Deep Data Science AIML End to End Master Class TM

1:Data Science in a nutshell

An overview of data science, its significance, and its impact on several fields are provided in this section.

Topics Covered:

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.

2. Section Two of Data Science Session

Overview:

The second part of the introduction, this section provides further insights into the instruments and technologies in Data Science.

Topics Covered:

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

Learning Outcomes:

Gain insight into the tools and techniques used in Data Science.

Evaluate instances of data science in the real world.

3. Data science against traditional analysis

Overview:

This section highlights the discrepancies and innovations between data science and conventional data analysis techniques.

Topics Covered:

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

Learning Outcomes:

To show how data science evolved from traditional analysis.

To highlight major differences and benefits associated with data science.

4. Data Scientist Part 1

Overview:

Introduction to the function of a Data Scientist, involving abilities, apparatus and processes.

Topics Covered:

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

Learning Outcomes:

Grasping the position and significance of a Data Scientist.

Identification of predominant capabilities and implements needed in a Data Scientist.

5. Part 2 Data Scientist

Overview:

This section takes a further look into the real life challenges as well as career paths of being a data scientist.

Topics Covered:

Problems that affect Data scientists

Consideration in data science that are ethical

Career pathways and chances for progression for data scientists

Portfolio building and practical experience gaining

Learning Outcomes:

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 impressing folder in Data science.

6. Data Science Process Overview

Overview:

The Data Science process is illustrated in summary form in this course, touching on stages and methods.

Topics Covered:

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

Learning Outcomes:

Recognize various phases encompassed by the Data Science process.

Have a general grip of each stage beginning from acquiring data to its deployment in form of models.

7. Data Science Process Overview Part 2 

Overview:

This is a continuation of the data science process with more complex ideas and tools.

Topics Covered:

Advanced Data Cleaning Techniques.

Feature Engineering and Selection.

Hyperparameter Tuning and Model Optimization.

Introduction to Model Interpretability.

Post-Deployment Monitoring and Maintenance.

Learning Outcomes:

Acquire mastery of advanced techniques in the Data Science process.

Learn how to improve models and ensure they remain effective post deployment.

8. An Opening Statement on Python for Data Science

Overview:

This part of the paper discusses about Python being a core tool in data science.

Topics Covered:

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.

Learning Outcomes:

Ability to organize and run Python scripts meant for Data Science.

Ability to write simple Python code and operate Jupyter Notebooks.

9. Python Libraries for Data Science

Overview:

An attentive look at critical Python libraries that are heavily utilized in Data Science.

Topics Covered:

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 in regards to Machine Learning.

Learning Outcomes:

You will become an expert within the core python libraries which are used for manipulation and representation of the data.

Next step is pre processing before visual representation of the raw data can occur.