Data Analytics & Science Course Combined Foundation & Intermediate Levels: [BEC_DA_CO] JUL 5 – OCT 5

Categories: Data Analytics and Science

About Course

When booked together, this course combines our Foundation and Intermediate Data Analytics courses into a single package at a discounted price. Click the links below for detailed information on each course.

Course Overview:

This 12-week comprehensive Data Analytics and Science course is a progressive Journey from the Foundation to the Intermediate level of the Data Analytics training programme, covering all the Modules in the following courses:

Foundation Level: Data Analytics [BEC_DA_FO_NYSC] – APRIL 12– MAY 10

Intermediate Level: Applied Data Science [BEC_DA_IM_NYSC] – MAY 17 – JUL 12

Course Content

Week 1: Introduction to Programming for Data
o Overview of Python/SQL for data analysis o Basic programming concepts (variables, loops, functions) o Working with libraries (Pandas, Numpy)

Week 2-3: Exploratory Data Analysis (EDA)
o Understanding data distribution o Identifying patterns and relationships o EDA techniques and tools

Week 4: Data Manipulation
o Data transformation and cleaning using Python o Aggregating and summarising data o Dealing with time series data

Week 5-6: Introduction to Machine Learning
o Basics of supervised and unsupervised learning o Building simple models (linear regression, classification) o Evaluating model performance

Week 7: Data Visualisation and Reporting
o Advanced visualisation techniques o Interactive dashboards (Power BI/Tableau) o Automating reports

Week 8: Capstone Project
o Applying data analysis techniques to a real-world problem o Developing a presentation based on findings and insights

Week 9: Advanced Data Visualisation
1. Custom visualisations with Python (Matplotlib, Seaborn) 2. Data storytelling and dashboard design 3. Case studies in visualisation

Week 10: Machine Learning Models
1. Decision trees, Random forests, and K-Nearest Neighbors 2. Feature engineering and model optimisation 3. Hyperparameter tuning

Week 11: Working with Unstructured Data
1. Introduction to text data and natural language processing (NLP) 2. Basic sentiment analysis 3. Handling large datasets (Hadoop/Spark) • Week : Predictive Analytics 1. Building predictive models (time series, forecasting) 2. Evaluating and fine-tuning models 3. Case studies in predictive analytics

Week 12: Hands-on Data Science Project
1. Developing a data pipeline 2. Presenting and defending model results 3. Collaboration and feedback • Final Project Presentation 1. Comprehensive project incorporating learned techniques 2. Peer review and feedback

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Data Analytics & Science Course Combined Foundation & Intermediate Levels: [BEC_DA_CO] JUL 5 – OCT 5

About Course

Course Content

Week 1: Introduction to Programming for Data
o Overview of Python/SQL for data analysis o Basic programming concepts (variables, loops, functions) o Working with libraries (Pandas, Numpy)

Week 2-3: Exploratory Data Analysis (EDA)
o Understanding data distribution o Identifying patterns and relationships o EDA techniques and tools

Week 4: Data Manipulation
o Data transformation and cleaning using Python o Aggregating and summarising data o Dealing with time series data

Week 5-6: Introduction to Machine Learning
o Basics of supervised and unsupervised learning o Building simple models (linear regression, classification) o Evaluating model performance

Week 7: Data Visualisation and Reporting
o Advanced visualisation techniques o Interactive dashboards (Power BI/Tableau) o Automating reports

Week 8: Capstone Project
o Applying data analysis techniques to a real-world problem o Developing a presentation based on findings and insights

Foundation Course Examination

Week 9: Advanced Data Visualisation
1. Custom visualisations with Python (Matplotlib, Seaborn) 2. Data storytelling and dashboard design 3. Case studies in visualisation

Week 10: Machine Learning Models
1. Decision trees, Random forests, and K-Nearest Neighbors 2. Feature engineering and model optimisation 3. Hyperparameter tuning

Week 12: Hands-on Data Science Project
1. Developing a data pipeline 2. Presenting and defending model results 3. Collaboration and feedback • Final Project Presentation 1. Comprehensive project incorporating learned techniques 2. Peer review and feedback

Intermediate Course Examination

Earn a certificate

About Course

Course Content

Week 1: Introduction to Programming for Data o Overview of Python/SQL for data analysis o Basic programming concepts (variables, loops, functions) o Working with libraries (Pandas, Numpy)

Week 2-3: Exploratory Data Analysis (EDA) o Understanding data distribution o Identifying patterns and relationships o EDA techniques and tools

Week 4: Data Manipulation o Data transformation and cleaning using Python o Aggregating and summarising data o Dealing with time series data

Week 5-6: Introduction to Machine Learning o Basics of supervised and unsupervised learning o Building simple models (linear regression, classification) o Evaluating model performance

Week 7: Data Visualisation and Reporting o Advanced visualisation techniques o Interactive dashboards (Power BI/Tableau) o Automating reports

Week 8: Capstone Project o Applying data analysis techniques to a real-world problem o Developing a presentation based on findings and insights

Foundation Course Examination

Week 9: Advanced Data Visualisation 1. Custom visualisations with Python (Matplotlib, Seaborn) 2. Data storytelling and dashboard design 3. Case studies in visualisation

Week 10: Machine Learning Models 1. Decision trees, Random forests, and K-Nearest Neighbors 2. Feature engineering and model optimisation 3. Hyperparameter tuning

Week 12: Hands-on Data Science Project 1. Developing a data pipeline 2. Presenting and defending model results 3. Collaboration and feedback • Final Project Presentation 1. Comprehensive project incorporating learned techniques 2. Peer review and feedback

Intermediate Course Examination

Earn a certificate

Student Ratings & Reviews

Week 1: Introduction to Programming for Data
o Overview of Python/SQL for data analysis o Basic programming concepts (variables, loops, functions) o Working with libraries (Pandas, Numpy)

Week 2-3: Exploratory Data Analysis (EDA)
o Understanding data distribution o Identifying patterns and relationships o EDA techniques and tools

Week 4: Data Manipulation
o Data transformation and cleaning using Python o Aggregating and summarising data o Dealing with time series data

Week 5-6: Introduction to Machine Learning
o Basics of supervised and unsupervised learning o Building simple models (linear regression, classification) o Evaluating model performance

Week 7: Data Visualisation and Reporting
o Advanced visualisation techniques o Interactive dashboards (Power BI/Tableau) o Automating reports

Week 8: Capstone Project
o Applying data analysis techniques to a real-world problem o Developing a presentation based on findings and insights

Week 9: Advanced Data Visualisation
1. Custom visualisations with Python (Matplotlib, Seaborn) 2. Data storytelling and dashboard design 3. Case studies in visualisation

Week 10: Machine Learning Models
1. Decision trees, Random forests, and K-Nearest Neighbors 2. Feature engineering and model optimisation 3. Hyperparameter tuning

Week 12: Hands-on Data Science Project
1. Developing a data pipeline 2. Presenting and defending model results 3. Collaboration and feedback • Final Project Presentation 1. Comprehensive project incorporating learned techniques 2. Peer review and feedback