Advanced Data Science Professional with Python (ADSP) - HRDF Claimable

Learn Online  5-day / 40 Hours     International Certification

27 Sep – 1 Oct 2021  I   25 – 29 Oct 2021   9.30am – 5.30pm

Learn Online 

5-day / 40 Hours   

 International Certification

27 Sep – 1 Oct 2021  I   25 – 29 Oct 2021 

9.30am – 5.30pm

Course Objective
  • Acquire advanced knowledge on how to use data science with Python Programming to uncover business insights and trend.

  • Learn how to use algorithms and basic Artificial Intelligence / Machine Learning techniques to make predictions.

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

Programme Details

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

  • What is Data Science
  • Data Science Vs. Analytics
  • What is Data warehouse
  • 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 Science?
  • ELT vs ETL
  • 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)
  • 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 Validation Train & Test, Bootstrapping, K-Fold validation etc
  • 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
  • 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)
  • 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
  • Importing Data into from various sources
  • Database Input (Connecting to database)
  • Viewing Data objects – sub setting, methods
  • Exporting Data to various formats
  • 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
  • Introduction to A/B Testing
  • Measuring Conversion for A/B Testing
  • 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
  • What is Data Science
  • Data Science Vs. Analytics
  • What is Data warehouse
  • 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 Science?
  • ELT vs ETL
  • 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
  • Importing Data into from various sources
  • Database Input (Connecting to database)
  • Viewing Data objects – sub setting, methods
  • Exporting Data to various formats
  • 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)
  • 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)
  • 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
  • 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 Validation Train & Test, Bootstrapping, K-Fold validation etc
  • 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
  • Introduction to A/B Testing
  • Measuring Conversion for A/B Testing
  • 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

HRDF

All our training programs are HRDF approved, under the “SBL-Khas” scheme. Fee will be paid by PSMB to REDtone on behalf of employers. No upfront payment is required from the participants. For more information, please visit www.hrdf.com.my

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