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Programming
Data Analytics & Visualisation with Python, R & SQL
Data Analytics & Visualisation with Python, R & SQL
Curriculum
12 Sections
41 Lessons
12 Weeks
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Introductory Statistics and Understanding Data
6
1.0
Understanding Data and Its Elements
1.1
Measures of Central Tendencies: Mean, Median, Mode
1.2
Measures of Dispersion: Range, Variance, Standard Deviation
1.3
Population and Samples
1.4
The Normal Distribution
1.5
Basics of Hypothesis Testing (Null and Alternate Hypotheses)
Python Basics for Data Analysis
5
2.1
Understanding Python Syntax and Data Types
2.2
Variables, Expressions, and Statements
2.3
Control Flow: Loops and Conditional Statements
2.4
Lists, Tuples, Dictionaries, and Sets
2.5
Manipulating Data Structures Using Built-In Methods
R Basics for Data Analysis
5
3.0
Understanding R Syntax and Data Types
3.1
Basic Operations and Functions in R
3.2
Vectors, Lists, Matrices
3.3
Data Frames, and Factors
3.4
Manipulating Data Structures Using R Functions
Data Preparation Techniques
5
4.0
Handling Missing Data, Outliers, and Duplicates
4.1
Using Python Libraries (Pandas)
4.2
Using R Libraries (dplyr & tidyr)
4.3
Normalization and Scaling of Data
4.4
Applying Filters, Grouping, and Aggregations
Introduction to Data Visualization
3
5.0
Overview of Python Visualization Libraries: Matplotlib and Seaborn
5.1
Overview of R Visualization Libraries: ggplot2
5.2
Hands-On Exercises for Creating Simple Visualizations
Advanced Visualization with Python
2
6.0
Customizing Plots with Matplotlib (Colors, Labels, Styles)
6.1
Creating Interactive Visualizations with Plotly and Bokeh
Advanced Visualization with R
2
7.0
Building Complex Visualizations with ggplot2
7.1
Interactive Visualizations with Shiny
Introduction to SQL for Data Analysis
5
8.0
Introduction to Databases and SQL
8.1
Writing Simple Queries (SELECT, WHERE, ORDER BY)
8.2
Aggregating Data (GROUP BY, HAVING, COUNT, SUM, AVG)
8.3
Connecting Python and R to SQL Databases
8.4
Retrieving and Manipulating Data Using SQL
Advanced SQL and Database Management
2
9.0
Joins (INNER, OUTER, LEFT, RIGHT)
9.1
Subqueries and Nested Queries
Integrating SQL with Python
2
10.0
Using Python Libraries (e.g., SQLAlchemy) for Querying
10.1
Automating Database Operations with Python Scripts
Integrating SQL with R
2
11.0
Using R Libraries (e.g., RSQLite) to Access Databases
11.1
Conducting Data Analysis on SQL Outputs
Capstone Project
2
12.0
Project Development
12.1
Project Presentation
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