Data Science

Get Certified as a Data Science Specialist.

Syllabus for Data Science with Python course (18 weeks):

1.  Introduction to data science: (1 week)
2. What is data science (how is it different from Data Analytics and Data Engineering)
3. Examples of real-life data science applications (as an inspiration for final capstone project)
4. Data science job market
5. Life of a data scientist
6. Data science reading resources

Brushing up your statistics: (1.5 weeks)

1. Basics of probability
2. Explore statistical distribution
3. Understand population and sampling distributions

Python for data science: (4 weeks)

1. Part I: Set up the Google colab and Jupyter notebook in your system
2. Part II: Python basics

a. functions,
b. strings,
c. variables,
d. numbers
e. lists,
f. dictionaries
g. conditional logics

3. Part III: Object-oriented programming with python (class and objects)
4. Part IV: Data manipulation with Pandas
5. Part V: Data visualization Matplotlib, Seaborn and interactive plotting with Bokeh
6. Part VI: Statistics with python: introduction to numpy, scipy and statsmodel
7. Part VII: Web-scraping with Python: APIs and beautiful soup
8. Project I: Data exploration with python

Experimental Design: (1 week)

1. Hypothesis testing
2. A/B testing
3. Project II: Propose an experimental design and apply inferential statistics and statistical testing on the problem

Working with SQL: (2.5 weeks)

1. Difference between SQL and NoSQL
2. Basic querying
3. Aggregation and grouping
4. Join
5. How to combine SQL with python

Introduction to Machine Learning: Supervised Learning (4 weeks)

1. Introduction:

a. Data cleaning
b. Data exploration
c. Feature engineering
e. What does the Machine Learning algorithms do?
f. Difference between supervised and unsupervised Machine Learning
g. Difference between classification and regression
h. Introduction to Scikit Learn for Machine Learning

2. Linear regression
3. Logistic regression
4. Lasso and ridge regression
5. Support Vector Machine
6. Naïve Bayes
7. K-nearest neighbours
8. Random Forests
9. Gradient boosting
10. Dimensionality reductio
11. Ensembling multiple models
12. Project III: Propose your 1^st Supervised Learning project

Introduction to Machine Learning: Unsupervised Learning: (2 weeks)

1. Introduction to Clustering and cluster evaluation
2. K-means clustering
3. Hierarchical clustering

Final Capstone Project: (2 weeks)

1. Propose your final capstone project (a real-life application-oriented yet fun for you)
2. Perform necessary statistical exploration, build models, evaluate model performance, add insightful visualization
3. Present your project