Certificate in Data Science – EDUREX – Professional Courses, Certifications

ByUniversity of Ibadan

Overview
Curriculum
Institution

Data Science is a multidisciplinary field that combines techniques from statistics, mathematics, programming, and domain expertise to extract insights and knowledge from data. It involves collecting, cleaning, analyzing, and interpreting large volumes of structured and unstructured data to make data-driven decisions and solve complex problems.

What you’ll learn

Comprehensive understanding of data science concepts, tools, and techniques.
Developing skills in data manipulation, analysis, and visualization.
Learning how to apply statistical and machine learning algorithms to extract insights and make predictions.
Ability to work with big data and utilize distributed computing frameworks.
Building a portfolio of projects that demonstrate proficiency in data science.

Requirements

Basic proficiency in the English language, numeracy and computer literacy.
Basic understanding of mathematics and statistics.
Proficiency in at least one programming language, preferably Python or R.
Familiarity with data manipulation and analysis using tools like spreadsheets or databases.
Candidates must have access to either a computer or smartphone with internet connectivity.
Candidate must be equipped with quality webcam and headphones.

Duration & Fees

Regular – 10 Weeks – ₦230,000
Fast-Track – 8 Weeks – ₦345,000

Program Dates

August – October, 2024
October – December, 2024
February – April, 2025
May – July, 2025

Curriculum

10 Sections
21 Lessons
10

Expand all sectionsCollapse all sections

Module 1: Introduction to Data Science
2
- 1.1
  Understanding the fundamentals of data science, its applications, and its role in various industries
- 1.2
  Exploring the data science lifecycle, including data collection, preprocessing, analysis, and visualization
Module 2: Programming for Data Science
3
Module 3: Data Exploration and Visualization
2
- 3.1
  Understanding exploratory data analysis techniques to gain insights into the data.
- 3.2
  Using data visualization tools, such as Matplotlib, Seaborn, or ggplot in R, to present and communicate data effectively.
Module 4: Statistical Analysis and Modeling
2
- 4.1
  Learning statistical concepts and techniques for data analysis, including hypothesis testing, regression analysis, and correlation analysis.
- 4.2
  Building predictive models using techniques such as linear regression, logistic regression, decision trees, and random forests.
Module 5: Machine Learning
3
Module 6: Big Data Analytics
2
- 6.1
  Handling and processing large-scale datasets using technologies like Hadoop and Spark.
- 6.2
  Understanding distributed computing and parallel processing for efficient analysis of big data.
Module 7: Data Mining and Text Analytics
2
- 7.1
  Extracting useful information and patterns from large datasets using data mining techniques.
- 7.2
  Analyzing unstructured data, such as text, using natural language processing (NLP) techniques.
Module 8: Data Ethics and Privacy
1
- 8.1
  Understanding ethical considerations and legal aspects related to data science, including privacy, data protection, and responsible data usage.
Module 9: Data Storytelling and Communication
2
- 9.1
  Developing skills to effectively communicate and visualize data insights to non-technical stakeholders.
- 9.2
  Presenting data-driven insights in a compelling and understandable manner.
Module 10: Real-world Projects and Applications
2
- 10.1
  Working on hands-on projects that simulate real-world data science problems.
- 10.2
  Applying the knowledge and skills acquired to solve practical data science challenges.

University of Ibadan