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Real time case study comprehensive End-to-End Master Class
Updated on Sep, 2026
IT and Software , Other IT and Software, Python
Duration - 10.5 hours
Training 5 or more people ?
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An overview of data science, its significance, and its impact on several fields is provided in this section.
Data science: what is it?
The Importance of Information in Modern Society
The Wide Range of Applications for Data Science
Data scientists, data engineers, and data analysts are important players in the field of data science.
Learning Objectives:
Recognize the fundamentals of data science and its applications.
Understanding Data Science’s Key Roles and Applications.
The second part of the introduction provides further insights into the instruments and technologies in Data Science.
Outlook on the Instruments and Technologies Used in Data Science
Data Wrangling, Exploration and Visualization
Grasping Big Data and Its Hurdles
Case Studies in Data Science from the Real World
Gain insight into the tools and techniques used in Data Science.
Evaluate instances of data science in the real world.
This section highlights the discrepancies and innovations between data science and conventional data analysis techniques.
Reasonable conventional analysis approaches
From conventional to data science progression
Disparities in methodology, tools, and outcomes
Benefits of data science as compared to traditional analysis
To show how data science evolved from traditional analysis.
To highlight major differences and benefits associated with data science.
Introduction to the function of a Data Scientist, involving abilities, apparatus and processes.
What is a Data Scientist?
Significant Abilities for Data Scientists: Computer programming, Statistical packages and Domain expertise
Popular Tools Utilized by Data Scientists
Daily Duties of a Data Scientist
Grasping the position and significance of a Data Scientist.
Identification of predominant capabilities and implementations needed in a Data Scientist.
This section takes a further look at the real-life challenges as well as career paths of being a data scientist.
Problems that affect Data scientists
Consideration of ethical issues in data science that are ethical
Career pathways and chances for progression for data scientists
Portfolio building and practical experience gaining
Get an understanding of the challenges and ethical issues associated with Data science.
Find out about career options available as well as ways of putting together an impressive folder in Data Science.
The Data Science process is illustrated in summary form in this course, touching on stages and methods.
The life cycle of data science
Data acquisition and processing
Exploratory Data Analysis (EDA)
Model development and assessment
Deployment as well as scrutiny of Data Science models
Recognize various phases encompassed by the Data Science process.
Have a general grip of each stage, beginning from acquiring data to its deployment in the form of models.
This is a continuation of the data science process with more complex ideas and tools.
Advanced Data Cleaning Techniques.
Feature Engineering and Selection.
Hyperparameter Tuning and Model Optimization.
Introduction to Model Interpretability.
Post-Deployment Monitoring and Maintenance.
Acquire mastery of advanced techniques in the Data Science process.
Learn how to improve models and ensure they remain effective post-deployment.
This part of the paper discusses about Python being a core tool in data science.
Why Should One Use Python in Data Science?
Configuring The Environment To Use Python For Data Analytics.
Introduction To Jupyter Notebooks.
Basic Python Syntax And Operations.
Overview Of Data Science Libraries In Python.
Ability to organize and run Python scripts meant for Data Science.
Ability to write simple Python code and operate Jupyter Notebooks.
An attentive look at critical Python libraries that are heavily utilized in Data Science.
Summary of NumPy, Pandas, Matplotlib and Seaborn.
Managing with NumPy Arrays.
Data Handling using Pandas.
Visual Representation through Matplotlib and Seaborn.
Some Introduction to SciPy and Scikit-Learn regarding Machine Learning.
You will become an expert within the core Python libraries, which are used for the manipulation and representation of the data.
The next step is pre-processing before visual representation of the raw data can occur.
Anyone can learn this class it is very simple.
Check out the detailed breakdown of what’s inside the course
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