Advanced Data Science Professional (ADSP) using Python x ChatGPT
Data Scientist has become one of the most in-demand job position in every industry. The demand for professionals with Data Science expertise are booming and is expected to grow exponentially year-on-year.
Python has become programming language of choice in the areas of Data Science. Recent studies have shown that majority data scientists are utilizing Python daily operations, making it the number one tool for data professionals. As Python becomes the mainstream language of choice, it is essential that Data Scientist acquire the necessary knowledge and skills to stay competitive.
Advanced Data Science Professional (ADSP) using Python x ChatGPT
$528.00 – $880.00
- Duration: 4 Day (Onsite) / 32 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 and skills on advanced data science techniques and methodologies, and develop their proficiency in using Python for complex data analysis and modeling tasks.
Learn how to tackle complex data analysis challenges, build sophisticated predictive models, and extract meaningful insights from diverse datasets using Python and ChatGPT
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
Course Outline
Module 1 Introduction to Data Science
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Data Science is a discipline on data, its application, and how it can be utilized to help companies and professionals to make better decisions. In this module, participants will be introduced to the core components of data science. Participants will also learn about how to craft a data science strategy that is both efficient and reliable.
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Topics Covered
- 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
- ChatGPT and Its Role in Data Science
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Module 2 Deep Dive into Python Programming for Data Science
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Python Programming language is one of the most accessible and flexible programming languages available. The syntax is simplified and can be easily written. With its extensive libraries, it is no wonder that data professionals are using Python as their preferred language. In this module, participants will learn the fundamentals of Python Programming and some of its popular libraries for Data Science.
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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
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Module 3 Importing / Exporting Data with Python
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Reading / writing data one of the first steps to kickstarting any Data Science project. Participants will have the chance to experience first-hand how to read / write data from various sources.
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Topics Covered
- Importing Data into from Various sources
- Database Input (Connecting to database)
- Viewing Data objects - sub setting, methods
- Exporting Data to various formats
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Module 4 Data Cleansing with Python
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The accuracy and quality of any analysis depend on how well the data is being cleaned. Data cleaning can be used to detect and correct errors or anomalies in the data. In this module, participants will learn the art of data cleaning to enhance the quality of data.
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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
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Module 5 Data Visualization with Python
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Data visualization helps people see, interact, and understand data better. The right visualization can help align the understanding of various stakeholders. Participants will have the opportunity to learn how to use Python Programming to perform Data Visualization.
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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)
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Module 6 Statistics Fundamentals
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Statistics are being used by Data Scientists to gather, review, analyze, and draw conclusions from data. Statistics are the core of machine learning algorithms, capturing, and translating data patterns into actionable evidence.
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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
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Module 7 Introduction to Machine Learning using ChatGPT
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Machine learning is a field of study that gives computers the ability to learn without being programmed extensively. It applies algorithms to process the data and get trained for delivering future predictions. In this module, participants will be introduced to the key concepts of Machine Learning and how it can be easily applied using Python Programming.
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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
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Module 8 Predictive Analytics with Python x ChatGPT
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A statistical technique derived from data mining, machine learning, and predictive modeling by using data from past events to predict the future. Participants will experience first-hand how predictive analytics can be deployed using Python Programming.
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Topics Covered
- Introduction to Predictive Modelling
- Types of Business Problems
- Mapping of Techniques
- Using ChatGPT to Deploy Machine Learning Model
- Linear Regression
- Logistic Regression
- Segmentation - Cluster Analysis (K-Means / DBSCAN)
- Decision Trees (CHAID/CART/CD 5.0)
- Time Series Forecasting
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Module 9 Understanding A/B Testing Concepts
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A/B testing is a framework that lets you set up your experiment to compare the performance of two versions of the same thing. It helps teams validate key questions, establishes causality, and helps data-driven teams looking to optimize their products.
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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
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Advanced Data Science Professional (ADSP) involves rigorous usage of real-time case studies, hands-on exercises and group discussions
What You Can Expect
Technology-Based Training |
Open-Source Tools |
80% Hands-On Exercises / 20% Theory |
Designed by Industry Leaders and Subject-Matter Experts |
Progressive Learning |
Highly interactive, supportive and encouraging |
Be recognized as a CASUGOL Certified Professional |
What Learners Say
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