Course Overview

Data Science with Python Course is an intensive program covering wide spectrum of Data Science teaching concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression & classification modeling techniques. In this course Python - a powerful open source tool is used that prepares you well for the data science job market. Understand the overall analytics landscape, exploratory data analysis, statistical inferences, statistical modeling for regression and classification problems and machine learning.

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Course Key Features

High Lights

Why Become Data Scientist?

 

SQL has become a very important tool for the data scientists since it is helps in fetching, updating, inserting, manipulating and modifying data from data sources like RDBMS or datawarehouses. Without having knowledge in SQL a data scientist has to rely on others to get the data for them. Python is the most common coding language typically required in data science roles. Because of its versatility, a data scientist  can use Python for almost all the steps involved in data science processes. It can take various formats of data and easily import SQL tables into the Python code. Python comes with various Statistical, Machine Learning & Deep Learning libraries that can help a data scientist perform almost all tasks he/she needs.

Why most prefer SCALA for their training neeeds?

 

Upcoming Schedules

August 2021
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Curriculum

Intro to Data Science (1 Hour )

Topics:

  • What is Data Science?
  • Analytics Landscape
  • Life Cycle of a Data Science Projects
  • Data Science Tools & Technologies

Learning Objectives:

Get an idea of what is data science. Why data science is "Rosy" or "Handy" or "Fascinating"

Get acquainted with various analysis and visualization tools used in data science

Delivery Type:
Theory

Hands-on workshop
No hands-on

Home Assignment
No

Mastering Python (11 Hours)

Topics:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data
  • Case Study

Learning Objectives:

  • Learn how to install Python distribution - Anaconda Learn basic data types, strings & regular expressions
  • Data structures that are used in Python
  • Learn all about loops and control statements in Python
  • Write user-defined functions in Python. Learn about Lambda function.
  • Learn object oriented way of writing classes & objects
  • Learn how to import datasets into Python. Also learn how to write output into files from Python
  • Manipulate & analyze data using Pandas library. Learn generating insights from your data
  • Use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization
  • Hands-on session on a real-life case study

Skills
Python

Subskills

  • Basics
  • Container Objects
  • Python Flow Control
  • User Defined Functions
  • File Handling
  • Data Manipulation
  • Visualization

Core Competencies

  • Variables, Data Types
  • List, Tuple, Set, Dictionary
  • Looping Constructs, Conditional Statements
  • Creating functions, Calling UDF, Function Arguments, Classes and Objects
  • Reading, Writing to a File
  • Dataframe Operations, EDA
  • Univariate, Bivariate Plots, EDA

Delivery Type:

Theory + Workshop

Hands-on workshop

  • Know how to install python distribution like anaconda and other libraries
  • Write python code for definining your own functions,and also learn to write object oriented way of writing classes and objects
  • Write python code to import dataset into python notebook
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset

Home Assignment
Yes

Probability & Statistics (3 Hours)

Topics:

  • Measures of Central Tendency
  • Measures of Dispersion
  • Descriptive Statistics
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing

Learning Objectives:

  • Visit basics like mean (expected value), median and mode
  • Distribution of data in terms of variance, standard deviation and interquartile range
  • Basic summaries about the data and the measures. Together with simple graphics analysis
  • Basics of probability with daily life examples
  • Marginal probability and its importance with respective to datascience
  • Learn baye's theorem and conditional probability
  • Learn alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value,

Skills
Statistics, Probability

Subskills
Basics/Intermediate

Core Competencies

  • Mean, Medain, Mode

  • variance, standard deviation

  • Measure of Central Tendency and Dispersion

  • Events, Trials, Likelihood

  • Probability Interpretations, Conditional Probability

  • Various Probability Functions and their Constructions

  • Formulating and Testing Hypothesis

Delivery Type:
Theory + Workshop

Hands-on workshop
Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Home Assignment
Yes

Advanced Statistics & Predictive Modeling - I (8 Hours)

Topics:

  • ANOVA
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA

Learning Objectives:

  • Analysis of Variance and its practical use
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable. It covers strong concepts, model building, evaluating model parameters, measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization
  • Real Life Case Study with Linear Regression
  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. Covers techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion
  • Real-Life case study with PCA & FA

Skills
Statistics, Data Science, Python

Subskills

  • Basics
  • Maths behind Linear Regression, Statsmodel library, Case Study
  • Linear Algebra, Case Study

Core Competencies

  • Analysis of Variance

  • Building and Evaluating Linear Regression Model using OLS with Python statsmodel

  • Vectors, Matrices, Eigenvalues, Eigenvectors

     

Delivery Type:
Theory + Workshop

Hands-on workshop

  • PROJECT 1
    • TITLE - Predict House Pricing
      DESCRIPTION - With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling

Home Assignment
Yes

Advanced Statistics & Predictive Modeling - II (10 Hours)

Topics:

  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbor Algorithm
  • Case Study: K-Nearest Neighbor Algorithm
  • Decision Tree
  • Case Study: Decision Tree

Learning Objectives:

  • Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value
  • Real Life Case Study with Binomial Logistic Regression
  • KNN Algorithm for Classification Problem. Covers techniques that are used to find the optimum value for K
  • Real Life Case Study with KNN
  • Decision Trees - for regression & classification problem. Covers both Classification & regression problem. Candidates get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID
  • Real Life Case Study with Decision Tree

Skills
Data Science, Python

Subskills

  • Maths behind Logistic Regression. sklearn library, Case Study
  • KNN Algorithm, Case Study
  • Building Decision for Regression and Classification problems with sklearn library, Case Study

Core Competencies

  • Building and Evaluating Logistic Regression Model with Python sklearn
  • Distance Metrics, Elbow Curve
  • ID3, CHART, CHAID, Entropy, Information gain, gini index

Delivery Type:
Theory + Workshop

Hands-on workshop

  • PROJECT 2
    • TITLE - Predict credit card defaulter using Logistic Regression
    • DESCRIPTION - With various customer attributes describing customer charactarestics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues
  • PROJECT 3
    • TITLE - Predict chronic kidney disease using KNN
    • DESCRIPTION - Predict if a patient is likely to get any chronic kidney disease depending on the health metrics
  • PROJECT 4
    • TITLE - Predict quality of Wine using Decision Tree
    • DESCRIPTION - Wine comes in various style. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees)

Home Assignment
Yes

Time Series Forecasting (6 Hours)

Topics:

  • Understand Time Series Data
  • Visualizing TIme Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • ARIMA
  • Case Study: Time Series Modeling on Stock Price

Learning Objectives:

  • Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data
  • Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data
  • Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data
  • Real Life Case Study with ARIMA

Skills
Data Science, Python

Subskills

  • Time Component in Data
  • Systematic and Non-Systematic Components
  • Smooting Methods
  • Time Series Models
  • Case Study in Python

Core Competencies

  • Features of Time Series Data
  • Level, Trend, Seasonality, Noise
  • Time Constant, Types of Smooting
  • Basic Exponential Smoothing, Double Exponential Smoothing, Triple Exponential Smoothing
  • Building Time Series Forecasting Model
  • AR, MA, ARMA, ARIMA

Delivery Type:
Theory + Workshop

Hands-on workshop

  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data
  • Write python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smooting constants.
  • Write python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model

Home Assignment
Yes

Capstone Project (1 Hour)

Topics:

Industry relevant capstone project under experiened industry-expert mentor

Learning Objectives:

An industry mentor guided group project to handle a real-life project. The same way you would execute a data science project in any business problem

Delivery Type:
Workshop

Hands-on workshop
Project to be selected by candidates.

Home Assignment
Yes

Elementary programming knowledge

  1. Data Science Tools & Technologies - Get acquainted with various analysis and visualization tools such as matplotlib and seaborn
  2. Statistics for Data Science - Understand the behavior of data as you build significant models and gain strong concepts on Statistics Fundamentals
  3. Python for Data Science - Learn about the various libraries offered by Python to manipulate data. Use of various Python libraries like Numpy, Pandas, Scikit-Learn, Statsmodel
  4. Exploratory Data Analysis - Use Python libraries and work on data manipulation, data preparation and data explorations
  5. Data Visualization using Python - Use of Python graphics libraries like Matplotlib, Seaborn etc.
  6. Advanced Statistics & Predictive Modeling
    • Learn about Analysis of Variance and its practical uses
    • Learn to apply Linear Regression with OLS Estimate to predict a continuous variable
    • Learn to apply Binomial Logistic Regression for Binomial Classification Problems

This course is for you if:

  • You are interested in the field of data science and want to learn essential data science skills
  • You are new to Python or are self-taught and you are looking for a more robust, structured learning program
  • You're a Data Analyst, Economist, or Researcher who works with large datasets and wants to make analysis easier and more effective with Python
  • You're a Software or Data Engineer interested in learning the fundamentals of quantitative analysis

Covers Exploratory Data Analysis, Linear Regression, Logistic Regression, Decision Tree, Time Series Forecasting, Recommender Engines, Text Mining, ANN, SVM, K means Clustering, Ensemble Machine Learning Techniques

PROJECT 1

TITLE - Predict House Price using Linear Regression
DESCRIPTION - With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

PROJECT 2

TITLE - Predict credit card defaulter using Logistic Regression
DESCRIPTION - With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.

PROJECT 3

TITLE - Predict chronic kidney disease using KNN
DESCRIPTION - Predict if a patient is likely to get any chronic kidney disease depending on the health metrics

PROJECT 4

TITLE - Predict quality of Wine using Decision Tree
DESCRIPTION - Wine comes in various style. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees)

SCALA Trainers

Benny Gyan

JESS LEE YAN KEOW (Jezz)

LIONEL SEAH

PATRICK TAN

 

Call Us @ (65)64172475


OR

 

Contact Us For An Appointment

Tools and Frameworks used

Python, MS Excel

 

Corporates We Work With

Join over 200 companies that has enrolled in SCALA's PCP or Professional Conversion Programmes and claim up to 90% support for the course fee and 18 months of salary support for qualified new hires.

 

Register with us today to enjoy the following

About SCALA

Industry Curated Curriculum

Our curriculum is created with the combined effort of our Board of Advisors and industry veterans. Focusing on providing both immediate and future knowledge, the curriculum equips SCALA’s trainees with in-depth logistical and supply chain knowledge as well as keeping them up to date on the latest development in the technology space. Our curriculum is also accredited by WorkForce Singapore and is recognized country-wide for its efficacy.

Strong Leadership

Founded by Dr Robert Yap in 2016, SCALA is an industry-level academy that helps organizations unleash the hidden value in their supply chain. Lead by SCALA’s Board of Advisors, which comprise of distinguish leaders from industry and academia, SCALA’s vision is to become the standard for practical, hands-on training of logistics and supply chain industry in Singapore and the region.

Mentorship/Coaching for Professional Growth

Mentoring is a key element of any successful talent development strategy to help enterprises build high-performing team. Tailored individually to every SCALA's Professional Conversion Programme (PCP) participants, mentors facilitate our participants, who are mid career switchers, towards a deeper understanding of their strengths, talents, personalities and values. Our mentors, having a personal yet professional relationship with their mentees, provide a safe space to learn and grow. As our PCP participants become more purposeful in charting their personal growths, they will be better positioned for success in their new roles.

Network of over 400 Corporate Members

SCALA’s Corporate Network which organises networking opportunities and programmes that help companies embrace innovation to uncover hidden value. By bringing together diverse experiences and businesses, the Corporate Network aims to raise the water level within and outside the industry, to create a profession out of supply chain and logistics!

FAQs

What practical skill sets can I expect to have upon completion of the course?
  • Get advanced knowledge of data science and how to use them in real life business
  • Understand the statistics and probability of Data science
  • Get an understanding of data collection, data mining and machine learning
  • Learn tools like Python
What can I expect to accomplish by the end of this course?

By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as data scientists

Does this class have any restrictions?

There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.

What should my personal setup look like?

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

Testimonials

See what people are saying

"

The salary support has greatly alleviated our manpower costs since we are a non-profit Social Enterprise startup trying to build a critical mass in the wholesale and retail sector, using e-commerce as a strategic tool. The lower cost was critical for our survival in the midst of a tough economy undergoing restructuring.

Kim Wong

Administration Manager, Actsmarket Limited

"

The Supply Chain PCP has been really helpful for us to quickly upskill new hires from a different sector, and equip them with fundamental skills and knowledge for them to excel in the logistics industry. Our new hires have really appreciated the training and guidance given during the programme. The salary support has also been very helpful in incentivising commitment to this programme and helping us to find alternative means to cover the impact on manpower, and in the long run, as employers and employees, are all better off for it.

Timothy Ng

Head, engagement, learning and development, Ninjavan

"

The mentors that are assigned to them are also people with many years of experience and who selflessly share their knowledge. The projects enable them to think from different perspectives and are beneficial to their employers.

Gabriel Lim

Chief Operating Office, Shalom Movers

"

The salary support has helped to accelerate our expansion plans. The invaluable training materials are very informative, and allow my staff to think about various possible scenarios in the workplace.

Alice Wee

Executive Director, Wine Clique Pte. Ltd.

"

Our employees applied the concepts from the classroom training to transform and digitize our supply chain processes to great effect. As a company of over 26 years in oil and gas equipment trading, it is very timely that we get this opportunity to understand and implement industry best practices in today's digital age.

Wilson Chong

Manager, Wah Kee Marine Supplies Pte. Ltd.