In this course you will go through completed Client Projects by MFH.

1. Client Projects
2. Client Projects Architectures
3. Project Documentation / Runbooks
4. Step by Step Lab procedures to simulate client projects.

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Live Sessions will be conducted on Microsoft Teams.

Data science Curriculum

1. Foundations of Data Science Introduction to Data Science
– History and Evolution
– Roles in Data Science (Data Analyst, Data Engineer, Data Scientist)

2. Mathematics and Statistics
– Linear Algebra
– Probability Theory
– Descriptive Statistics
– Inferential Statistics
– Hypothesis Testing

3. Programming for Data Science
– Python Programming
– R Programming
– Data Structures and Algorithms
– Version Control (e.g., Git)

4. Data Manipulation and Analysis
– Data Wrangling
– Data Cleaning
– Data Transformation
– Data Exploration
– Exploratory Data Analysis (EDA)
– Summary Statistics

5. Data Visualization
– Matplotlib
– Seaborn
– Plotly
– ggplot2 (R)
– Tableau
– Power BI

6. Database Management
– SQL
– NoSQL Databases (e.g., MongoDB)
– Data Warehousing
– ETL (Extract, Transform, Load) Processes

7. Big Data Technologies
– Hadoop Ecosystem
– HDFS
– MapReduce
– Apache Spark
– Kafka

8. Machine Learning
– Supervised Learning
– Regression (Linear, Logistic)
– Classification (Decision Trees, SVM).
– Unsupervised Learning
– Clustering (K-means, Hierarchical)
– Dimensionality Reduction (PCA, t-SNE)
– Model Evaluation and Validation
– Cross-Validation
– Confusion Matrix
– ROC Curve
– Precision, Recall, F1 Score

9. Deep Learning
– Neural Networks
– Convolutional Neural Networks (CNNs)
– Recurrent Neural Networks (RNNs)
– Deep Learning Frameworks (TensorFlow, PyTorch)

10. Natural Language Processing (NLP)
– Text Preprocessing
– Sentiment Analysing
– Text Classification
– Topic Modeling

11. Data Engineering
– Data Pipeline Development
– Data Integration
– Data Storage Solutions
– Scalability and Performance Optimization

12. Cloud Computing for Data Science
– AWS
– Google Cloud Platform
– Azure
– Big Data Services in the Cloud

13. AI and Machine Learning Operations (MLOps)
– Model Deployment
– Model Monitoring and Management
– CI/CD for Machine Learning

14.Ethics in Data Science
– Data Privacy
– Bias and Fairness
– Ethical Use of Data

15. Projects and Applications
– Real-world Data Science Projects
– Kaggle Competitions
– Capstone Projects

16. Soft Skills
– Communication Skills
– Storytelling with Data
– Business Acumen
– Collaboration and Teamwork

By covering these topics, you’ll build a solid foundation in data science and be well-prepared to tackle real-world data challenges.