Lectures

Week	Topic(s)	Slide(s)	Additional Resources
Week I 10.02	Chapter 16 - Developing Efficient Algorithms [Liang] Chapter 3 - Analysis - Problem Solving with Algorithms and Data Structures using Python [Interactive Edition]	Ch16 : ppt | pdf Ch 3 : html
Week II 17.02	Chapter 17 - Sorting Chapter 6 - Searching and Sorting - Problem Solving with Algorithms and Data Structures using Python [Interactive Edition]	Ch17 : ppt | pdf Ch 3 : html
Week III 24.02	IPython, Jupyter Notebooks, Review of Python Data Types and  Structures, and Libraries IPython: Beyond Normal Python¶ Help and Documentation in IPython Keyboard Shortcuts in the IPython Shell IPython Magic Commands Input and Output History IPython and Shell Commands Errors and Debugging Profiling and Timing Code More IPython Resources - Review of Python Data Types and Stuctures (html) - Python Libraries (html)		- Jupyter Notebook Tutorial: The Definitive Guide (html) - A Beginner’s Tutorial to Jupyter Notebooks(html) - Jupyter Notebook: An Introduction (html) - Jupyter Notebook Users Manual (html) - 28 Jupyter Notebook Tips, Tricks, and Shortcuts (html) - Getting started with conda (html) - A gallery of interesting Jupyter Notebooks (html)
Week IV 02.03	Arrray-Oriented Programming with NumPy (html) Introduction to NumPy Understanding Data Types in Python The Basics of NumPy Arrays Computation on NumPy Arrays: Universal Functions Aggregations: Min, Max, and Everything In Between Computation on Arrays: Broadcasting Comparisons, Masks, and Boolean Logic Fancy Indexing Sorting Arrays Structured Data: NumPy's Structured Arrays		- tutprialspoint NumPy Tutorial (html) - Datacamp Python Numpy Array Tutorial (html) - The Ultimate Beginner’s Guide to NumPy (html) - A Complete Step-By-Step Numpy Tutorial (html)
Week V 09.3	Numpy - continued
Week VI 16.3	Break for Isolation. Please, stay at home and limit your movements outside of your homes except for your essential needs.
Week VII 23.03	Data Manipulation with Pandas¶ Introducing Pandas Objects Data Indexing and Selection Operating on Data in Pandas Handling Missing Data Hierarchical Indexing Combining Datasets: Concat and Append Combining Datasets: Merge and Join Aggregation and Grouping Pivot Tables Vectorized String Operations Working with Time Series High-Performance Pandas: eval() and query() Further Resources		- Pandas Pandas Tutorial: A Complete Introduction for Beginners (html) - EuroScipy 2016 Pandas Tutorial (html) - w3resource Pandas Tutorial(html) - Tutorialspoint Pandas turorial (html) - Java T point Pandas Tutorial (html) - geeksforgeeks Padas Tutorial (html)
Week VIII 30.3	Pandas - continued
Week IX 6.4	- Pandas - Continued - Ch 06 - Data Loading, Storage, and File Formats - Wes McKinney  (html) - Ch 08 - Strings - A Deeper  Look - Deitel (html) - Ch 09 - Files and Exceptions (html)		- The Best Format to Save Pandas Data (html) - Pandas: How to Read and Write Files (html) - Loading Data Into A Pandas Dataframe - A Performance Study  (html) - How to read most commonly used file formats in Data Science (using Python)? (html) - Importing Data into Pandas (html) - Pandas IO tools (text, CSV, HDF5, ...)  (html) - A Guide to the Newer Python String Format Techniques (html) - Common string operations (html)
Week X 11.4	Midterm Week
Week XI 20.4	Ch 10 - Review of OO Programming,            - operator overloading,            - names tuples            - data classes            - unit testing - doctest            - Time Series and Simple Linear Regression (html) Ch 04 - Visualization with Matplotlib - DS Handbook    - Introduction 04.00 (html)		- Primer on Python Decorators (html) - Decorators in Python (html) - 6 Essential Data Visualization Python Libraries - Matplotlib, Seaborn, Bokeh, Altair, Plotly, GGplot (html) - Tutorial: Comparing 7 Tools For Data Visualization in Python (html)
Week XII 27.04	Ch 12 - Natural Language Processing (NLP) (html)		- TextBlob: Simplified Text Processing (html) - Natural Language Processing for Beginners: Using TextBlob (html) - Introducing TextBlob (html) - spaCy (html) - spaCy 101: Everything you need to know (html) - spaCy Tutorial to Learn and Master Natural Language Processing (NLP) (html) - Natural Language Processing With spaCy in Python (html) - Textatistic (html)
Week XIII 04.05	Ch 15 - Machine Learning: Classification, Regression and Clustering (html)		- scikit-learn Machine Learning in Python (html) - Introduction to k-Nearest Neighbors: A powerful Machine Learning Algorithm (with implementation in Python & R) (html) - K Nearest Neighbors - Classification (html) - KNN Algorithm - Finding Nearest Neighbors (html) - Model Validation Techniques (html) - Regression Algorithms - Overview (html) - Commonly used Machine Learning Algorithms (html) - K-Means (html) - The Most Comprehensive Guide to K-Means Clustering You’ll Ever Need (html) - Dimensionality Reduction Algorithms: Strengths and Weaknesses (html) - Choosing the right estimator (html)
Week XIV 12.05	Chapter 16 - Deep Learning (html)		- Deep Learning Tutorial (html) - A Guide to Deep Learning by YN (html) -CNN Tutorial (html)
Week XV 19.05	Chapter 17 - Big Data: Big Data: Hadoop, Spark, NoSQL and IoT (html)		- Big Data & Analytics Tutorials (html) - Big Data Hadoop Tutorial (html) - NoSQL Databses (html) - Apache Spark Tutorial (html) - What is Big Data (html)

 

Grades

   - Grades (html)