Data Analytics and Science Mastery Course:Foundation, Intermediate & Advanced Levels: [BEC_DA_CU_04] JUL 5 – NOV 16

Data Analytics and Science Mastery Course:Foundation, Intermediate & Advanced Levels: [BEC_DA_CU_04] JUL 5 – NOV 16

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About Course

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

Course Overview:

This 16-week comprehensive Data Science and Analytics Course is a progressive Journey from the Foundation to the Advanced level of the Data Analytics and Science training programme, covering all the Modules in the following courses:

Foundation Level: Data Analytics [BEC_DA_FO_01] – APR 5 – MAY 25

Intermediate Level: Applied Data Science [BEC_DA_IM_02] – MAY 31 – JUN 22

Mastery Level: Mastering Data Science [BEC_DA_MA_11] – JUN 28- JLY 20

 

 

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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)
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

Week 13: Deep Learning Fundamentals
o Introduction to neural networks o Building deep learning models (TensorFlow, Keras) o Applications of deep learning (image, text, voice data)

Week 14: Model Deployment and Optimization
o Deploying models using cloud platforms (AWS, Google Cloud) o Real-time data processing and model monitoring o Optimizing models for performance and scalability

Week 15: Advanced Predictive and Prescriptive Analytics
o Prescriptive models for decision-making o Advanced time series analysis o Real-world case studies

Week 16: Final Capstone Project
o End-to-end project development and deployment o Presenting solutions to real business challenges o Feedback and review

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