Advanced Data Science Professional Using Python - Online via Zoom

For more information: www.casugol.com/adsp

International Acclaimed Certification. 5-Star Reviews

Suitable for everyone. Learn in an Interactive, Supportive, and Encouraging Environment.

Duration: 4 Day (Onsite) / 24 Hours (Online via Zoom)

Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination

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

Acquire advanced knowledge on how to use Data Science with Python Programming to uncover business insights and trend.

Learn how to leverage on the power of Python Programming to deploy sophisticated statistical algorithms and models to advance Data Science, Artificial Intelligence / Machine Learning capabilities in any industry vertical.

Pre-Requisite

No pre-requisite. Advanced Data Science Professional is suitable for everyone.

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 Programming based on the syllabus covered

Participants are expected to score a minimum of 70% to pass the examination

Module 1 Introduction to Data Science

• 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

Module 2 Deep Dive into Python Programming

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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