Data Science for Big Data Analytics-Essentials

Big Data Fundamentals:

• Understanding core concepts of big data, including its types and characteristics.

Data Handling:

• Introduction to tools and frameworks like Hadoop and Spark for processing large datasets.

Data Processing Techniques:

• Methods for efficiently processing and analyzing big data.

Statistical Analysis:

• Application of statistical techniques to derive insights from large datasets.

Basic Data Science Knowledge:

• Understanding of fundamental data science concepts and methods.

Statistics:

• Proficiency in basic statistical methods and analysis.

Programming Skills:

• Familiarity with programming languages like Python or R used in data science.

Data Handling:

• Experience with data manipulation and cleaning techniques.

Big Data Tools Proficiency:

• Ability to use tools and technologies like Hadoop, Spark, and other big data frameworks.

Data Analysis and Visualization:

• Skills in analyzing large datasets and visualizing results effectively.

Statistical Analysis:

• Advanced statistical techniques for handling big data, including hypothesis testing and regression analysis.

Data Management:

• Expertise in data handling, cleaning, and preprocessing for big data environments.