Requirements
- Basic understanding on Cyber Security
Features
- Cyber Security Zero to Hero with Realtime Exposure
Target audiences
- Switching Domains, Job Seekers, Freshers, Cyber Security Engineers, Employees, Technical Managers, Technical Leads, Team Leads
MFH Data Engineering Realtime Program :
1. Business Client projects (Live Sessions)
2. Beginners to Advanced Labs covering all Topics
3. Access to Learning Management System.
4. Interview Preparation (Tips from Architects, Interview Questions, Mock Interviews)
Duration : 3 Months
WA to enquire : https://wa.me/917671801206
Join WA Grp : https://chat.whatsapp.com/IsdDmAkrgAMAKftWhv4Hso
What is Data science Program ?
Through this program you will get opportunity to work on Business Client Real-time / Live Projects.
You will also get trained through Cyber Security Training & Labs (Optional).
Labs will you practice every topic starting from Beginner to Advanced.
Other than Client Projects, What is Course Content for Labs & Theory ?
Whatsapp for course content.
How will you get Hands-on ?
MFH will share all the client projects with Real-time Program members.
You can join all or few client projects of your choice.
You can showcase these client projects in your profile.
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.
