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

Click to Enroll

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.