Mode: Public / Inhouse
Venue: Central Singapore
Training Fee: S$1337.50
1. Learning Outcome
About Python for Data Science
According to Indeed.com, the average base salary for a Data Scientist in Singapore in 2021 is around S$7100 monthly. Data science is a skill in demand in this modern age with buzzwords like big data, artificial intelligence, machine learning, deep learning etc.
In this data science course, you will be learning 3 important skills to becoming a data scientist.
Python
Python has been ranked the top programming language in 2021 by IEEE Spectrum. IEEE Spectrum’s rankings are one measure of what languages are worth investing time in to learn.
Python is so popular because it is easy to read, understand and use. This makes it a great place to start for people new to programming. With basic python, you can then move on to use the powerful data science packages available.
Data preparation, visualization, and analysis
Go through basic statistics, then move on to use Pandas, a Python library, to prepare your data for analysis.
Communicate insights by creating diagrams and charts that describe large amounts of data but are easy to understand.
Machine learning and AI
Next, learn about supervised and unsupervised machine learning. Use machine learning, Python libraries again, to go through data and try to predict future values or extract insights. Next, try different libraries to see which does predictions best.
Skills You Will Learn
Acquire basic mathematical and statistical knowledge
Set up your Python coding environment
Evaluate and pre-process data
Access financial data set
Perform data mining using Pandas
Perform data visualization using Matplotlib
Perform machine learning using scikit, Tensorflow, Keras
2. Breakdown of Training:
Curriculum For This Course
Day 1: Basics of Python
- Coding environment (e.g. Anaconda, Jupyter notebook ,Spyder, VSCode) and IDE
- Different Integrated Development Environment (IDEs)
- Python data types and variables e.g. int, string
- Syntax e.g. comments, print
- Control flow e.g. if/else, for, while
- Lambda functions
- Python data objects and usage e.g. get, join
Day 2: Data Mining Pandas
- Useful functions for data mining/data preprocessing
- Combine, merge, combine, append different data sets
- Evaluate data integrity and quality
- Clean, preprocess data
Day 2: Data Visualization
- Plotting functionalities
- Basic quantitative analysis for stocks using Python e.g. candlestick, Bollinger bands
Day 3: Machine Learning: Scikit Tensorflow, Keras
- Identifying various data sources e.g. Kaggle.com, Amazon’s AWS datasets
- Linear regression model vs Decision trees
- Recurrent Neural Network (RNN)
- Random Forest Regressor
- Cross validation to do better model evaluation
- Machine learning data pipeline
- Perform and validate test hypothesis
Content Of Lesson
More About the Course
Hands-on practice includes:
Python
- Set up an environment in line with best practices
- Debug common installation issues
- Use an IDE e.g. editor, console, debugger
- Fixing control flow, functions, data objects problems
Data mining: Pandas
- Use common python libraries
- Getting and consolidating data from Yahoo Finance/Quand/Intrinio
- Calculating risks of a stock portfolio
- Change parameters to do “what-if” analysis to see how portfolios perform
Data Visualization: Matplotlib
- Change parameters to do “what-if” analysis to see how portfolios perform
- Estimate prices e.g. Facebook stock
Machine Learning: scikit, Tensorflow, Keras
- Change parameters to do “what-if” analysis to see how portfolios perform
- Estimate prices e.g. Facebook stock, house prices
- Analyze same problem using different methods
- Discuss how to improve modelling approach to improve returns
Post Course Suport
At emarsity, we want to make sure our learners have a full grasp of the course attended. Therefore, post-course support is critical to ensure your learnings are clearly understood.
Our comprehensive Post Course Support Package comprises the following:
At emarsity, we want to make sure our learners have a full grasp of the course attended. Therefore, post-course support is critical to ensure your learnings are clearly understood.
1 x Free Refresher Course
Attend the course again within 6 months to refresh/reinforce your knowledge. Limited to 2 refreshers per course, you are advised to reserve your seat in advance.
6 Months Email Support
Email us your queries after the course, we will help with your queries.
Who is this training for?
- Anyone keen to learn about Data Science or Python programming
- Aspiring Data Science professionals
- Professionals who are working with large data sets and want to analyze more efficiently using Python
Pre-Requisite
Basic computer knowledge is essential as there will be practical sessions.
Bring along a laptop for hands-on session. (note: tablets and smartphones are not recommended)
SEO Course Instructor
Mr. Chirag is an experienced finance lecturer teaching Finance and Applied Statistics/Data Science techniques in Finance for the last 15 years. Having graduated from the prestigious Ivy League Columbia University, he proceeded to work for large investment banks in New York and Tokyo.
He enjoys training learners in modern data science & machine learning techniques with a focus on practical applications and applying concepts learned to solve real-world problems.
He is well versed in Python, C++ and enjoys making computer science concepts easy to understand for learners.
Being an industry professional, he stresses on preparing learners for the workplace. He is extremely approachable and is always willing to help students when encountering study-related problems and highly encourages learners to adopt a “think out of the box” attitude towards problem-solving.
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