Data Science Short Courses

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Enrolled: 34 students
Duration: 80 hours
Lectures: 12
Level: Beginner


Working hours

Monday 9:30 am - 6.00 pm
Tuesday 9:30 am - 6.00 pm
Wednesday 9:30 am - 6.00 pm
Thursday 9:30 am - 6.00 pm
Friday 9:30 am - 5.00 pm
Saturday Closed
Sunday Closed

The study of data science will emphasize the importance of ethical considerations and privacy in handling data. It covers legal frameworks and best practices related to data collection, storage, and usage, ensuring responsible and ethical data handling. They will gain proficiency in programming languages commonly used in data science, such as Python or R, allowing them to write code, debug programs, and implement complex algorithms. They will learn to effectively communicate their findings and insights derived from data analysis, conveying complex information in a clear and understandable manner.

Career Outcomes

  • Data Analyst
  • Business Intelligence Analyst
  • Data Engineer
  • Junior Data Scientist
  • Data Architech

What is the target audience?

  • Mastering the field of data science begins with understanding and working with the core technology frameworks used for analyzing big data. You’ll learn the developmental and programming frameworks Hadoop and Spark used to process massive amounts of data in a distributed computing environment, and develop expertise in complex data science algorithms and their implementation using R, the preferred language for statistical processing. The insights you will glean from the data are presented as consumable reports using data visualization platforms such as Tableau.

    Once you have mastered data management and predective analytics techniques, you will gain exposure to state-of-the-art machine learning technologies. This expansive data science learning path will help you excel across the entire spectrum of big data and data science technologies and techniques.

Topics Covered

Computer Instruction and Data
Collecting and Analyzing Data
Understand how to Solve Problems with Algorithms 1
Understand How to Solve Problems with Algorithms 2
Structure, Manipulate and Represent Data
Developing and Testing Program Code
Developing a Simple Database using SQL
Data Retrieval with SQL
Inferential Statistics 1
Inferential Statistics 2
Data Visualization
Data Science Project Lifecycle
Faq Content 1
Faq Content 2

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