Advanced Data Science Professional with Python (ADSP)

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Online Session: 9:30am to 5:30pm (SGT / UTC +8) 5 Lessons Click on preferred date to register

Course Information

  • Duration: 5 Day / 40 Hours
  • Who Should Attend: Professionals or Anyone interested in pursuing a career as a data scientist and use data to understand the world, uncover insights, and make better decisions

Course Objective

Advanced Data Science Professional with Python (ADSP) is designed for participants interested in pursuing a career as a data scientist and acquire knowledge on using Python to uncover insights and make better decision.

Pre-Requisite

It is preferred that participants attend and successfully completed Data Analytics Essentials (DAE)

Examination

Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Data Science and Python based on the syllabus covered

Certification

Participants will receive a Certificate of Competency upon successfully completing and fulfilling all course requirements course
Module 1 Introduction to Data Science
Topics Covered
  • What is Data Science
  • Data Science Vs. Analytics vs. Data warehousing
  • Online Analytical Processing (OLAP)
  • MIS Reporting
  • Data Science and its Industry Relevance
  • Problems and Objectives in Different Industries
  • How to Harness the power of Data Sceince?
  • Data Analytics vs Data Science

Module 2 Deep Dive into Python Programming
Topics Covered
  • Python Editors & IDE
  • Custom Environment Settings
  • Basic Rules in Python
  • Most Common Packages / Libraries in Python (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
  • Tuples, Lists, Dictionaries)
  • List and Dictionary Comprehensions
  • Variable & Value Labels –  Date & Time Values
  • Basic Operations – Mathematical – string – date
  • Reading and writing data
  • Simple plotting/Control flow/Debugging/Code profiling

Module 3 Importing / Exporting Data with Python
Topics Covered
  • Importing Data into from Various sources
  • Database Input (Connecting to database)
  • Viewing Data objects – sub setting, methods
  • Exporting Data to various formats

Module 4 Data Cleansing with Python
Topics Covered
  • Cleaning of Data with Python
  • Steps to Data Manipulation
  • Python Tools for Data manipulation
  • User Defined Functions in Python
  • Stripping out extraneous information
  • Normalization of Data and Data Formatting
  • Important Python Packages e.g.Pandas, Numpy etc)

Module 5 Data Visualization with Python
Topics Covered
  • Exploratory Data Analysis
  • Descriptive Statistics, Frequency Tables and Summarization
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs
  • Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Pandas and scipy.stats etc)

Module 6 Statistics Fundamentals
Topics Covered
  • Basic Statistics – Measures of Central Tendencies and Variance
  • Building blocks (Probability Distributions, Normal distribution, Central Limit Theorem)
  • Inferential Statistics (Sampling, Concept of Hypothesis Testing)
  • Statistical Methods: Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi-square
  • Statistical Methods: ANOVA
  • Statistical Methods: Correlation and Chi-square

Module 7 Introduction to Machine Learning
Topics Covered
  • Statistical Learning vs Machine Learning
  • Iteration and Evaluation
  • Supervised Learning vs Unsupervised Learning
  • Predictive Modelling – Data Pre-processing, Sampling, Model Building, Validation
  • Concept of Overfitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
  • Cross ValidationTrain & Test, Bootstrapping, K-Fold validation etc

Module 8 Understanding Predictive Analytics
Topics Covered
  • Introduction to Predictive Modelling
  • Types of Business Problems
  • Mapping of Techniques
  • Linear Regression
  • Logistic Regression
  • Segmentation – Cluster Analysis (K-Means / DBSCAN)
  • Decision Trees (CHAID/CART/CD 5.0)
  • Time Series Forecasting

Module 9 Understanding A/B Testing Concepts
Topics Covered
  • Introduction to A/B Testing
  • Measuring Conversion for A/B Testing/li>
  • T-Test and P-Value
  • Measuring T-Statistics and P-Values using Python
  • A/B Test Gotchas
  • Novelty Effects, Seasonal Effects, and Selection of Bias
  • Data Pollution

Advanced Data Science Professional (ADSP) involves rigorous usage of real-time case studies, hands-on exercises and group discussions