Data Science 102
Begins March 2, 2019 | 7 Weeks | S$2000 (Up to 100% Subsidies Available)
Schedule: 8th Dec 18 to 6th Jan 19 (Bi-weekly classes)
Learn Machine Learning Using Python
What is DS102?
Data science and analytics are the current hot topics in both academia and industry. This course aims to introduce data science and analytics to students with no relevant background. Through this course, students will learn the types of problems that can be solved by data science and analytics.
The students will also be introduced to the three broad topics in data science and analytics: data management, data visualisation and machine learning techniques. Learning in this course will be very “hands-on” and classes are designed to allow students to practise the techniques and concepts discussed in the lectures.
Data Science Track
The Data Science 101 (DS101) course is built for beginners with no background in programming. Finishing DS101 prepares students for the Data Science 102 (DS102) course, where they learn about data analytics toolsets in Python. DS102 can also be taken by software engineers who are already well versed in Python. In our master class, Data Science 103, students will graduate with data know-how in an organisation setting and be certified as a data analyst by our academy.
DS102 Course Details
DS102 will be held once a week over 7 consecutive weeks. Each session would be 3 hours long held at 991D Alexandra Road #01-22/23 Singapore 119972. The course fees is $2000 before subsidy however Singaporeans and PRs are eligible for subsidies of up to 100%.
This course is an advance Python course and trainees are required to have the prerequisites before commencing the course. These prerequisites are also covered in our DS101 series.
DS102 is our advanced course after DS101 and hence all students are already expected to have a good working knowledge of Python programming. If you find that you do not have these competencies, consider taking the DS101 course instead.
BASIC PYTHON SYNTAX AND DATA STRUCTURE
Familiar with python syntax, and the data structure (list, dictionaries, tuples)
PYTHON CONDITIONS AND ITERATIONS
Familiar with if, elif and else conditional constructs, as well as writing iterations using python for and while loops
FUNCTIONS AND LIBARIES
Know how to write python functions, read python library documentations and make use of python libraries
Find out what you will learn throughout the 7 weeks course.
Project 1: Exploratory Analysis (Due on Week 2)
- Practice exploratory data analysis on movie dataset
- Required to present approach on week 2
Project 2: Visualisation of Food Data (Due on Week 4)
- Practice data visualisation concepts and libraries 1b
- Required to present approach on week 4
Project 3: Scraping Job Data (Due on Week 5)
- Practice web scraping libraries and concepts
- Required to present approach on week 5
Project 4: Yelp Reviews (Due on Week 6)
- Students are to use libraries learnt to perform text mining on Yelp reviews
- Required to present approach on week 6
Project 5: Passenger Survival Project (Due on Week 7)
- Consolidation project where students will be able to apply the concepts they learn from the lessons, specifically, Lesson 1, Lesson 2, Lesson 3, and Lesson 6
- Required to present approach on week 7
Lecture 1a: Introduction to Pandas
- What is Pandas, and how it is used in the industry
- Data structure of Pandas (series and dataframe)
- Pandas series (create, access data, changing data, checking for null values, boolean selector)
- Pandas dataframe (create, changing the index of the rows and columns, adding new series to a dataframe)
Lecture 1b: Performing statistical analysis with Pandas
- Getting measures of centres (mean, median, mode), and how to interpret them
- Getting the measures of spread (variance and standard deviation), and how to interpret them
- Obtaining skewness and kurtosis, and how to interpret them
Lab Exercise 1 (Due on Week 2)
Lecture 2: Data manipulation with Pandas
- Data wrangling using Pandas
- Data cleaning using Pandas (handle missing data, normalising datatypes, changing casing, renaming columns, and saving results)
- Data transformation using Pandas (centering data, scaling data, and correlation)
- Data aggregation
- Reshaping and pivoting the data
Lab Exercise 2 (Due on Week 3)
- Lecture 3a: Basic Visualisation Tools
- Line chart
- Bar chart
- Scatter plot
- Box plot
Lecture 3b: Advanced Visualisation Tools
- Heat map
- Factor plot
- Stacked bar chart
- Geospatial visualisation
- Word cloud
Lab Exercise 3 (Due on Week 4)
Lecture 4a: Introduction to HTML Structure
- HTML Structure (elements, tags, attributes, IDs, classes)
Lecture 4b: Basic Web Scraping Techniques
- Intro to Beautifulsoup
Lecture 4c: Advance Web Scraping Techniques
- Scraping for download links
- Scraping table data
- Data cleaning
- Data visualisation from web data
- Circumventing bot Detection
Lab Exercise 4 (Due on Week 5)
Lecture 5a: Text Mining
- Intro to text mining concept
Lecture 5b: Libraries
- Intro to NLTK library
Lecture 5c: Information Retrieval
- Concept of information retrieval
- Data cleansing for text data
- HTML decoding
- Removing URL
- Removing special characters
Lecture 5d: Information Extraction
- Data Preprocessing
- Stemming (Porter, Lancaster, Snowball)
- Pos Tagging
Lecture 5e: Data Mining
- Text Classification
- Sentiment Analysis
- Text Clustering
Lab Exercise 5 (Due on Week 6)
Lecture 6a: Intro to Machine Learning
- Intro to Machine Learning concept
- Prediction vs classification models
- Supervised Models vs Unsupervised Models
Lecture 6b: Libraries
- Intro to Scikit Learn
Lecture 6c: Implementation of Models
- Linear regression
- Multi-Linear regression
- Decision Tree
- Logistic regression
- Naïve Bayes
- Random Forest
- K-Means Clustering
- Hierachical Clustering
- How to measure effectiveness of model using RMSE & confusion matrix
Project Assignment (Due on Week 7)
Lecture 7: Consolidation & Closing
- Select students will be chosen to present their approach for the project
- Week 1 to week 6 review
- How to build a data pipeline
- How materials relates to data science in industry
Our courses are $2000 before subsidy. Students are eligible for course subsidies under the CITREP+ framework. Subsidies ranges from 70% to 100% depending on which tier you fall under.
CITREP+ supports local professionals in keeping pace with technology shifts through continuous and proactive training.For more information, you can visit the IMDA website here for more information.
Check out what students have to say about Hackwagon’s courses.
Daniel Adam Leong
The instructors are highly knowledgeable to be able to bring across new concepts as well as to explain the practicality of the concepts in the workforce! The atmosphere was also very conducive with meals and drinks being offered. Their follow-up service is also impeccable as they continue to update previous classes regarding new happenings in the data science field. Overall, it was a blast and I’m glad to have signed up for it!
The environment was really conducive for learning: spacious, with all the necessary amenities. Besides that, the instructor was very passionate, and was well-prepared for class with in-class worksheets to guide. During the lesson there were also teaching assistants who went around the room to provide guidance when help was needed. The TAs are contactable via telegram chat should you require any assistance with your homework.
Foo Rong Chang
“Excellent course where instructors are both knowledgeable and passionate. There are teaching assistants walking around to assist you in your learning and are more than willing to go out of their way to help you in other languages as well should you need the help. Helpful, friendly and approachable are the traits that they possess and have made the learning journey for us students so much easier and more fun as well!”
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GET YOUR ANSWERS TO QUESTIONS YOU MAY HAVE ABOUT THE COURSE.
What is Data Science?
Data scientists perform research and analysis on data and helps companies to improve business by predicting growth, trends and insights based on huge amounts of data.
Why Learn Data Science?
Data Science was voted as the #1 Job of 2016 by Glassdoor and demand for Big Data jobs are expected to increase up exponentially in the future.
What is the Expected Pay of a Data Scientist?
Data Scientists earn an estimated mid-career salary of $104,000 annually.
My company has data science problems, what do I do?
If your company has seemingly huge amounts of data, learning data science skills will allow you to manipulate that data into actionable insights. Should the problems be tough to solve, our experienced instructors can solve your data science problems together in the capacity of a consultant.