Diploma in Big Data Analytics
This Course Focuses on Acquiring Skills to Collect, Store, Process, and Analyze large Datasets, Using Specialized Tools to Extract Insights and Make Strategic Business Decisions.
1. BIG DATA ANALYTICS
What is Big Data? Characteristics (Volume, Velocity, Variety, Veracity, Value) - Importance and Applications - Introduction to Big Data Ecosystem and Tools.
Data Sources and Types (Structured, Semi-Structured, Unstructured) - Introduction to Data Warehousing - Tools and Techniques for Data Collection.
2. PYTHON FOR DATA ANALYSIS
Variables, Data Types, Loops, Functions, OOP.
NumPy: Arrays, Mathematical functions, Linear algebra.
Pandas: Series and DataFrame, Data cleaning, Merging, Grouping, Time series.
Matplotlib: Basic plotting, Histograms, 3D plotting.
3. HADOOP
Overview of Hadoop and HDFS - Introduction to MapReduce - Installing and setting up Hadoop - Understanding YARN and its role.
4. APACHE SPARK
Overview of Spark and its components (Spark Core, Spark SQL, Streaming, MLlib) - Comparison of Spark and Hadoop - Cluster Architecture.
Data loading and processing - Transformations and actions - Integrating Spark with Python - Working with Large-Scale Datasets.
MACHINE LEARNING BASICS
Introduction to Machine Learning (Supervised vs. Unsupervised) - Overview of Scikit-learn - Simple Algorithms: Linear Regression, Classification - Big Data Visualization Tools (Matplotlib, Plotly, Seaborn).