Data Science
Track 1
Data Science Overview
- Introduction to Data Science
- Data Science in Different Sectors
- Python components and its Standard Libraries
- Introduction to Big Data and other Tools
Track 2
Statistics and Business Applications
- Introduction to Statistics
- Descriptive and Inferential Statistics
- Data Sampling and Data Distributions
- Hypothesis Testing
Track 3
Machine Learning
- Introduction to AI and Machine learning
- Data Preprocessing
- Supervised and unsupervised algorithms
- Time series and social network analysis
Track 4
Tools and Language
- Data Science with R and Python
- Data Visualizations Tools
- SQL and NoSQL Databases
- Integration with Hadoop Mapreduce and Spark
Track 5
Hands-On Learning
- Learn to use Pandas, NumPy and SciPy
- ML and NLP with Scikit-Learn
- Data Visualization using matplotlib
- Learn to work with different file types
- APIs collect the data from internet
Track 6
Capstone Project
- Property Price Prediction
- Customer Segmentation
- Loan Prediction Model
- Retail Store Insights
- Fraud Detection
Track 7
Career Service
- Career Advice
- Create a high-quality resume and cover letter
- Interview coaching and practice
- Job search Advice
- Mock interviews for both technical and non-technical topics
Track 8
Mentorship Support
- Setting learning goals
- Review of projects and exercises
- Industry insights
- Interview tips
- Career and Job Search advice
- Tracking weekly progress