assessment and tailored curriculum, our experienced trainers deliver modules in areas of accreditation requirements as well as complementary practices such as leadership, communication, and technology adoption. Official certification exam voucher is provided upon completion, ensuring professional growth and measurable results. Contact us now to partner with Averest Training in order to bridge the gaps in your workforce and unlock the full potential of your team.
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Data Analytics and Big Data Training Cirriculum
Eligibility .
This course is designed for:
• Data Scientists and Analysts: Professionals looking to expand their skills in analyzing large datasets using Big Data technologies.
• Developers and Engineers: Individuals seeking to build and optimize Big Data platforms for advanced analytics.
• Business Leaders and Entrepreneurs: Executives interested in leveraging data analytics and Big Data for better decision-making.
• IT Professionals: Engineers and system administrators managing data infrastructure and looking to implement Big Data solutions.
• Tech Enthusiasts: Anyone interested in understanding how Big Data and data analytics transform business insights and decision-making.
Pre-requisites .
To fully benefit from this training program, participants should have a basic understanding of programming concepts (e.g., variables, data types, control flow) and familiarity with data analysis concepts (e.g., descriptive statistics, data visualization). While prior experience with Big Data technologies is not required, a foundational understanding of databases and SQL would be beneficial.
Data Analytics and Big Data Course Content .
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Module 1 Introduction to Data Analytics and Big Data.- What is data analytics? Overview of the data analytics lifecycle: Data collection, processing, analysis, and visualization.
- Defining Big Data: Understanding the 5 Vs of Big Data (Volume, Variety, Velocity, Veracity, and Value).
- The intersection of Big Data and data analytics: How Big Data drives advanced analytics and real-time insights.
- Common tools and platforms: Overview of Hadoop, Spark, NoSQL databases, and distributed data processing systems.
- Real-world applications of data analytics and Big Data: Fraud detection, personalized marketing, predictive maintenance, and more.
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Module 2 Big Data Architecture and Processing Frameworks.- Introduction to Hadoop: Hadoop Distributed File System (HDFS) and MapReduce for batch processing.
- Apache Spark: In-memory data processing for fast analytics and machine learning on Big Data.
- Data storage for Big Data: NoSQL databases (e.g., MongoDB, Cassandra) vs. traditional relational databases.
- Data preprocessing: Techniques to clean, transform, and prepare large datasets for analytics.
- Integration of data processing and analytics: How to combine Spark with machine learning libraries like MLlib.
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Module 3 Data Analytics Techniques for Big Data.- Exploratory data analysis (EDA): Techniques for summarizing and visualizing Big Data to uncover hidden insights.
- Descriptive, predictive, and prescriptive analytics: Understanding the different types of data analysis and their use cases.
- Machine learning for Big Data: Scaling algorithms like decision trees, k-means, and logistic regression on large datasets.
- Real-time data analytics: Analyzing data streams in real-time for quick decision-making.
- Case studies: Applications of data analytics in industries like finance (fraud detection), healthcare (patient outcomes), and marketing (customer segmentation).
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Module 4 Data Visualization and Reporting for Big Data.- The role of data visualization in Big Data: How to communicate insights effectively using charts, dashboards, and reports.
- Tools for visualizing Big Data: Tableau, Power BI, and custom visualizations using Python (Matplotlib, Seaborn).
- Best practices for visualizing complex data: Creating interactive dashboards, real-time visualizations, and data storytelling.
- Working with unstructured data: Visualizing insights from text, image, and video data.
- Reporting frameworks for Big Data: Automating reports and real-time analytics in business dashboards.
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Module 5 Big Data in Action: Industry Use Cases and Future Trends.- Real-world applications of Big Data analytics across industries: Retail, manufacturing, healthcare, and government.
- Predictive and prescriptive analytics in Big Data: How companies use advanced analytics to optimize operations and predict future outcomes.
- AI and machine learning in Big Data: Leveraging AI to enhance decision-making, personalization, and automation.
- Emerging trends in Big Data: Data lakes, cloud-based analytics, edge computing, and AI-powered analytics.
- Challenges and considerations: Data privacy, security, governance, and ethical concerns with Big Data.
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