Specialist Diploma Big Data Analytics Course with Machine Learning

Specialist Diploma Big Data Analytics Course with Machine Learning

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20 hours
10 Curriculum
$432.00$396.00
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Data Science and Artificial intelligence have transformed the world completely. Organizations around the world are leveraging artificial intelligence to avoid repetitive tasks and improve customer experience. Robots are taking on the world by storm and are continuously building intelligence comparable to human brains. Artificial Intelligence and Machine Learning are the highest-paying jobs in the world. As per a recent estimate, more than 90% of companies will use artificial intelligence in one way or the other to build or enhance their products and services. These companies are looking for people who are skilled in data science and AI. Unfortunately, the industry is facing an acute shortage of highly skilled people to fill the void. Therefore, OMNI ACADEMY Designed a Diploma in Big Data Analytics with Machine Learning Courses – Online Classes Available.
Program Key Learning  [ 5 Courses to make you a Big Data domain true expert ]
  1. BD-01   Big Data Analytics Foundation with Python
  2. BD-02   Machine Learning with Python- (Data Analysis, Data Visualization, Machine Learning– Apache Spark)
  3. BD-03   Microsoft Power BI and Big Data Visualization
  4. BD-04   Introduction to NoSQL Database
  5. BD-05   Big Data Technologies Infrastructure Design
Course Content: BD-01: Big Data Analytics Foundation with Python
  • Introduction to Big Data and Analytics
  • Overview of Python in Data Science
  • Python Libraries for Data Analysis: NumPy, Pandas
  • Data Preprocessing: Cleaning, Wrangling, Transformation
  • Exploratory Data Analysis (EDA) using Python
  • Data visualization techniques in Python (Matplotlib, Seaborn)
  • Case Study: Analyzing a real-world dataset with Python
2. BD-02: Machine Learning with Python (Data Analysis, Data Visualization, Machine Learning – Apache Spark)
  • Introduction to Machine Learning and its types (Supervised, Unsupervised, Reinforcement)
  • Python libraries for machine learning (Scikit-learn, TensorFlow, Keras)
  • Data Preprocessing for Machine Learning (Feature Scaling, Encoding)
  • Building Machine Learning models (Regression, Classification, Clustering)
  • Data Visualization for Machine Learning Insights
  • Introduction to Apache Spark for distributed machine learning
  • Hands-on: Building a Machine Learning model using Apache Spark MLlib
3. BD-03: Microsoft Power BI and Big Data Visualization
  • Introduction to Power BI and its components
  • Connecting Power BI to Big Data sources (SQL, NoSQL, Spark)
  • Data transformation and modeling in Power BI
  • Designing interactive dashboards for Big Data insights
  • Advanced visualization techniques for Big Data in Power BI
  • Using Power BI for storytelling with data
  • Case Study: Implementing a Big Data visualization project in Power BI
4. BD-04: Introduction to NoSQL Database
  • Introduction to NoSQL Databases and why they’re needed for Big Data
  • Types of NoSQL databases: Document, Key-Value, Columnar, Graph
  • Working with MongoDB: CRUD operations and aggregation
  • Introduction to Cassandra and HBase for Big Data storage
  • Data modeling in NoSQL databases
  • Performance optimization and indexing in NoSQL
  • Hands-on: Building a NoSQL database and querying data
5. BD-05: Big Data Technologies Infrastructure Design
  • Overview of Big Data Architecture and its components
  • Introduction to Hadoop Ecosystem: HDFS, MapReduce, YARN
  • Designing scalable Big Data infrastructure using Hadoop and Spark
  • Cloud technologies for Big Data (AWS, Azure, Google Cloud)
  • Data processing pipelines and data lakes
  • Best practices for Big Data infrastructure management
  • Hands-on: Designing a Big Data infrastructure solution
6. BD-06: Big Data Analytics Using Apache Hadoop
  • Introduction to Apache Hadoop and its components
  • Setting up Hadoop clusters and managing HDFS
  • Distributed computing with Hadoop MapReduce
  • Data ingestion and processing with Apache Pig and Hive
  • Managing large-scale data using Apache HBase
  • Best practices for Hadoop deployment and security
  • Hands-on: Implementing Hadoop-based Big Data analytics
7. BD-07: Advanced Machine Learning Algorithms
  • Deep Dive into Advanced Machine Learning Algorithms
  • Decision Trees, Random Forest, Gradient Boosting, XGBoost
  • Support Vector Machines (SVM) and Neural Networks
  • Ensemble Learning and Hyperparameter Tuning
  • Model evaluation techniques and optimization
  • Hands-on: Advanced Machine Learning model creation and evaluation
8. BD-08: Real-Time Data Processing with Apache Kafka
  • Introduction to Apache Kafka and its components
  • Kafka architecture: Producers, Consumers, Brokers, and Zookeeper
  • Real-time data ingestion with Kafka and Big Data systems
  • Integrating Apache Kafka with Hadoop and Spark
  • Data stream processing in Kafka with Apache Flink and Spark Streaming
  • Best practices for Kafka deployment and monitoring
  • Hands-on: Implementing real-time data streaming with Kafka
9. BD-09: Big Data Security and Governance
  • Introduction to Big Data Security and Privacy concerns
  • Security frameworks and models for Big Data (Hadoop, Spark)
  • Data governance practices in Big Data analytics
  • Ensuring compliance and data privacy with GDPR and other regulations
  • Auditing and monitoring in Big Data environments
  • Best practices for securing Big Data infrastructure
  • Hands-on: Implementing security measures in Big Data projects
10. BD-10: Capstone Project: Big Data Analytics with Machine Learning
  • Overview of the Capstone Project and its expectations
  • Developing an end-to-end Big Data solution with Machine Learning
  • Real-world dataset analysis using Python, Spark, and NoSQL
  • Building models and presenting actionable insights from Big Data
  • Data visualization using Power BI and presenting results
  • Final Presentation of Capstone Project
  • Evaluation and feedback from instructors
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