Professional Diploma in Data Analytics

Course Overview

The “Professional Diploma in Data Analytics” is a comprehensive 12-month program designed to equip learners with advanced knowledge and skills in data analytics, data science, and professional data analytics. This three-semester program covers essential topics and tools, providing a progressive learning path from foundational to advanced levels. By the end of each semester, learners will achieve specific qualifications: Certificate in Data Analytics (Semester 1), Diploma in Data Science (Semester 2), and Professional Diploma in Data Analytics (Semester 3).

Semester 1: Certificate in Data Analytics (4 months)

This semester provides a strong foundation in data analytics, covering the basic tools, techniques, and concepts required to perform data analysis.

Module 1: Introduction to Data Analytics (40 hours)

Introduction to Data Analytics
Overview of data analytics and its importance
Key concepts and terminology
Data Collection and Preparation
Data sources and types
Data cleaning and preprocessing techniques
Exploratory Data Analysis (EDA)
Descriptive statistics
Data visualisation techniques (histograms, box plots, scatter plots)

Module 2: Basic Statistical Analysis (40 hours)

Fundamentals of Statistics
Measures of central tendency and dispersion
Probability concepts and distributions
Hypothesis Testing
Null and alternative hypotheses
t-tests, chi-square tests, and ANOVA
Correlation and Regression
Correlation analysis
Simple linear regression

Module 3: Data Manipulation with Excel and SQL (40 hours)

Advanced Excel for Data Analysis
Excel functions and formulas
PivotTables and PivotCharts
Introduction to SQL
Basic SQL queries (SELECT, INSERT, UPDATE, DELETE)
Data manipulation and aggregation in SQL
Combining Excel and SQL for Data Analysis
Importing and exporting data between Excel and SQL
Real-world data analysis projects

Semester 2: Diploma in Data Science (4 months)

This semester builds on the foundational knowledge from the first semester, introducing more advanced data science concepts, tools, and techniques.

Module 4: Programming for Data Science with Python (60 hours)

Python Programming Basics
Python syntax and data types
Control flow and functions
Data Manipulation with Pandas
Working with DataFrames and Series
Data cleaning and transformation with Pandas
Data Visualization with Matplotlib and Seaborn
Creating and customizing plots
Advanced visualization techniques

Module 5: Machine Learning Fundamentals (60 hours)

Introduction to Machine Learning
Overview of machine learning concepts
Supervised vs. unsupervised learning
Supervised Learning Techniques
Regression algorithms (linear regression, decision trees)
Classification algorithms (logistic regression, k-nearest neighbors)
Unsupervised Learning Techniques
Clustering algorithms (k-means, hierarchical clustering)
Dimensionality reduction (PCA, t-SNE)

Module 6: Data Science Tools and Techniques (40 hours)

Introduction to R Programming
Basics of R syntax and data types
Data manipulation with dplyr and tidyr
Advanced-Data Analysis with R
Statistical modelling in R
Data visualisation with ggplot2
Big Data and Cloud Computing
Introduction to Hadoop and Spark
Cloud platforms for data science (AWS, Google Cloud, Azure)

Semester 3: Professional Diploma in Data Analytics (4 months)

This semester focuses on advanced topics in data analytics, including deep learning, big data analytics, and professional skills for data scientists.

Module 7: Advanced Machine Learning and Deep Learning (60 hours)

Advanced Machine Learning Techniques
Ensemble methods (random forests, gradient boosting)
Model evaluation and tuning
Introduction to Deep Learning
Neural networks and deep learning basics
Building deep learning models with TensorFlow and Keras
Advanced Deep Learning Applications
Convolutional neural networks (CNNs) for image analysis
Recurrent neural networks (RNNs) for time series analysis

Module 8: Big Data Analytics (40 hours)

Big Data Concepts and Technologies
Overview of big data and its challenges
Introduction to big data technologies (Hadoop, Spark)
Data Processing with Spark
Spark architecture and components
Using Spark for data processing and analysis
Hands-On Big Data Projects
Real-world big data analytics projects
Practical exercises with Hadoop and Spark

Module 9: Professional Skills and Ethics for Data Scientists (30 hours)

Professional Skills for Data Scientists
Effective communication and presentation skills
Time management and productivity techniques
Ethical Issues in Data Science
Ethical considerations in data science
Privacy and data protection
Career Development and Networking
Building a professional portfolio
Job search strategies and networking

Module 10: Capstone Project (30 hours)

Project Planning and Design
Selecting a project topic
Planning and designing the project
Project Development
Implementing the project
Integrating various data analytics concepts
Project Presentation and Review
Presenting the project to peers and instructors
Receiving feedback and making improvements

Conclusion

The “Professional Diploma in Data Analytics” program provides a comprehensive and progressive learning path from foundational data analytics to advanced data science and professional data analytics. Through a blend of theoretical knowledge and practical experience, learners will develop the skills needed to excel in the field of data analytics. By the end of the course, participants will be well-equipped to tackle real-world data challenges and advance their careers in data science and analytics.

Join us at CTTI and embark on a transformative journey to mastering data analytics.