Data Analytics Essentials (DAE) with Python

Advanced Data Science Professional with Python (ADSP)

IFS Academy and CASUGOL, Singapore has signed a MoU for delivering world class Certification Programs to students, faculty and corporates in the field of Computer & Information Technology. IFS Academy is Certified Training Partner (CTP) of Casugol in India. Based in Singapore, CASUGOL is an International Certification Provider offering a wide range of professional Certification Programs, and Executive Workshops in Digital Transformation, and Emerging Technologies, designed for all industries and verticals. IFS Academy will run these courses in India in association with CASUGOL.

Why CASUGOL?

• Customization of Programs for specific industry, organisation, government agencies, statutory boards.

• Benefit from contribution from leading Industry Experts, Academics, and Researchers from across the world.

• Dynamic learning environment that providing participants with professional networking opportunity.

• Flexible programmes designed to cater to the individual needs of participants, whether for professional upskilling, or for general interest.

• Opportunities for employers to develop their workforce and for individuals to enhance their career.

• Online support for participants after the training.

Mode of Training: Instructor-Led Online

Course Information

Training Schedule:

Course Information

Duration: 5 Day / 40 Hours

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

Pre-Requisite

It is preferred that participants successfully completed and pass Data Analytics Essentials (DAE) or Python Programming Essentials (PPE)

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

Course Contents:

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 Validation Train & 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

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