Intermediate Guide To Data Analytics
This course deepens your data analytics skills. Work with real-world datasets, use industry tools, and create insights-driven reports.
Pre-requisite Program(s)
The following program(s) are pre-requisite to taking this program:
RWL Code
BLP-2935CPC5974
Category
Data Analytics
Program Type
Paid Program
Program Fee
₦45,000.00 (Preoder)
Publish Date
26/04/2025
Language
7 Lessons |
20hrs:30min
1
This lesson provides a comprehensive introduction to SQL for data analysis, starting with the setup of DB Browser for SQLite as a beginner-friendly database tool. It covers the installation process, database creation, and table structure definition using a practical CustomerData.sqlite example. The lesson explains the fundamental concept of SQL as a Structured Query Language for interacting with relational databases, and introduces the basic structure of databases with tables, rows, and columns. Students learn to write their first SQL queries using SELECT statements to retrieve data, apply WHERE clauses for filtering results based on specific conditions, and use ORDER BY to sort query outputs. The lesson includes practical examples with a Customers table containing customer information, and concludes with a hands-on challenge to reinforce the learned concepts.
This intermediate lesson expands SQL capabilities by introducing complex querying techniques and data combination methods. It covers logical operators (AND, OR, NOT) for creating sophisticated filtering conditions, and wildcard usage with LIKE for pattern matching in text searches. The lesson teaches table joining techniques using INNER and LEFT JOINs to combine related data from multiple sources, explores data aggregation using GROUP BY for summary statistics, and introduces the HAVING clause for filtering grouped results based on aggregate conditions.
This advanced lesson delves into sophisticated SQL techniques for complex data analysis scenarios. It covers the comprehensive use of built-in aggregate functions including COUNT, SUM, AVG, MIN, and MAX for statistical calculations. The lesson introduces subquery concepts for nested data retrieval, teaches the use of aliases and calculated columns for enhanced query readability and custom computations, explores advanced nesting techniques for multi-layered analysis, and concludes with creating simple views for reusable query templates.
This lesson introduces Python as a powerful programming language for data analysis, beginning with environment setup using Jupyter Notebook and Anaconda. It covers fundamental Python concepts including data types and structures such as lists, dictionaries, and tuples, progresses to basic script writing, and introduces the pandas library for data loading and reading operations. The lesson concludes with techniques for displaying basic data summaries and generating initial insights from datasets.
This lesson focuses on essential data preparation techniques using Python, covering comprehensive data cleaning methods including handling missing data with dropna and fillna functions. It teaches dataset filtering and slicing techniques, string function applications and conditional logic implementation, data combination methods using merge and concat operations, and new column creation using the apply function. The lesson concludes with an introduction to basic data visualization techniques for initial data exploration.
This lesson introduces R as a statistical programming language for data analysis, beginning with R and RStudio environment setup. It covers data importing techniques using read.csv function, data exploration methods using summary, str, and indexing functions for understanding dataset characteristics. The lesson teaches basic plotting techniques using the ggplot2 package for data visualization, and introduces the dplyr package for efficient data filtering and grouping operations.
This comprehensive lesson provides guidance on tool selection and integration in data analysis workflows, comparing the strengths and appropriate use cases for R, Python, and SQL. It introduces SPSS as an additional statistical analysis tool, explores the strengths and limitations of each platform for different analytical scenarios, covers interoperability concepts for using multiple tools in integrated workflows, and provides resources for continued learning including free tools, datasets, and community support options.